# Data-driven Approaches for Enhancing Resilience in Large-scale Transport Networks

Angelo Furno

► To cite this version:

Angelo Furno. Data-driven Approaches for Enhancing Resilience in Large-scale Transport Networks: A journey through data analysis, traffic modelling, complex networks and flexible software architectures for resilient and sustainable cities. Computer Science [cs]. ENTPE, University Claude Bernard Lyon 1 - HDR Thesis, 2023. tel-04462782

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Distributed under a Creative Commons Attribution 4.0 International License
A thesis submitted in fulfilment of the requirements for the degree of
# HABILITATION À DIRIGER DES RECHERCHES

Speciality: INFORMATIQUE

by

ANGELO FURNO

# Data-driven Approaches for Enhancing
Resilience in Large-scale Transport Networks

A journey through data analysis, traffic modelling, complex networks and flexible software architectures for resilient and sustainable cities.

Presented and publicly defended on **13 November 2023**
in front of the committee composed of:

<table><tr><td>EDWARD CHUNG</td><td>Reviewer</td><td>Professor, The Hong Kong Polytechnic University, Hong Kong</td></tr><tr><td>HERVÉ RIVANO</td><td>Reviewer</td><td>Professor, HDR, CITI lab, INRIA, INSA de Lyon, France</td></tr><tr><td>FRANCESCO VITI</td><td>Reviewer</td><td>Associate Professor, MobiLab, University of Luxembourg, Luxembourg</td></tr></table>

<table><tr><td>SYBIL DERRIBLE</td><td>Examiner</td><td>Professor, CSUN lab, University of Illinois Chicago, US</td></tr><tr><td>LATIFA OUKHELLOU</td><td>Examiner</td><td>Research Director, GRETTIA lab, University Gustave Eiffel, France</td></tr><tr><td>DAMIEN TROMEUR-DERVOUT</td><td>Examiner</td><td>Professor, Institut Camille Jordan, Université Lyon 1, CNRS, France</td></tr></table>
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Acknowledgements

I like to imagine myself scrolling the pages of this manuscript in a few years from now, thinking of all the situations and emotions I was experiencing while trying to figure out how to weave the threads of my research into a coherent and meaningful whole.

Without a doubt, my wife and my daughter have pervaded every page of this document. Echoes of bizarre chats, kids' songs, and loud summer games will easily pop up in my mind. I feel obliged to thank them first and foremost. You have shouldered the burden of this HDR with me, tolerating my erratic moods and my mental absence while writing pages of this manuscript. Without your unwavering support, I would never have been able to complete any of these chapters. Thank you, my *beloved* Valeria and Laetitia!

Special thanks also go to my family, who have journeyed with me through my career adventures and have supported me throughout these long years despite the distance. Thank you, Carmen, Zia, Francesco, Ni & Gi, Alfio, Imma and Marco. You probably don't know, but our many remote calls provided warm comfort, helped me keep pace, and led to the completion of this work.

This HDR encompasses a constellation of work done with young and passionate PhD and postdoctoral fellows whom I have had the fortune of crossing paths with. I appreciate the excellent work you all have done. Each of you will be thanked and mentioned in this manuscript. So, please have patience once again and read the entire manuscript to find your name!

Special words of gratitude are reserved for Nour-Eddin, for whom the term mentor falls short. Your positive and generous guidance has significantly contributed to the writing of this HDR. It is a pleasure working with you!

I have also been fortunate to meet exceptional colleagues along the way: Eugenio, Marco, Razvan, and Ludovic. You have inspired me, opened my mind, and helped me achieve significant objectives in my professional life. You've also been great friends to grab a beer or two with!

A special thanks to Anne-Christine for reading and revising this document, and to all the members of the LICIT-ECO7 family for their cheerful presence in the lab.

Many thanks to the reviewers of this HDR and all the jury members for their dedication in reading this document. I hope I have managed to instil some genuine curiosity about the research I've conducted so far, and that the reading will not be overly burdensome.

A final thought goes to Salima and, more generally, to those who are no longer with me. I met you, Salima, when I first came to Lyon as a young PhD student for the SASO conference so many years ago. Who could have thought that you would support my HDR candidacy with such wonderful words and enthusiasm? You should have been present on the day of my defence, but life is sometimes so unfair. I am sure my thanks will reach you anyway, and you will share them with my mother as well.
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Abstract

This Habilitation à Diriger des Recherches (HDR) thesis presents a comprehensive summary of my research activities aimed at enhancing the resilience of large-scale transport networks by joining a data-driven approach with complex network theory.

Ensuring the resilience of critical urban infrastructures, particularly the multi-modal urban transportation network, is crucial for the economic and social development of modern cities. It constitutes a significant research challenge that involves multiple disciplines, ranging from information and data science to economics and urban planning. The daily transportation of people and goods to and from urban areas relies heavily on a robust multi-modal transport network, capable of operating efficiently during normal and high-load situations, as well as adapting and recovering swiftly in the presence of recurrent and sudden disruptions. However, major cities encounter vital challenges in achieving these objectives due to urbanisation growth, extreme weather events, climate change impacts, health crises, and high pollution resulting from greenhouse gas emissions. Therefore, novel solutions are required to enhance the capability of city stakeholders to understand the actual functioning of the urban infrastructures, identify vulnerabilities and anticipate disruptions, as well as promptly implement adaptation or damage mitigation strategies following unexpected events.

My research work towards these goals is founded on three methodological pillars: (i) processing large and diverse datasets to gain insights into human mobility and presence practices, (ii) leveraging complex networks theory and methods to support real-time network monitoring, and (iii) developing real-time big data platforms and algorithms for resilience-oriented decision-making. The manuscript is organised into six chapters, as follows.

Chapter 1 lays the groundwork by introducing the core concepts of the manuscript and identifying the research gaps that motivated the research activities reported in subsequent chapters.

Chapter 2 delves into the potential of passively collected mobile phone data to support the understanding and management of critical infrastructures. The exploration of this data has been addressed according to two perspectives: firstly, using analytical tools to understand cellular traffic demand through the lens of call detail records, towards the efficient management and deployment of network resources; secondly, harnessing mobile phone data, and particularly network signalling data, to enhance the understanding of travel demand by accurately estimating Origin-Destination matrices.

Chapter 3 investigates further the potential of network signalling data for transportation studies in urban settings, by employing unsupervised machine learning techniques to process such data. First, it presents a framework developed to support the estimation of time-varying mean spatial speeds of multiple urban zones of a given city. Subsequently, it delves into the research efforts focused on reconstructing human mobility trajectories with unprecedented spatio-temporal accuracy, comparable to GPS data.

Chapter 4 delves into the assessment and management of vulnerabilities within complex
transport networks. This chapter describes the metrics and approaches identified to detect
the most critical components of large transport networks. It also explores the design of
resilient and sustainable on-demand mobility services, such as park-and-ride systems, capa-
ble of augmenting existing transportation networks with more resilient alternatives against
disruptions and unexpected events.

Chapter 5 introduces the innovative real-time big data platform, PROMENADE, pro-
posed to support the real-time monitoring and resilient management of large-scale urban
infrastructures. It also presents the platform prototype tailored to road traffic networks,
encompassing solutions for large-scale real-time traffic monitoring and agent-based control
to reduce traffic congestion and vulnerability.

Finally, Chapter 6 concludes the HDR thesis by summarising the primary contributions
and outlining short-term and long-term research perspectives that will shape future activities
in the years to come.
# Contents

<table><tr><td>List of Abbreviations</td><td>vii</td></tr><tr><td>List of Figures</td><td>ix</td></tr><tr><td>List of Tables</td><td>xiii</td></tr><tr><td>Introduction</td><td>1</td></tr><tr><td>Early Research Topics</td><td>1</td></tr><tr><td>Evolution of the Research Context</td><td>2</td></tr><tr><td>Data-driven Urban Transport Resilience</td><td>3</td></tr><tr><td>Structure of the Document</td><td>4</td></tr><tr><td>1 Basic Notions</td><td>7</td></tr><tr><td>1.1 Massive Data Sources for Mobility Studies</td><td>7</td></tr><tr><td>1.1.1 Main Sources of Trajectory Data</td><td>9</td></tr><tr><td>1.1.2 Analysis of a Massive Real-world NSD Dataset</td><td>11</td></tr><tr><td>1.2 Resilience Modelling</td><td>14</td></tr><tr><td>1.2.1 Main Approaches to Assessing Resilience: a High-level Classification</td><td>16</td></tr><tr><td>1.2.2 Vulnerability Analysis of Transport Networks</td><td>19</td></tr><tr><td>2 Mobile Traffic Profiling and Travel Demand Estimation</td><td>25</td></tr><tr><td>2.1 Mobile Traffic Demand Profiling with Call Detail Records</td><td>26</td></tr><tr><td>2.1.1 Time Profiling of Mobile Traffic Demand</td><td>26</td></tr><tr><td>2.1.2 Spatial Profiling of Mobile Traffic Demand</td><td>29</td></tr><tr><td>2.1.3 Spatio-temporal Profiling of Mobile Traffic Demand</td><td>33</td></tr><tr><td>2.2 Origin-Destination Flows at Regional Scale via Network Signalling Data</td><td>35</td></tr><tr><td>2.2.1 Static OD Matrix Estimation</td><td>35</td></tr><tr><td>2.2.2 Dynamic OD Matrix Estimation</td><td>39</td></tr><tr><td>3 Zonal Traffic Speed and Urban Trajectory Reconstruction</td><td>45</td></tr><tr><td>3.1 Estimation of Mean Spatial Speeds in Urban Areas</td><td>47</td></tr><tr><td>3.2 Trajectory Inference at Scale with Network Signalling Data</td><td>55</td></tr><tr><td>3.2.1 Trajectory identification</td><td>58</td></tr><tr><td>3.2.2 Trajectory Augmentation</td><td>60</td></tr><tr><td>3.2.3 Performance Evaluation</td><td>61</td></tr><tr><td>3.2.4 Large-Scale Applications of TRANSIT</td><td>62</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>3.3</td>
      <td>Multi-modal Path Reconstruction via Hidden Markov Model</td>
      <td>68</td>
    </tr>
    <tr>
      <td>3.3.1</td>
      <td>Empirical Evaluation in the Lyon Case Study</td>
      <td>71</td>
    </tr>
    <tr>
      <td>3.4</td>
      <td>Towards Privacy-aware Mobility Inference</td>
      <td>74</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Assessing and Managing Vulnerabilities in Transportation Networks</td>
      <td>81</td>
    </tr>
    <tr>
      <td>4.1</td>
      <td>Vulnerability Analysis: a Comparative Study</td>
      <td>83</td>
    </tr>
    <tr>
      <td>4.1.1</td>
      <td>Graph-theory metrics</td>
      <td>83</td>
    </tr>
    <tr>
      <td>4.1.2</td>
      <td>Demand-sensitive Vulnerability Metric</td>
      <td>84</td>
    </tr>
    <tr>
      <td>4.1.3</td>
      <td>Stress Testing Methodology</td>
      <td>85</td>
    </tr>
    <tr>
      <td>4.1.4</td>
      <td>Evaluation</td>
      <td>86</td>
    </tr>
    <tr>
      <td>4.2</td>
      <td>Graph Theory and Big Data Processing towards Online Transport Monitoring</td>
      <td>91</td>
    </tr>
    <tr>
      <td>4.2.1</td>
      <td>Weighted Degree Centrality Metrics for Transport Network Resilience</td>
      <td>92</td>
    </tr>
    <tr>
      <td>4.2.2</td>
      <td>Correlation of Betweenness Centrality and Traffic Metrics</td>
      <td>97</td>
    </tr>
    <tr>
      <td>4.3</td>
      <td>Vulnerability-aware Multi-modal Network Design</td>
      <td>100</td>
    </tr>
    <tr>
      <td>4.3.1</td>
      <td>Park-and-Ride for Resilient On-Demand Urban Mobility</td>
      <td>100</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Real-time and Large-scale Solutions for Resilient Urban Networks</td>
      <td>109</td>
    </tr>
    <tr>
      <td>5.1</td>
      <td>Modelling Approach and Platform Requirements</td>
      <td>110</td>
    </tr>
    <tr>
      <td>5.1.1</td>
      <td>Complex network modelling of city infrastructures</td>
      <td>111</td>
    </tr>
    <tr>
      <td>5.1.2</td>
      <td>Functional requirements</td>
      <td>113</td>
    </tr>
    <tr>
      <td>5.1.3</td>
      <td>Non-functional requirements</td>
      <td>113</td>
    </tr>
    <tr>
      <td>5.1.4</td>
      <td>Additional features</td>
      <td>114</td>
    </tr>
    <tr>
      <td>5.2</td>
      <td>PROMENADE architecture</td>
      <td>114</td>
    </tr>
    <tr>
      <td>5.2.1</td>
      <td>Live-stream and historical-data ingestion</td>
      <td>115</td>
    </tr>
    <tr>
      <td>5.2.2</td>
      <td>Temporary and permanent storage</td>
      <td>116</td>
    </tr>
    <tr>
      <td>5.2.3</td>
      <td>Stream and batch processing</td>
      <td>116</td>
    </tr>
    <tr>
      <td>5.2.4</td>
      <td>Service offering</td>
      <td>116</td>
    </tr>
    <tr>
      <td>5.3</td>
      <td>Platform implementation and its specialisation in road network monitoring</td>
      <td>117</td>
    </tr>
    <tr>
      <td>5.3.1</td>
      <td>Platform core implementation</td>
      <td>117</td>
    </tr>
    <tr>
      <td>5.3.2</td>
      <td>Specialisation for Road Network Resilience</td>
      <td>121</td>
    </tr>
    <tr>
      <td>5.4</td>
      <td>Fast Cluster-based Computation of Betweenness Centrality in Large Graphs</td>
      <td>125</td>
    </tr>
    <tr>
      <td>5.4.1</td>
      <td>Background</td>
      <td>126</td>
    </tr>
    <tr>
      <td>5.4.2</td>
      <td>Brandes' algorithm</td>
      <td>127</td>
    </tr>
    <tr>
      <td>5.4.3</td>
      <td>Equivalence class</td>
      <td>128</td>
    </tr>
    <tr>
      <td>5.4.4</td>
      <td>Clustering and BC computation</td>
      <td>129</td>
    </tr>
    <tr>
      <td>5.4.5</td>
      <td>Empirical Evaluation with Synthetic and Realistic Networks</td>
      <td>135</td>
    </tr>
    <tr>
      <td>5.5</td>
      <td>Multi-agent-based Large-Scale Traffic Control</td>
      <td>140</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Conclusion and Perspectives</td>
      <td>145</td>
    </tr>
    <tr>
      <td>6.1</td>
      <td>Short-term Perspectives</td>
      <td>146</td>
    </tr>
    <tr>
      <td>6.1.1</td>
      <td>Short-term Multi-modal Traffic Forecasting: looking for the atypical</td>
      <td>147</td>
    </tr>
    <tr>
      <td>6.1.2</td>
      <td>Synthetic Population Generation via Multi-Source Data</td>
      <td>153</td>
    </tr>
    <tr>
      <td>6.2</td>
      <td>Longer-term Perspectives</td>
      <td>156</td>
    </tr>
    <tr>
      <td colspan="2">Bibliography</td>
      <td>159</td>
    </tr>
  </tbody>
</table>
List of Abbreviations

<table><tr><td>P2P</td><td>Peer-to-Peer</td><td>2</td></tr><tr><td>CDR</td><td>Call Details Records</td><td>3</td></tr><tr><td>CI</td><td>Critical Infrastructure</td><td>3</td></tr><tr><td>NSD</td><td>Network Signalling Data</td><td>5</td></tr><tr><td>OD</td><td>Origin-Destination</td><td>5</td></tr><tr><td>UAPD</td><td>User Activity-Dependent Positioning Data</td><td>5</td></tr><tr><td>GPS</td><td>Global Positioning System</td><td>5</td></tr><tr><td>IoT</td><td>Internet of Things</td><td>6</td></tr><tr><td>TAZ</td><td>Transportation Analysis Zone</td><td>8</td></tr><tr><td>FCD</td><td>Floating Car Data</td><td>9</td></tr><tr><td>RADAR</td><td>RAdio Detection and Ranging</td><td>8</td></tr><tr><td>LIDAR</td><td>LLight Detection And Ranging</td><td>8</td></tr><tr><td>BSS</td><td>Bike-Sharing System</td><td>9</td></tr><tr><td>LBSN</td><td>Location-Based Social Networks</td><td>9</td></tr><tr><td>LA</td><td>Location Area</td><td>11</td></tr><tr><td>TA</td><td>Tracking Area</td><td>11</td></tr><tr><td>GDPR</td><td>General Data Protection Regulation</td><td>12</td></tr><tr><td>CDF</td><td>Cumulative Distribution Function</td><td>12</td></tr><tr><td>PTN</td><td>Public Transport Network</td><td>21</td></tr><tr><td>TRANSIT</td><td>TRAjectory inference at scale with mobile Network SIgnaling daTa</td><td>21</td></tr><tr><td>MWS</td><td>Median Week Signature</td><td>30</td></tr><tr><td>PoIs</td><td>Points of Interest</td><td>30</td></tr><tr><td>OSM</td><td>OpenStreetMap</td><td>30</td></tr><tr><td>EFA</td><td>Exploratory Factor Analysis</td><td>34</td></tr><tr><td>EDR</td><td>Enquête Déplacements Régionale (Regional Travel Survey)</td><td>36</td></tr><tr><td>LAU</td><td>Location Area Update</td><td>43</td></tr><tr><td>MFD</td><td>Macroscopic Fundamental Diagram</td><td>48</td></tr><tr><td>MAE</td><td>Mean Absolute Error</td><td>55</td></tr><tr><td>RMSAE</td><td>Root Mean Absolute Error</td><td>55</td></tr><tr><td>MAPE</td><td>Mean Absolute Percentage Error</td><td>55</td></tr><tr><td>RMSAPE</td><td>Root Mean Square Absolute Percentage Error</td><td>55</td></tr><tr><td>HMM</td><td>Hidden Markov Model</td><td>69</td></tr><tr><td>ATG</td><td>Adaptive Tree Generalisation</td><td>77</td></tr><tr><td>BC</td><td>Betweenness Centrality</td><td>83</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>UBC</td>
      <td>Unweighted Betweenness Centrality</td>
      <td>84</td>
    </tr>
    <tr>
      <td>TTBC</td>
      <td>Travel-Time weighted Betweenness Centrality</td>
      <td>84</td>
    </tr>
    <tr>
      <td>STC</td>
      <td>Stress Test Criticality</td>
      <td>86</td>
    </tr>
    <tr>
      <td>FFBC</td>
      <td>Free-Flow weighted Betweenness Centrality</td>
      <td>97</td>
    </tr>
    <tr>
      <td>QDG</td>
      <td>Quai Dr. Gailleton</td>
      <td>99</td>
    </tr>
    <tr>
      <td>QCB</td>
      <td>Quai Claude Bernard</td>
      <td>99</td>
    </tr>
    <tr>
      <td>PR</td>
      <td>Park-and-Ride</td>
      <td>100</td>
    </tr>
    <tr>
      <td>MILP</td>
      <td>Mixed-Integer Linear Programming</td>
      <td>101</td>
    </tr>
    <tr>
      <td>LRA</td>
      <td>Lagrangian Relaxation Algorithm</td>
      <td>101</td>
    </tr>
    <tr>
      <td>LR</td>
      <td>Lagrangian Relaxation</td>
      <td>104</td>
    </tr>
    <tr>
      <td>SLA</td>
      <td>Service Level Agreement</td>
      <td>111</td>
    </tr>
    <tr>
      <td>IMP</td>
      <td>Initialization and Monitoring Plane</td>
      <td>114</td>
    </tr>
    <tr>
      <td>SI</td>
      <td>Stream data Ingestion</td>
      <td>114</td>
    </tr>
    <tr>
      <td>HI</td>
      <td>Historical data Ingestion</td>
      <td>114</td>
    </tr>
    <tr>
      <td>DAQ</td>
      <td>Distributed Asynchronous producer-consumer Queue</td>
      <td>114</td>
    </tr>
    <tr>
      <td>PS</td>
      <td>Permanent Storage</td>
      <td>114</td>
    </tr>
    <tr>
      <td>SP</td>
      <td>Stream Processing</td>
      <td>114</td>
    </tr>
    <tr>
      <td>BP</td>
      <td>Batch Processing</td>
      <td>114</td>
    </tr>
    <tr>
      <td>SVC</td>
      <td>Service offering layer</td>
      <td>114</td>
    </tr>
    <tr>
      <td>SSSP</td>
      <td>Single-Source Shortest-Paths</td>
      <td>128</td>
    </tr>
    <tr>
      <td>BFS</td>
      <td>Breadth-First Search</td>
      <td>128</td>
    </tr>
    <tr>
      <td>AS</td>
      <td>Algorithmic Speedup</td>
      <td>135</td>
    </tr>
    <tr>
      <td>COMFORT</td>
      <td>Cooperative Multi-agent Framework for large-scale Routing-based Traffic control</td>
      <td>140</td>
    </tr>
    <tr>
      <td>MAS</td>
      <td>Multi-Agent System</td>
      <td>141</td>
    </tr>
    <tr>
      <td>EMD</td>
      <td>Enquête Ménage Déplacements</td>
      <td>154</td>
    </tr>
    <tr>
      <td>GNN</td>
      <td>Graph Neural Networks</td>
      <td>151</td>
    </tr>
    <tr>
      <td>GCNN</td>
      <td>Graph Convolutional Neural Networks</td>
      <td>151</td>
    </tr>
    <tr>
      <td>ST-ED-RMGC</td>
      <td>Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network</td>
      <td>151</td>
    </tr>
    <tr>
      <td>RMGC</td>
      <td>Residual Multi-graph Convolutional network</td>
      <td>151</td>
    </tr>
    <tr>
      <td>MSE</td>
      <td>Mean Squared Error</td>
      <td>152</td>
    </tr>
    <tr>
      <td>LNN</td>
      <td>Liquid Neural Networks</td>
      <td>153</td>
    </tr>
    <tr>
      <td>EMD</td>
      <td>Enquête Ménage Déplacements</td>
      <td>154</td>
    </tr>
    <tr>
      <td>MCMC</td>
      <td>Markov Chain Monte Carlo</td>
      <td>154</td>
    </tr>
    <tr>
      <td>RaaS</td>
      <td>Resilience-as-a-Service</td>
      <td>156</td>
    </tr>
    <tr>
      <td>EV</td>
      <td>Electric and electrified Vehicle</td>
      <td>158</td>
    </tr>
  </tbody>
</table>
List of Figures

<table><tr><td>1</td><td>Structure of the document: a visual representation.</td><td>4</td></tr><tr><td>1.1</td><td>Examples of inference of one trajectory of a volunteer from (a) CDR, and (b) NSD, compared to the corresponding GPS trace (in blue) used as ground truth.</td><td>12</td></tr><tr><td>1.2</td><td>CDF of inter-event times recorded in NSD, CDR, and CDR+. The plots refer to (a) median, and (b) average times per user.</td><td>13</td></tr><tr><td>1.3</td><td>Sample weekly trajectories of one voluntary user inferred from (a) CDR, and (b) NSD.</td><td>14</td></tr><tr><td>1.4</td><td>The general framework for resilience engineering proposed by Hollnagel et al., 2006.</td><td>16</td></tr><tr><td>1.5</td><td>Transition among states of a disrupted system as proposed in Henry et al., 2012.</td><td>19</td></tr><tr><td>1.6</td><td>Resilience curve and conditional vulnerability as from Mattsson and Jenelius, 2015.</td><td>20</td></tr><tr><td>2.1</td><td>Mobile traffic profile categories for the CDR of the city of Milan.</td><td>28</td></tr><tr><td>2.2</td><td>Residential urban fabrics. Characteristic signatures (with standard deviation) and maps of related unit areas in representative city scenarios.</td><td>31</td></tr><tr><td>2.3</td><td>Office fabric signatures s<sub>2</sub>, s<sub>5</sub> and s<sub>7</sub> and maps of the related unit areas in Italian and French cities, with OpenStreetMap data.</td><td>32</td></tr><tr><td>2.4</td><td>Transportation fabric signatures s<sub>9</sub>, s<sub>10</sub> and s<sub>36</sub>, and their maps for French and Italian cities, with OpenStreetMap data.</td><td>33</td></tr><tr><td>2.5</td><td>Land use detection. (a)-(d) EFA of the total communication activity (sum of incoming/outgoing calls and SMS) in the Orange-2014 dataset. Loadings of the 1596 Voronoi cells (i.e., EFA variables) on four (out of fourteen) representative classes (i.e., EFA factors). (e)-(h) Thurstone's scores of the 91×24 hours (i.e., EFA samples) on a selection of the 16 classes (i.e., EFA factors). Figure best viewed in colours.</td><td>34</td></tr><tr><td>2.6</td><td>Cell tower locations (a), and (b) EDR sectors in the region of interest (Rhône-Alpes).</td><td>36</td></tr><tr><td>2.7</td><td>The OD matrix reconstruction framework.</td><td>37</td></tr><tr><td>2.8</td><td>Graphical comparison between the NSD-reconstructed OD matrix (a) and the EDR OD matrix (b). (c) Linear regression of the NSD OD matrix with respect to the EDR one.</td><td>38</td></tr><tr><td>2.9</td><td>Reconstruction of time-dependent OD matrices: (a) the extended framework; (b) The approach for imputing trip starting time.</td><td>40</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>4.9</td>
      <td>Network segmentation (a); and the network used for the real instance I<sup>R</sup> (b).</td>
      <td>104</td>
    </tr>
    <tr>
      <td>4.10</td>
      <td>Solutions found for the real instance: green nodes are origins (potential pickups) and red nodes are the destinations (potential drop-offs).</td>
      <td>107</td>
    </tr>
    <tr>
      <td>5.1</td>
      <td>Collecting and leveraging big smart city data as complex dynamic networks.</td>
      <td>112</td>
    </tr>
    <tr>
      <td>5.2</td>
      <td>Detailed architecture of PROMENADE.</td>
      <td>115</td>
    </tr>
    <tr>
      <td>5.3</td>
      <td>Underlying software and hardware technologies of PROMENADE-v2.0.</td>
      <td>117</td>
    </tr>
    <tr>
      <td>5.4</td>
      <td>Detailed architecture of PROMENADE-v2.0. Gray boxes indicate that the corresponding components are not implemented in the current prototype of the platform.</td>
      <td>119</td>
    </tr>
    <tr>
      <td>5.5</td>
      <td>Architecture of the IoT data-driven emulation software.</td>
      <td>123</td>
    </tr>
    <tr>
      <td>5.6</td>
      <td>GUI examples - Map with vulnerability indicator (BC). The hottest spots of the heatmap represent critical nodes with high BC values.</td>
      <td>125</td>
    </tr>
    <tr>
      <td>5.7</td>
      <td>Example of clustering.</td>
      <td>130</td>
    </tr>
    <tr>
      <td>5.8</td>
      <td>Classes of equivalent nodes in the blue cluster C<sub>1</sub></td>
      <td>130</td>
    </tr>
    <tr>
      <td>5.9</td>
      <td>Example of external node found through the HSN.</td>
      <td>132</td>
    </tr>
    <tr>
      <td>5.10</td>
      <td>Global SSSP explorations from pivots</td>
      <td>134</td>
    </tr>
    <tr>
      <td>5.11</td>
      <td>Comparison with Brandes' algorithm. Algorithmic speedup analysis - AS<sup>E/B</sup><sub>p=[1,5,10,15,20,25]</sub></td>
      <td>137</td>
    </tr>
    <tr>
      <td>5.12</td>
      <td>Comparison with Brandes' algorithm. Efficiency analysis - E<sub>p=[1,5,10,15,20,25]</sub></td>
      <td>138</td>
    </tr>
    <tr>
      <td>5.13</td>
      <td>Comparison with the HSN solution for synthetic (a) and real graphs (b).</td>
      <td>139</td>
    </tr>
    <tr>
      <td>5.14</td>
      <td>Performance analysis of the E1C-FastBC algorithm versus Brandes on the real graph Lyon-road-network.</td>
      <td>140</td>
    </tr>
    <tr>
      <td>5.15</td>
      <td>Activity diagram of the interactions between agents in COMFORT.</td>
      <td>141</td>
    </tr>
    <tr>
      <td>5.16</td>
      <td>The northern region network of the conurbation of Lyon (3rd, 6th district of Lyon and Villeurbanne). The network contains 1,883 nodes, 3,383 links and is divided in 17 zones. Each zone is represented by a distinct colour.</td>
      <td>143</td>
    </tr>
    <tr>
      <td>6.1</td>
      <td>Methodology flowchart</td>
      <td>148</td>
    </tr>
    <tr>
      <td>6.2</td>
      <td>The weekly signature of counts and observations for a week in March 2019.</td>
      <td>149</td>
    </tr>
    <tr>
      <td>6.3</td>
      <td>Average deviance of each mode per cluster (three largest identified clusters).</td>
      <td>150</td>
    </tr>
    <tr>
      <td>6.4</td>
      <td>Average deviance of each mode per cluster.</td>
      <td>155</td>
    </tr>
  </tbody>
</table>
<figure><img src="image_4.png" /></figure>
# List of Tables

<table><tbody><tr><td>1.1</td><td>Advantages and disadvantages of novel data sources for human mobility modelling.</td><td>8</td></tr><tr><td>1.2</td><td>Statistics on the large-scale network signalling data.</td><td>12</td></tr><tr><td>1.3</td><td>Methods for resilience assessment, adapted and extended from Serdar et al., 2022.</td><td>18</td></tr><tr><td>3.1</td><td>Average speed errors for each mean inter-event time (without de-biasing).</td><td>55</td></tr><tr><td>3.2</td><td>Daily speed errors with de-biasing in the worst-case scenario (20-minutes IET)</td><td>55</td></tr><tr><td>3.3</td><td>Descriptive statistics of the datasets used for the experiments (#matrices: number of matrices in the whole dataset, where each matrix represents the flows over a time step; #tiles: number of initial tiles over which the matrices are set; density: average graph density of the matrices; avg. #flows: average number of flows among the matrices in the dataset; avg. vol.: average sum of the flows; %anon. flows: fraction of flows that are above k = 10; %anon. vol: fraction of individuals that are in flows above k = 10.)</td><td>77</td></tr><tr><td>3.4</td><td>Performance on samples of the dataset senegal_crop, nyc, civ, and senegal. G̃: mean generalisation error; E: normalized reconstruction loss; S: fraction of volumes suppressed; D: distribution distance (Eq. 3.38). The reported time is the total computing time (in seconds) to run the anonymisation of all matrices of all datasets. Note that laplace_noise adds volumes, as it mostly applies a positive noise on a sparse matrix.</td><td>79</td></tr><tr><td>3.5</td><td>Performance on samples of the datasets senegal_big and senegal_split.</td><td>79</td></tr><tr><td>4.1</td><td>Origin-destination matrices for the simple virtual network with different demand levels (A and B). Values are expressed as vehicles per hour.</td><td>87</td></tr><tr><td>4.2</td><td>Simple network link rankings generated by the different metrics of criticality.</td><td>89</td></tr><tr><td>4.3</td><td>DIRIF network link values generated by the different metrics of criticality</td><td>90</td></tr><tr><td>4.4</td><td>Results on I<sup>R</sup> after 1 hour, with a heuristic time constraint of 20 minutes.</td><td>105</td></tr><tr><td>4.5</td><td>Park-and-ride flow shares with different budgets, using θ = 0.1, t<sup>access</sup> = 2 minutes, and t<sup>egress</sup> = 8 minutes.</td><td>106</td></tr><tr><td>5.1</td><td>Mapping of PROMENADE-v2.0 components to their corresponding K8s objects.</td><td>118</td></tr><tr><td>5.2</td><td>Road segments (links) and intersections (nodes): static data provided to PROMENADE-v2.0.</td><td>122</td></tr><tr><td>5.3</td><td>Road segments (links) and intersections (nodes): dynamic data collected and produced by PROMENADE-v2.0.</td><td>122</td></tr><tr><td>5.4</td><td>Road segments: dynamic raw (non-aggregated) data of vehicles.</td><td>124</td></tr></tbody></table>
<table>
  <tbody>
    <tr>
      <td>5.5</td>
      <td>The notation used with the BC exact computation algorithm.</td>
      <td>127</td>
    </tr>
    <tr>
      <td>5.6</td>
      <td>Normalised distances and normalised number of shortest paths for the blue cluster C<sub>1</sub>. Note that the values of d&#x0302; and σ&#x0302; are computed considering external node 17, as from Fig. 5.9(b).</td>
      <td>131</td>
    </tr>
    <tr>
      <td>5.7</td>
      <td>Topological information of synthetic &amp; real graphs. The names of the graphs are given in the first column, whereas the number of nodes and edges are given in the second and third columns. d<sub>avg</sub> and d<sub>max</sub> are the average and max degree, respectively. cc<sub>avg</sub> is the average clustering coefficient.</td>
      <td>136</td>
    </tr>
  </tbody>
</table>
# Introduction

Over the last ten years, my research has progressively shifted across three major domains: service computing, analytics for cognitive 5G networks, and human mobility modelling and management. Despite this domain shift, the core of my activities has, however, remained focused on one main goal: enabling efficient, scalable, robust, and sustainable utilisation of complex networked systems in the face of dynamic and often unpredictable usage loads and contexts. These last two terms, usage loads and contexts, may assume specific meanings according to the reference domain. From a general point of view, usage load refers to the amount of demand or activity being placed on a system by its users at any given time. Understanding and managing usage load is critical for ensuring that these systems remain stable and performant, even during periods of high or abnormal demand and in the presence of disruptive exogenous or endogenous factors. Context can be defined as the set of external factors and circumstances that affect the behaviour and performance of these systems. Analysing, modelling, and monitoring contextual factors is paramount to improve the scalability, reliability and recovery ability of the considered system, as well as to provide a better user experience.

Building on these foundations, my research journey has addressed so far a rather wide range of interrelated challenges, such as proposing solutions for data-driven understanding and modelling of user demand, performing knowledge inference from large-scale and multi-source datasets, implementing metrics, approaches and platforms for real-time monitoring of networked systems, and devising decision-making tools to anticipate or respond quickly to changes in user demand or external events. The diversity of the research topics has required acquiring expertise in several disciplines, including distributed and big data processing, complex network theory, data science and machine learning, telecommunications and transportation.

In this HDR thesis, I aim to summarise and present my main research contributions, with a particular focus on the activities from the last seven years. During this journey, I have had the opportunity to collaborate with enthusiastic colleagues who have helped me to achieve interesting findings in the aforementioned fields. I hope that this thesis adequately summarises these outcomes and fruitful collaborations.

## Early Research Topics

My research experience starts during the PhD program conducted at the Engineering Department of University of Sannio, Italy, where I decided to address the problem of simplifying the lives of Web users by devising novel approaches for *automatically and efficiently com-posing* existing Web applications (i.e., published as Web/cloud services) to generate novel value-added applications, able to satisfy complex users’ needs effectively. People using the Web for digital tasks often struggle to find services that fully meet their needs directly. Instead, they have to manually and repeatedly search and join multiple services from many providers to achieve more complex tasks. For instance, when planning a long-range trip, it is necessary to identify proper applications in order to solve specific sub-tasks, e.g., calcu-
lating the best itinerary to move from point A to point B; buying tickets for each involved transportation mode (e.g., flight, metro, bus); finding and booking accommodation, etc. The general problem - referred to as discovery by composition - is known to be tedious when performed manually and requires high computational and time resources (i.e., services are to be searched on the entire Web). Thus, my PhD thesis proposed techniques to make discovery by composition automated and scalable. In general, services and users' requirements are sensitive to their environment. The context is thus a crucial dimension, significantly impacting service design, query specification, and service execution. It contains implicit information essential for fully understanding users' needs. The latter are rarely properly described in computer-understandable language.

The main contribution of my PhD thesis can be summarised as follows: an integrated approach and a tool to automatically generate concrete and executable service compositions from existing Web services [1]; semantic support for enhancing context-awareness in service design, discovery and execution [2]; a fully distributed Peer-to-Peer (P2P) and cooperative approach to automatically and efficiently retrieve and join multiple services, based on epidemic diffusion protocols and semantic overlay networks [3]. We point the interested reader to [4] for more details.

## Evolution of the Research Context

During the last year of my PhD thesis, my advisor at the University of Sannio, Eugenio Zimeo, invited me and other members of his research group to investigate the problem of simplifying access to digital services and resources via data-driven content recommendation. To identify targets for the recommendation task, the idea was to leverage massive data on user preferences, either explicitly declared on social media (e.g., Facebook liked pages, LinkedIn groups) or implicitly determined by previous service usages performed by the user themselves or by their social environment (e.g., friends, work connections, etc.). In pursuing such research activities in parallel with my main PhD topics, I was led to develop a new theoretical and technical background in various disciplines encompassing distributed systems and Web service technologies. Data science and unsupervised machine learning (e.g., clustering) were necessary to analyse the data mined via social network APIs and identify communities of interest; information theory (e.g., collaborative filtering, semantic similarity, preference mining) was required to capture the semantics of user preferences and perform item recommendation. Complex networks theory was a natural choice to model the topology of users' preferences and relationships as multi-layer networks, while social network analysis tools (e.g., centrality metrics) were leveraged to spot opinion leaders within the identified interest user communities. Finally, big data processing techniques and technologies (e.g., map-reduce batch programming, stream processing, Spark, NoSQL databases, such as MongoDB<fnref n="1" /> and Neo4j<fnref n="2" />) became quickly indispensable to handle the huge variety, velocity and volumes, as well as the unstructured nature of the data retrieved from social networks (e.g., content of liked pages, user groups descriptions, etc.). Based on such techniques, I contributed to laying the foundation of a social network platform for service recommendation, which was subsequently further developed by the research team at University of Sannio [5].

This experience provided me with novel knowledge and perspectives that proved crucial for my subsequent career advancement and has guided my research journey until the present day. At the end of my doctoral studies, I sought to expand my academic career internationally, delving deeper in the research related to the processing and mining of large-scale datasets, applied machine learning, big data technologies and network analysis, with a focus on incorporating greater intelligence into the urban environment for increased resilience and quality of service. This led me to apply and be selected for a CORDIS postdoctoral research fellowship at the Urbanet team, now Agora of INRIA and the CITI laboratory of INSA

<footnote n="1">¹https://www.mongodb.com</footnote>

<footnote n="2">²https://neo4j.com</footnote>
Lyon. The postdoc was also supported by the research branch of Orange (Orange Labs, now Orange Innovation), the major telecommunication network operator in France. This marked the beginning of a strong and still-ongoing collaboration with the mobile provider, which has supported a relevant part of my research since then.

The primary contribution of the INRIA postdoc involved mining massive records of mobile phone activity, i.e., mobile traffic demand, in the form of Call Details Records (CDR), to derive communication usage patterns for two main purposes: (i) performing data-driven resource allocation in the provider's network, aligned with the vision of 5G cognitive radio networks [6, 7, 8], and (ii) inferring spatio-temporal insights on the socioeconomic characteristics of the urban environment [9, 10], according to the idea that mobile phone data could provide valuable support for the comprehension of urban metabolism. This notion includes, particularly, the capacity to provide dynamic descriptions of the presence, mobility and activities of the urban population and its interactions with the urban infrastructures to support resilient urban planning and management [11, 12]. The most relevant research results related to these aspects are highlighted in Chapter 2.

## Data-driven Urban Transport Resilience

At the end of my postdoctoral experience, I decided to join the Laboratoire d'Ingénierie Circulation Transport (LICIT-ECO7) of the University Gustave Eiffel and École Nationale des Travaux Publics de l'État (ENTPE), University of Lyon, where I am currently a permanent researcher and associate professor since October 2016. This was the opportunity of applying and extending my previous research on mobile traffic demand analysis and delve deeper into the study of data-driven resilient human mobility.

Ensuring the resilience of urban Critical Infrastructures (CIs), and particularly of the multi-modal urban transport system, is vital for the economic and social development of modern cities and represents a major research challenge involving multiple disciplines, from information and data science to economics and urban planning. The daily transport of people and goods to and from urban areas is highly dependent on a robust multi-modal transport network, capable of efficiently operating during normal and high-load situations, as well as of adapting and recovering itself quickly in the presence of recurrent and sudden perturbations. However, major cities face crucial challenges in achieving these objectives. In 2009, for the first time, more than half of the world's population lived in urban areas and by 2030, cities are expected to host 60% of the world's population. The growing concentration of people results in a continuous increase in demand for mobility, as well as frequent disruptions and significant stresses on the urban transport infrastructures. These aspects make user travel demand more uncertain and rapidly varying, degrade the mobility experience of users, deteriorate air quality and increase the risk of malfunctioning and cascading failures, especially in the presence of adverse weather conditions or extreme adverse situations. The recent COVID pandemic has exacerbated the vulnerability of the mobility system, exposing people to a high risk of contamination, deteriorated quality of service, and reduced and highly unequal access to transport.

On the other hand, cities are becoming increasingly *smart* and, therefore, able to collect vast amounts of data in real-time, via connected sensors, people and infrastructure. The recent development of 5G telecommunication networks provides reliable and ultra-fast connectivity, always-on broadband devices, and support for new computing paradigms such as fog and edge computing. In this context, massive, real-time data become available at unprecedented scales and resolution, with the great potential to provide very valuable information about how, where and when citizens move, interact, work and live, making it possible to continuously monitor and optimise the operations and usages of city infrastructures.

Based on such needs and challenges, my research activities of the last seven years at the LICIT-ECO7 have focused on the understanding of mobility practices and network
vulnerabilities, towards enhanced data-driven resilience of the transport system.

To achieve the stated objectives, my research has deliberately drawn upon previous experience, as well as methods and knowledge from multiple disciplines, including mobile phone data processing, edge/fog/cloud computing, complex network theory, machine learning methods, and big data technologies, that has flourished in a cohesive interdisciplinary approach to urban resilience engineering.

In that respect, my research revolved around the three major axes:

1. Design and development of innovative data-driven tools for the fine-grained understanding of human mobility;

2. Definition of innovative approaches, metrics and strategies for vulnerability assessment and monitoring of large-scale urban multi-modal transport networks;

3. Design and development of real-time data-driven decision tools and extensible platforms to support the resilient management of human mobility, and, more in general, of urban CIs.

As will be further detailed in the next chapters, these activities have been the result of significant collaboration with PhD and Postdoctoral students, as well as researchers from other (national or international) academic and industry institutions, that provided their invaluable help to refine and implement my research goals, methods and solutions.

# Structure of the Document

This document is organised in six chapters, as visually detailed in Fig. 1.

<figure><img src="image_5.png" /><figcaption>**Figure 1:** Structure of the document: a visual representation.</figcaption></figure>
Chapter 1 prepares the ground for the rest of the manuscript by introducing the main massive data sources adopted in the reported research and by introducing the basic notions related to resilience modelling, with a special focus on the conceptual frameworks proposed by Hollnagel et al. [13] (i.e., the four cornerstones of resilience) and Bellini et al. [14]. The research activities described in this HDR thesis can be seen as an effort to explore the four cornerstones of resilience in an original way, i.e., by utilising novel sources of massive data and combining methods from machine learning, network analysis, and big data solutions.

With this respect, the activities outlined in Chapters 2 and 3 relate to the *learn* cornerstone, i.e., *knowing what has happened*, which involves understanding via descriptive analytics how CIs of the urban system, such as the mobile phone telecommunication network and the multi-modal transportation system, are utilised by their users, in both recurring and abnormal situations.

Specifically, the first part of Chapter 2 describes the activities on the topic of CDR analytics for cognitive networking and urban fabrics detection, carried out during my postdoctoral studies. It also reports on the preliminary activities performed on Network Signalling Data (NSD) for travel demand estimation in the form of Origin-Destination (OD) matrices at a regional scale. This topic has been the subject of the PhD thesis of Mariem Fekih that I co-supervised.

Chapter 3 pushes forward the results of Chapter 2 by focusing on the reconstruction of human mobility information at a finer spatial resolution, in urban settings. First, the research activities related to mean speed estimation for urban regions are presented. To that purpose, we proposed a generic data representation, called User Activity-Dependent Positioning Data (UAPD), to model different kinds of mobile phone data. This topic was the focus of Manon Seppecher's PhD thesis, which I had the privilege of co-supervising. The second part of the chapter focuses instead on the topic of leveraging NSD to reconstruct Global Positioning System (GPS)-like human mobility trajectories in urban environment. This research was the subject of Loïc Bonnetain's PhD thesis, which I co-directed. This chapter also discusses the approaches developed to satisfy anonymisation constraints with mobile phone traces in order to ensure confidentiality in the collection, processing and sharing of mobility indicators derived from NSD. This subject is one of the topics of another ongoing PhD thesis (Benoit Matet) co-advised with the GRETTIA laboratory of University Gustave Eiffel.

Chapter 4 outlines the research efforts focused on modelling, analysing, and managing the vulnerabilities of multi-modal transport networks. First, we present research activities centred on the cornerstone of resilience known as *anticipating*, i.e., *knowing what to expect*. These activities have been helpful in highlighting the limitations of current approaches for identifying vulnerabilities to disruptions in complex and highly dynamic transportation networks. Building on these insights, the chapter explores the combined use of complex network metrics and mobility data for continuous monitoring. This approach enables quick detection and anticipation of disruptive events in large-scale settings. Our methodology offers a dynamic and reactive approach to the resilient management of transportation networks. It provides advantages over traditional vulnerability assessment methods by better accounting for events that are hard to model and predict. This is in line with the *monitoring*, i.e., *knowing what to look for*, aspect of resilience. Finally, we present an urban planning strategy aimed at integrating multi-modality, specifically focusing on on-demand shuttle services. This strategy aims to reduce the vulnerability of specific urban areas to recurrent disruptions while preserving efficient network operations under typical conditions. This aligns with the *responding*, i.e., *knowing what to do*, aspect of resilience. The research reported in this chapter includes significant contributions from Elise Henry's PhD thesis, which I co-supervised.

Chapter 5 expands upon the findings of Chapters 3 and 4. It introduces the architectural, technological, and algorithmic support developed to support the effective processing of both large historical and real-time stream data. Moreover, it discusses the strategies
implemented to address the two core elements of resilience introduced in the previous chap-
ter: continuous *monitoring, i.e., knowing what to look for*, and *responding, i.e., knowing
what to do*. Specifically, the first part of the chapter focuses on the design and prototyp-
ing of the PROMENADE general-purpose platform, its prototype for road networks and
related applications. The platform is part of the homonymous project, ANR JCJC PROM-
ENADE, which I have led from February 2019. Inspired by the principles of the Internet
of Things (IoT), cloud computing, and micro-services architectures, the platform supports
the continuous development of data-intensive and real-time applications. The second part
of the chapter delves with the applications of the platform prototype. A first one lever-
ages a betweenness-centrality-based approach for quasi-real-time monitoring of very large
networks. This application exploits a new algorithm for the rapid calculation of the between-
ness centrality indicator on large networks, which is also described in this chapter. A second
application focuses on the large-scale control of urban road traffic through a multi-agent
cooperative framework. Both applications were developed in the context of Cecile Daniel's
PhD thesis, which I co-directed in collaboration with the LIRIS laboratory of University
Claude Bernard Lyon 1, and in collaboration with the University of Sannio.

The final Chapter 6 summarises the primary contributions of my research activities thus
far and highlights future research perspectives that I will be pursuing in the short and long
terms.
# Chapter 1

## Basic Notions

This chapter introduces the foundational notions underpinning this manuscript, organised into two primary sections.

In the first section, a succinct overview of the most relevant large-scale data sources used in mobility studies is presented. The focus is solely on the data incorporated into my research, with no intention of exhaustively covering the vast field of data-driven transportation studies. This section also includes a comparative analysis of NSD and CDR data, based on a real-world dataset provided by Orange Innovation. The concepts introduced in this section represent the necessary groundwork for the rest of the manuscript, particularly for Chapters 2 and 3, which focus on the analysis and processing of mobile phone data.

The second section shifts to resilience modelling, introducing specific vocabulary to assist the reader in accurately interpreting the manuscript when discussing resilience and related concepts. Considering the broad nature of resilience, rooted in the fields of critical infrastructures and risk management, this section delves deeper into its nuances as it pertains to transportation. Particularly, this section provides a thorough exploration of related work on vulnerability assessment for multi-modal transport networks, and identifies the most pertinent research gaps that motivated the activities described in Chapters 4 and 5.

### 1.1 Massive Data Sources for Mobility Studies

A key objective of my research at LICIT-ECO7 has been to investigate the potential of large-scale mobility data sources in order to develop automatic approaches to derive insights on human mobility. This exploration has contributed to enriching traditional modelling of travel demand and enhancing the management of transport networks. In that respect, a strong attention has been reserved to data sources preserving trajectory information at an individual level, and, particularly, to mobile phone passive data. Working on such data has come with various challenges, such as overcoming the inherent limitations in terms of accuracy of these data sources, which are not explicitly thought to provide mobility information, as well as identifying proper combinations of techniques and technologies to enable the automatic processing of their huge volumes. In the following, the most relevant sources of massive trajectory data are shortly (and definitely non-exhaustively) described, together with a brief analysis of their main advantages and limitations (see Table 1.1). The interested reader can refer to recent surveys on the topic, such as [15, 16, 17], for a more detailed bibliography on these data sources and their applications in the field of human mobility study and modelling.

For decades, household surveys, completed through phones, the Internet or face-to-face interviews, have been the major source of data for analysing and modelling travel demand at city, regional or even nation-wide scales. Actively compiled by travellers (or by operators
<table><caption>**Table 1.1:** Advantages and disadvantages of novel data sources for human mobility modelling.</caption><thead><tr><th>Data Source</th><th>Advantages</th><th>Disadvantages</th><th>Related Chapters</th></tr></thead><tbody><tr><td>GPS</td><td><ul><li>High spatial accuracy and temporal resolution</li><li>Speed data available</li></ul></td><td><ul><li>Privacy concerns</li><li>Small sample size</li><li>Unavailability of data (e.g., tunnels)</li></ul></td><td><ul><li>Chapter 3</li><li>Chapter 4</li><li>Chapter 5</li><li>Chapter 6</li></ul></td></tr><tr><td>Ticketing and smart-card</td><td><ul><li>Large sample size</li><li>Low collection cost</li></ul></td><td><ul><li>Limited to transit users</li><li>Incomplete trip records (e.g., missing tap-out)</li></ul></td><td><ul><li>Chapter 3</li><li>Chapter 6</li></ul></td></tr><tr><td>BSS Data</td><td><ul><li>Detailed urban mobility patterns</li><li>Reflects infrastructure utilisation and behavioural shifts</li><li>Low collection cost</li></ul></td><td><ul><li>Limited to users of the specific BSS</li><li>Potential privacy concerns</li><li>Data might be skewed in areas with lower BSS penetration and affected by exogenous factors</li></ul></td><td><ul><li>Chapter 6</li></ul></td></tr><tr><td>LSBN</td><td><ul><li>Can provide detailed information on trip motifs (e.g., leisure, sport) and some mode choices</li></ul></td><td><ul><li>Limited geographic coverage</li><li>Data accuracy issues</li></ul></td><td><ul><li>Chapter 3, in the form of UAPD</li></ul></td></tr><tr><td>Call Detail Records</td><td><ul><li>Very large sample size</li><li>Low collection cost</li></ul></td><td><ul><li>Limited to telecommunication network users</li><li>Spatial resolution limited to antenna's coverage area</li><li>Limited temporal resolution</li></ul></td><td><ul><li>Chapter 2</li></ul></td></tr><tr><td>Network Signalling Data</td><td><ul><li>Very large sample size</li><li>Low collection cost</li><li>High temporal granularity</li></ul></td><td><ul><li>Privacy concerns</li><li>Noise and oscillation effect</li></ul></td><td><ul><li>Chapter 3</li><li>Chapter 4</li></ul></td></tr></tbody></table>

on their behalf) in the form of travel diaries, they have fed plenty of transportation studies aimed to reconstruct mobility indicators, i.e., the daily number of trips, travel mode choices, routes, motifs and typical travel times, usually defined between OD pairs of Transportation Analysis Zones (TAZs).

However, as will be further discussed in Sec. 2.2, household travel surveys suffer from multiple well-known issues [18]: they can be very expensive to conduct, become quickly outdated, and, due to high non-response rates, are unavoidably biased towards the relatively small part of the respondent population.

Other relevant sources of mobility data include loop detectors, camera, RAdio Detection and Ranging (RADAR) and LiGht Detection And Ranging (LIDAR) sensors, which usually only provide aggregate counts of moving objects, thus not allowing to follow their movements in space and time.

Triggered by new technologies, this situation has deeply changed with the emergence of novel (often said opportunistic) sources of massive data for transportation. These data can be collected via, among others, smart ticketing public transit systems, widely adopted navigation systems and mobile applications (such as TomTom, Google Maps, Waze, etc.), highly popular social media platforms, or mobile phone records collected by telecommunication service providers. All these sources offer advanced possibilities to passively collect information on large masses of travellers and, consequently, to extract large-scale spatio-temporal patterns related to multi-modal travel demand and traffic variables.
1.1.1 Main Sources of Trajectory Data

Among the most traditional sources of trajectory data, GPS logs are produced using in-
formation from multiple satellites that provide precise localisation of moving objects (like
smartphones, vehicles) equipped with a GPS receiver. Modern navigation systems can thus
use the GPS to record the trajectories of a users movement with a high degree of spatial
accuracy (around 5 meters) and high temporal resolution (in the order of a few seconds),
regularly uploading the collected data to the navigation service provider's back-end system.
Floating Car Data (FCD) refers to the collection of traffic and mobility information from
moving vehicles, predominantly leveraging the GPS technology. In essence, vehicles act as
mobile sensors on the road, transmitting data about their speed, direction, and location.
Even though GPS-derived data could be very accurate, it is rare to achieve a high pen-
etration rate in the travellers' population, which makes it complicated, if not impossible, to
perform at affordable costs city-wide or region-wide studies that require, e.g., the estimation
of vehicle flows. However, FCD remain an invaluable data source for accurately analysing
mobility trajectories and estimating and forecasting congestion states, especially at local
scales like road segments or areas crossed by heavily trafficked roads. Due to these proper-
ties, FCD are often used as a source of ground truth to measure the quality of reconstruction
methods that utilise other datasets (see Chapters 2 and 3). Moreover, FCD can be instru-
mental in identifying anomalies and responses to disruptions by comparing traffic conditions
during specific events to typical situations averaged over multiple days of observations (see
Chapter 4). GPS applications for mobility are extensively surveyed in [19].

Public transport authorities usually equip their transportation infrastructure (e.g., metro and bus stations) and means (e.g., buses, tramways) with ticketing data collection systems, which allow storing aggregate (e.g., hourly) and spatially localised counts of transit usage. Recent public transit ticketing systems allow travellers to carry a personal smart-card, or their own credit card, and check-in/check-out for boarding and alighting at public transport stops. These systems can thus generate, at large scale, massive public transport trajectory data including, i.e., card identifier, stop origin, boarding time, stop destination, alighting time for each individual transit user. Smart-card data have thus the advantage to provide a large coverage of public transport trips, even at an individual level, with very high accuracy both in space and time (see Chapter 3 for their use to validate aggregate statistics on mobility derived from NSD). Nevertheless, the data suffers from missing validations (faults, frauds, etc.), and cannot provide any information on other transportation modes such as car, bike, walk, train. A detailed analysis of mobility patterns detection via smart card data can be found in [20].

As a sustainable alternative to motorised vehicles and transit, Bike-Sharing Systems (BSSs) serve as a crucial data source from a research perspective, shedding light on active mobility patterns, infrastructure utilisation, and evolving human behaviours. Typically, BSS datasets capture trip duration, start and end points, and often include user demographics. Analysing BSS data allows cities to appreciate the role of cycling as a resilient mode of urban mobility, aligning with current health challenges and broader sustainable development goals. Systems like Velo’v in Lyon and Vélib in Paris have evidenced the transformative potential of BSSs, as demonstrated by the surge in cycling trips following their launches [21, 22]. The COVID-19 pandemic further underscored the significance of BSSs in steering urban mobility transformations rooted in ecological awareness [23]. With public support, infrastructure, and incentives, BSSs advocate for a transition to sustainable, multi-modal mobility, especially in urban environments. However, it’s worth noting that while BSSs provide a convenient commuting option, cycling is still influenced by external factors like weather, topography, and urban design. These considerations present research challenges that must be acknowledged when leveraging BSS data in transportation studies (see, e.g., Chapter 6). A comprehensive review on this topic can be found in [24].

Location-Based Social Networks (LBSN) data have more recently emerged with the
widespread diffusion of social networking applications such as Twitter (now X), Facebook,
Instagram, Strava. These data typically include temporal and localisation information, to-
gether with content information (text, photos, ratings, etc.) related to the interaction of
people with places of the urban environment (e.g., check-ins at restaurants or museums,
sport activities in parks, participation to concerts and public events, etc.). LBSN data can
thus enable the identification of spatio-temporal patterns related to mobility, socioeconomic
urban activities as well as attendance and perception of places. Despite such advantages,
the availability of location data with LBSN is only limited to a small percentage of the social
networking population and app usages, which again hampers the possibility of performing
statistically relevant and large-scale studies on human mobility. A detailed survey on usages
of LBSN data for urban studies is provided in [25].

As already discussed in the introduction to this manuscript, CDR issued from mobile
network providers deserve a special attention as an indirect source of mobility data. CDR
are data collected by mobile network operators that contain information about the usage of
mobile phone services by customers. This data typically reports the date and time of call or
text message initiation and termination, the numbers of the calling and called party (before
anonymisation), the duration of the call or message, and the location of the phone when
the call or message took place [26]. The location of a Call Details Records log is usually
provided in terms of the identifier of the antenna where the communication event has taken
place. In recent data collections, CDR typically also report information on data connections
and the amount of traffic downloaded/uploaded by mobile phone apps running on the phone.
For privacy concerns, CDR are typically pseudo-anonymised<fnref n="1" /> and/or aggregated in time and
space. Multiple CDR logs related to different users are summarised by reporting the total
number of observed events at the given base station, with a given time periodicity (e.g.,
every hour). This is the kind of data used in the research activities described in Sec. 2.1.

CDR present a unique combination of desirable properties: (i) they offer unprecedented penetration as they are available for the whole subscriber base of a network provider, which typically covers tens or hundreds of millions of users; (ii) they are recorded continuously over long time periods, allowing fine-grained longitudinal studies over months or years; and, (iii) they are passively collected and maintained in curated databases for billing purposes, which makes them a very cost-efficient source of data for secondary use and analysis.

Due to such interesting properties, CDR have become an important source of data for various fields of research, such as telecommunications, urban planning, epidemiology, and sociology. In telecommunications, CDR have been used to study patterns of communication, the evolution of social networks, and the impact of mobile phone services on the economy. In transportation and urban planning, CDR have been employed to derive and validate general laws that govern human movements [27], reconstructing OD matrices [28], understanding urban land use dynamics [9, 10] or inferring population density shifts over time [29]. In epidemiology, CDR have been used to study the spread of infectious diseases [30]. In sociology, CDR have been used to study social inequalities, community structures, and individual behaviours [31]. In this sense, CDR are a primary source of information to learn the behaviours of urban complex systems and CIs. Results related to the use of aggregate CDR data are reported in the first part of Chapter 2 (Sec. 2.1).

However, and despite their significant advantages for human-centric mobility studies, CDR have fundamental limitations in terms of positioning accuracy in both space and time. In space, the mobile device locations can only be mapped to the coverage area or position of the base stations to which it is associated [32]; in time, the sampling process is driven by the occurrence of voice call establishments or text message transmission, which are both sparse and irregularly distributed [33]. Lastly, even though they might cover a significant portion of the population depending on the operator’s market share, these data are inherently skewed by the socio-economic traits of the provider’s network user base. Ultimately, these problems limit the utility of CDR for studies that require a high level of spatio-temporal detail [34], making it challenging to gain a detailed understanding of such behaviours.

<footnote n="1">¹The identifiers of the caller and the callee are replaced by random identifiers.</footnote>
NSD represent another kind of mobile phone passive data, which allows to overcome such limitations. NSD can be considered the latest generation of CDR-like data. Compared to CDR, NSD convey richer information on the activity of a mobile phone user, and can be collected for the entire subscriber base of a mobile telecommunication provider. They include all the network data-plane events generated by every device associated with the provider's radio access network across 2G, 3G, 4G and, more recently, 5G cellular technologies. NSD events are triggered by a variety of interactions: (i) voice and texting communications (i.e., call establishments and SMS transmissions, which are fully equivalent to those logged by CDR), (ii) handovers (i.e., device cell changes during communication), (iii) Location Area (LA) and Tracking Area (TA) updates (i.e., cell changes that cross boundaries among larger regions named LA in 2G/3G and TA in 4G, also affecting idle devices), (iv) active paging (i.e., periodic requests to update the location of the device started from the network side), (v) network attaches and detaches (i.e., devices joining or leaving the network as they are turned on/off), and (vi) data connections (i.e., requests to assign resources for traffic generated by mobile applications running on the device).

The diversity of events encompassed by NSD inherently enriches the data with additional information and increases their temporal frequency compared to CDR. Due to such properties, these data are appealing to explore human mobility at scales impossible to analyse with alternative data sources traditionally used in transportation, such as household surveys or GPS data. However, NSD come with fundamental issues and limitations, such as low accuracy in space, sparsity in time and high noise due to oscillation effects. These issues make them not directly applicable for fine-grained mobility studies. Analysing the potential of such data and addressing their issues for mobility analysis have been at the centre of the research described in the second part of Chapter 2 (Sec. 2.2), which presents the results of the initial research conducted on test instances of NSD. Despite the limitations associated with these initial data, such activities allowed verifying the potential of novel generation mobile phone data for reconstructing travel demand at a regional scale.

As further elaborated in Chapter 3, these attributes also make the data substantially larger in size, and more prone to noise, manifested as higher oscillation occurrences, than what is seen in traditional CDR data. Moreover, the characteristics of NSD don't necessarily enhance the spatial resolution of each logged event, which is still localised at the level of a network base station. Consequently, more sophisticated machine-learning based processing techniques are imperative to transform this raw data into meaningful and fine-grained insights regarding human mobility. Utilising extensive individual mobile phone datasets, Chapter 3 demonstrates the capability to derive detailed speed estimations (Sec. 3.1), GPS-like trajectories (Sec. 3.2) and multi-modal routes within the transportation network (Sec. 3.3). It also raises considerations about the need for anonymisation methods, and suggests strategies to retain highly-detailed mobility information while upholding privacy constraints (Sec. 3.4).

### 1.1.2 Analysis of a Massive Real-world NSD Dataset

To provide a basic understanding of how mobile phone data can provide useful information on mobility trajectories, this section introduces a real-world large-scale NSD dataset provided by Orange Innovation in the context of the ANR PROMENADE project, and related to the cities of Paris and Lyon. This section also provides empirical evidence of the increased accuracy of NSD when compared to CDR. This dataset has supported most of the research studies described in Chapter 3.

Firstly, we separated the dataset in two parts, denoted as $D_P$ and $D_L$, for Paris and Lyon, respectively. $D_P$ and $D_L$ were gathered during three consecutive months in 2019, from March 15 to June 15. The data collection, performed via the operator's network probes, was authorised by the Data Protection Officer (DPO) of Orange according to article
89 of the General Data Protection Regulation (GDPR)<fnref n="2" />, which provides an exemption for research, in particular for scientific and research purposes. The data were collected and processed exclusively on the Orange Innovation secure Big Data platform. The data were also pseudonymised and stored in a private directory in a server located in the operator premises, and accessible only to authorised researchers. All source data were deleted 12 months after the collection. The datasets included more than 150 billions of logged events overall, observed on a mobile phone network including more than 4,600 antennas. The resulting total user base tallied to over 10 millions of individual mobile subscribers identifiers (IMSI) and over 3 millions of estimated residents in the two considered cities. Additional details on the $D_P$ and $D_L$ NSD datasets are reported in Tab. 1.2.

<table><caption>**Table 1.2:** Statistics on the large-scale network signalling data.</caption><thead><tr><th rowspan="2">Dataset</th><th rowspan="2">City</th><th>Area</th><th>Nb</th><th colspan="2">2G events (·10<sup>6</sup>)</th><th colspan="2">3G events (·10<sup>6</sup>)</th><th colspan="2">4G events (·10<sup>6</sup>)</th></tr><tr><th>(km<sup>2</sup>)</th><th>antennas</th><th>Nb IMSI</th><th>Nb events</th><th>Nb IMSI</th><th>Nb events</th><th>Nb IMSI</th><th>Nb events</th></tr></thead><tbody><tr><td>D<sub>L</sub></td><td>Lyon</td><td>1,506</td><td>646</td><td>1.7</td><td>83</td><td>2.8</td><td>1,470</td><td>2.9</td><td>20,994</td></tr><tr><td>D<sub>P</sub></td><td>Paris</td><td>5,784</td><td>3,972</td><td>5.9</td><td>850</td><td>6.5</td><td>10,166</td><td>6.1</td><td>116,461</td></tr></tbody></table>

The main challenge we addressed consisted in reconstructing from $D_P$ and $D_L$ the separate trips of each mobile phone device with a high spatial accuracy to infer information about the exact paths travelled (e.g., as the sequence of road segments, or the combination of transport modes). City environments exacerbate the problem, as they feature difficult-to-track short trips over entangled dense road layouts with multiple transportation modes. Traditional CDR are not suitable to address the task, due to their limited spatial resolution and sampling frequency. For instance, Fig. 1.1(a) shows the localisation samples recorded by CDR for an exemplary urban displacement; a linear interpolation of the CDR samples (solid red) is superposed to the same user trajectory, as observed via GPS data (dotted blue). The figure makes it clear that inferring the actual movement from CDR is an arduous mission.

<figure><img src="image_6.png" /><figcaption>**Figure 1.1:** Examples of inference of one trajectory of a volunteer from (a) CDR, and (b) NSD, compared to the corresponding GPS trace (in blue) used as ground truth.</figcaption></figure>

By visually comparing Fig. 1.1(b) to Fig. 1.1(a), it appears evident how the variety of collected events with NSD may increase significantly the number of points observed for a given user, paving the way to finer-grained studies of human mobility. The assortment of situations captured by NSD (i.e., voice calls, text messages, handovers, location updates, active paging, attaches/detaches and data connections) is much wider than the sole call-and-text-related events in CDR. This naturally leads to a much higher sampling frequency of the locations of devices (hence, users) over time in NSD with respect to traditional CDR.

A quantitative inspection of the increased temporal accuracy of NSD is provided in Fig. 1.2. The two plots present the Cumulative Distribution Function (CDF) of the time

<footnote n="2">²https://gdpr.eu/tag/gdpr/</footnote>
<figure><img src="image_7.png" /><figcaption>**Figure 1.2:** CDF of inter-event times recorded in NSD, CDR, and CDR+. The plots refer to (a) median, and (b) average times per user.</figcaption></figure>

between subsequent NSD events; specifically, the distributions are computed over the (a) median and (b) mean inter-event time recorded for each device, hence they provide a fair view of the statistics across the observed population. We also report equivalent CDF obtained using other kinds of mobile network data: (i) CDR, which, as already mentioned, only capture voice and texting communication events, and (ii) CDR augmented with LA and TA update events, which we term CDR+. The rationale of this comparison is that CDR are the most widely adopted source of data from mobile networks, whereas CDR+ have been recently used for human mobility trajectory inference in some studies from the related literature [35]. We directly extrapolated CDR and CDR+ from the available NSD database, by simply retaining only the spatio-temporal samples generated by the events that are captured by such data sources, while filtering out the information associated to all other network event types.

The distributions in Fig. 1.2 yield a number of interesting observations. NSD grants a median inter-event time below 1 minute for 90% of the users, while that figure grows to 5 minutes for CDR+ and over 30 minutes for CDR. Per-user averages that are biased by long inactivity periods highlight even more the difference between the data sources: NSD keeps averages below 15 minutes for 90% of the users, whereas CDR+ and CDR record mean inter-arrivals of up to 1 hour and 3.5 hours for the same user fraction. The conclusion is that NSD ensure a sampling rate increase of more than one order of magnitude with respect to CDR and of a factor 5 over CDR+. Importantly, these results are fairly uniform over the considered population.

NSD are not supposed to bring any advantage over other classes of mobile network positioning data in terms of the absolute spatial accuracy of each location sample. As a matter of fact, NSD, CDR, CDR+, and any other network data types, are collected on the same radio access network infrastructure: therefore, the locations used to geo-reference the events are those of a matching set of base stations to which mobile devices associate over time. To prove our point, we run experiments with ground truth GPS data collected by a small set of volunteers. For each volunteer, we computed the distance between the location of the antenna associated to all generated network events and the corresponding GPS position at the time. Repeating the process for all CDR, CDR+ and NSD events yielded very similar average distances, between 0.26 and 0.28 km, in the three cases.

However, NSD provide a much more accurate spatial representation of the trajectory as a whole, as a direct consequence of the increased sampling rate. This is clearly shown in plots (a) and (b) of Fig. 1.1 for a single trajectory, as well as in plots (a) and (b) of Fig. 1.3 for multiple trips of a same user. These figures highlight the capability of NSD to capture individual mobility patterns in a much more exhaustive way compared to CDR. The unprecedented spatio-temporal resolution of NSD is at the basis of the framework described in Sec. 3.2.
<figure><img src="image_8.png" /><figcaption>**Figure 1.3:** Sample weekly trajectories of one voluntary user inferred from (a) CDR, and (b) NSD.</figcaption></figure>

An important aspect of the data employed for our study is that it covered three generations of cellular network technologies. This let us investigate the relevance of events generated by 2G, 3G, and 4G events on the accuracy of the positioning data. Tab. 1.2 breaks down the number of unique devices observed under each technology, as well as the number of events recorded, separately reported for the two large-scale datasets related to Paris and Lyon, $\mathcal{D}_P$ and $\mathcal{D}_L$, respectively. The figures evidence how the number of users that can be monitored by the three radio access technologies is comparable, and partially overlapping. However, the sets of geo-referenced NSD collected for the monitored devices is completely different: the number of events grows by more than one order of magnitude when moving from one cellular generation to the next.

While this is a clear result of the increased consumption of mobile services and associated growth of mobile data traffic that newer network technologies support, it further distinguishes our study from the many previous works that date back to the 2005-2015 period, and that could only rely on limited 2G and 3G data (which includes also the results discussed in Sec. 2.2).

## 1.2 Resilience Modelling

To guide readers through the vast and often misapplied terminology surrounding “resilience”, a word already frequently used in this document without a precise definition, we present a brief introduction below. It covers essential terms, definitions, and methodologies from the realm of resilience modelling, which will be employed throughout this document

The word “resilience” is derived from the Latin term *resilire*, meaning “to bounce” or “to rebound”. The term was initially used in material science to describe the resistance of materials to physical shocks and has been since then widely used in other fields such as biology [36], social sciences [37], and engineering [13] to denote the speed at which a complex system bounces back from the degradation of its functions deriving from disruptive events [38]. Resilience has also important relationships and differences with the concept of *reliability*, the latter usually referring to the ability of a system to consistently perform its functions for the period of time intended under a *well-specified set of operating conditions encountered* [39]. Moreover, reliability typically assumes no or weak correlations among the different system components that can exhibit failures as a consequence of a disruption. Conversely, resilience does not consider explicit assumptions on the nature and extent of disruptions, which often have the nature of *surprises* [40], and presumes the existence of possible complex interactions and inter-dependencies between the system components,
forming a *complex network* [38]. These differences are particularly relevant in our context of transportation networks, exposed to hardly predictable disruptive events and composed of multiple topologies associated with different transport modes, typically interacting according to complex spatio-temporal patterns, and often exhibiting long-range spatial correlations [38].

Given the diverse and complex responses of systems to perturbations, as well as the highly heterogeneous nature of the perturbations, resilience is a multifaceted and broad domain of research. As a result, there is currently no consensus on the definition of resilience, as noted in prior work [41]. First of all, as discussed in [42], resilience can be defined with respect to four dimensions: *technical*, *organisational*, *social*, and *economic*. Technical resilience relates to the ability of physical systems to perform well during disruptions through the engineered features of the constituting and interacting components. Organisational resilience refers to the capability of critical facilities management organisations to make decisions and take actions that increase robustness, redundancy, resourcefulness, and rapidity. Social resilience involves reducing negative consequences on communities due to the loss of critical services. Economic resilience involves reducing both direct and indirect economic losses caused by the occurred disruption. In this HDR thesis, we will mostly refer to technical resilience, in an acceptation that revolves around four fundamental, partially overlapping concepts [42, 40, 43, 38], defined as follows.

Firstly, *robustness* refers to the *preparedness* of the system to absorb and resist disruptions, thanks to the abundance of resources (*i.e.*, *redundancy*) and the capability (*i.e.*, *resourcefulness*) to identify problems, establish priorities, and mobilise resources when conditions exist that threaten to disrupt some element of the system. Robustness usually refers to a situation in which the system does not leave its stable state of operation despite the occurrence of disruptions, which should be therefore fully absorbed by the system without significantly affecting its performance. *Vulnerability* is a term strictly related to robustness. Specifically, vulnerability can be considered the antinome of robustness [44]. Vulnerability is defined as the expected damage (consequences *C*) that occurs in a system when a hazardous event *A* occurs and the system is exposed to it [45]. The triplet (*A*, *C*, *U*) is the definition of *risk* [45], with *U* representing the *uncertainty* of the occurrence of event *A*. Vulnerability assessment tries therefore to quantify the uncertainty *U* as well as the severity of consequences *C*, with associated probability *P*, given the knowledge *K* of the actual state of the system under analysis, *i.e.*, $V = (C, P, U, K|A)$ [44].

Secondly, the terms *rapidity*, *graceful extensibility* or *flexibility* can all refer to the capability of the system to stretch its existing resources to meet priorities and achieve goals in a timely manner to contain losses in the presence of disruptions. In particular, rapidity refers to the time passing between the stable state of operation and the new state of operation associated with a reduced level of service, while the extensibility capability of the system determines the amount of performance loss associated with the new degraded state of operation. The residual capacity associated with the degraded state is normally identified with the term of *survivability*.

Thirdly, *recovery* is a central aspect of resilience, corresponding to a system's capability to rebound to normal operation from the degraded state of equilibrium reached in the aftermath of a disruption. Recovery capabilities are largely dependent on the procedures, structures, and resources that have been developed before the occurrence of a disruption to handle expected or unexpected emergency situations. They are especially relevant when the event falls outside the scope of variations and disturbances that the system in question is known to be capable of handling. The traditional notion of recovery corresponds to the definition of ecological or static resilience, reflecting whether the system returns (or not) to the same state of function after some external perturbation or shock. A second, more recent notion of recovery corresponds instead to engineering or dynamic resilience, which refers to the rapidity of this recovery to a new level of operation [46]. It is worth highlighting that the new recovered state may not necessarily correspond to the initial normal state, as it
can be associated with lower or higher levels of performance with respect to the situation preceding the disruption.

Fourthly, *adaptability* refers to the capability of the system to sustain adaptation over its life cycle, by changing its internal organisation, structure and components with respect to predictable changes or sudden disruptions. This notion requires the system to be flexible in its architecture in order to sustain changes over long scales, which normally include multiple cycles of disruptions and failures.

It is important to note that these concepts are strictly interrelated and that a resilient system should exhibit all these characteristics to varying degrees, depending on the specific risk situation the system is exposed to (see Fig. 1.4(a)). We will explicitly refer to these properties in this and the following chapters to clarify how the developed approaches and tools support specific components of transport resilience.

### 1.2.1 Main Approaches to Assessing Resilience: a High-level Classification

The prologue of the book on resilience engineering from Hollnagel et al. [13] provides a valuable conceptual framework to classify relevant approaches from the literature on the subject of resilience quantification and management.

<figure><img src="image_9.png" /><figcaption>**Figure 1.4:** The general framework for resilience engineering proposed by Hollnagel et al., 2006.</figcaption></figure>

Hollnagel et al. present resilience engineering as a field encompassing traditional safety (or risk) management. Resilience engineering focuses in fact on both *expected and unexpected conditions rather than just avoiding failures*, as normal outcomes are way more probable than extreme events, incidents and disasters (see Fig. 1.4(a)). According to the authors, it is in fact *necessary to look at success as well as at failures precisely in order to understand failures or why things can go wrong*. This represents a very interesting definition that clarifies how resilience engineering not only deals with safety but also with system efficiency, and that no fundamental conceptual differences exist between performance that leads to failures and performance that leads to successes. The framework also provides a clear description of what kind of operational solutions one should consider for improving system resilience. As graphically presented in Fig. 1.4(b), resilience engineering covers four main cornerstones, namely: (i) *knowing what to do*, i.e., the set of prepared strategies to *stretch* and *recover* in the presence of *actual* sporadic or recurrent disruptions; (ii) *knowing what to look for*, i.e., the capability to *monitor* the actual or potential *critical* (external or internal) threats a system is exposed to; (iii) *knowing what to expect*, i.e., the set of features that help to *anticipate potential* future threats, changes and opportunities by means of *adaptation*.
capabilities; (iv) *knowing what has happened, i.e.*, the ability to *learn* from past *factual success* or failure stories in the system's life-cycle.

Similar ideas were advanced by Bellini *et al.* in [14], where resilience-engineering is considered as composed of four key elements: *respond, anticipate, monitor*, and *learn*. The authors suggest that digital technologies can enhance the efficiency and effectiveness of resilience-building processes. For example, cloud computing, big data, and simulation tools can be used to *anticipate* potential risks and test emergency responses. *IoT* sensing and 5G technologies can improve urban *monitoring* and warning systems. Real-time data processing can contribute to more efficient *emergency responses*, while advanced data analysis techniques based on machine learning and optimisation can support *learning* and policy-making. The literature generally refers to this approach as *resilience analytics* [47, 48], which involves using data-driven processes to support resilience through descriptive, predictive, and prescriptive modelling.

In their recent survey [49], Serdar *et al.* analyse the abundant literature on resilience assessment for urban transport, identifying the main approaches adopted so far in the field. The authors identify five main categories of approaches: *big data methods and technologies*, *simulation methods*, *model optimisation*, *graph theory*, and *probability-based methods*.

*Big data methods and technologies* involve the use of large volumes of data from various sources, such as social media, mobile phone and GPS data, to analyse and monitor the performance of transportation networks in real-time. The data can be structured, semi-structured, or unstructured, and can be processed through different analytical tools and algorithms to detect abnormalities and reflect on past events. The authors consider big data methods suitable for assessing the resilience of transportation networks in terms of system performance, and they can provide valuable insights for smart city applications. However, the effectiveness and resource consumption of big data methods largely depend on using the correct indicators and data type, being often not capable of predicting future performance with respect to unprecedented events. Moreover, the authors insist on the absence of a good understanding of the actual, multi-modal and time-varying travel demand, which is a fundamental component that needs to be learned in order to anticipate and recover from disruptions.

*Simulation methods* are identified as another crucial category of approaches for assessing the resilience of transportation networks under fresh developments and unprecedented events. They involve creating models of transportation networks and testing them under different scenarios and traffic configurations. Simulation methods can consider different network components and their interactions, but they are resource-demanding, which makes it hard to scale up. They also require calibration and a deep understanding of the system to apply them correctly.

*Model optimisation* is considered suitable for post-disaster planning, preparedness, and recovery budget allocation. The approaches falling in this category involve developing mathematical models of transportation networks and optimising them to allocate resources efficiently. Model optimisation methods can be used to optimally schedule and prioritise the allocation of resources and evaluate the effectiveness of different mitigation plans. However, developing an efficient mathematical representation of the system requires a good understanding of both the system and the threats it faces. Methods in this category often combine with graph-based approaches for modelling the underlying transport network as well as simulation to compute generalised costs included in the objective functions or in the problem constraints.

*Graph theory* approaches to resilience engineering traditionally involve using network topology modelling and connectivity analysis to study and monitor the performance of transportation networks. Methods in this category generally adopt a simplistic representation of the transport system, which has however the important advantage of resource efficiency, thus being particularly suitable for analysing large networks. Graph theory methods do not naturally account for traffic dynamics, congestion formation, redistribution, and capacity
<table><caption>**Table 1.3:** Methods for resilience assessment, adapted and extended from Serdar *et al.*, 2022.</caption><thead><tr><th>Assessment method</th><th>Remarks</th><th>Related Chapters</th></tr></thead><tbody><tr><td>Big data, IoT, Machine Learning</td><td><strong>Suitability/Strength</strong><ul><li>Suitable for smart city applications and resilience assessment through real-time system performance assessment, detecting the abnormalities, and reflecting on previous events.</li><li>Using correct indicators and the type of data (e.g., structured, semi-structured, or unstructured) play a central role in its effectiveness and resource consumption.</li></ul><strong>Limitations</strong><ul><li>Under-exploited for understanding and modelling of multi-modal, time-varying travel demand.</li><li>Could be not suitable for predicting future performance toward unprecedented events.</li></ul></td><td>Chapter 2, Chapter 3, Chapter 5</td></tr><tr><td>Simulation</td><td><strong>Suitability/Strength</strong><ul><li>Suitable to assess fresh developments and unprecedented events, can consider different traffic compositions, network components, and their interactions.</li></ul><strong>Limitations</strong><ul><li>Resource-demanding, which makes it hard to scale up.</li><li>Require calibration and deep understanding to apply it correctly.</li></ul></td><td>Chapter 4, Chapter 5</td></tr><tr><td>Graph theory (complex networks, network topology)</td><td><strong>Suitability/Strength</strong><ul><li>Suitable for large networks since it is simple and resource-efficient.</li><li>Can be used in conjunction with other methods.</li></ul><strong>Limitations</strong><ul><li>Usually does not account for traffic performance, travel demand, and the dynamics of the urban transport system (traffic redistribution, congestion formation, etc.)</li><li>Basic static modelling of disruptions, mostly based on edge cut and nodes' removal. Does not typically take into account partial capacity reduction deriving from disruptions.</li></ul></td><td>Chapter 3, Chapter 4, Chapter 5</td></tr><tr><td>Model optimization</td><td><strong>Suitability/Strength</strong><ul><li>Suitable for post-disaster planning, preparedness, and recovery budget allocation.</li></ul><strong>Limitations</strong><ul><li>Require good understanding of the system and threats to develop an efficient mathematical representation of the system.</li></ul></td><td>Chapter 4</td></tr><tr><td>Probability-based methods</td><td><strong>Suitability/Strength</strong><ul><li>Suitable for assessing system resilience in terms of its reliability.</li><li>Help to evaluate different arrangements and prioritizing severe disturbances mitigation plans.</li></ul><strong>Limitations</strong><ul><li>Rely on the accuracy and representativeness of the statistical data used in the development of probability estimations.</li></ul></td><td>Chapter 5</td></tr></tbody></table>

reduction due to accidents, which represents one of the most relevant limitations of these approaches, usually requiring them to be used in conjunction with other methods, such as traffic simulation and big data processing. The modelling of time-related dynamics can be addressed with more complex modelling approaches such as dynamic and multi-layer networks.

*Probability-based methods* involve assessing the resilience of transportation networks by modelling the probability of failures as well as the causal relationships among the system components, usually leveraging Bayesian models or data-derived statistical models of system performance. They help to evaluate different arrangements and prioritise severe disturbance mitigation plans. The accuracy of probability-based methods strongly depends on the representativeness of the statistical data used in the development of probability estimations.
Tab. 1.3, borrowed and partially adapted from [49], summarises the main conclusions from the authors and identifies the chapters of this HDR thesis that deal with each of the mentioned categories.

## 1.2.2 Vulnerability Analysis of Transport Networks

In the following, we propose a concise literature review, largely based on [50, 46, 49], about quantitative approaches and metrics for vulnerability analysis of urban transport networks, leaving the interested reader to the numerous surveys on the broader topic of network resilience engineering for more details [13, 38, 51, 47, 52].

In urban transportation, vulnerability refers to the degree of exposure of a system to hazards and losses due to the risks present in its operating environment [53]. Analysing the vulnerabilities of a system corresponds to the *knowing what to expect* dimension in the general framework proposed by Hollnagel et al. [13] (see Fig. 1.4(b)).

In [43], one of the first general-purpose methods to model and quantify vulnerabilities by measuring the impact of disruptions on a complex system was introduced. The framework also allows evaluating the positive effect of recovery strategies on the disrupted system (i.e., the *knowing what to do* cornerstone). Specifically, the system under analysis is assumed to be stressed at time $t_e$ by a disruptive event $e_j$ that causes service loss to the system. As a result, the system shifts from the original state of regular operation $s_0$ to a disrupted stable state $s_d$ at time $t_d$. Recovery actions are taken at time $t_s$ bringing the system to a recovered state at time $t_f$. The transition sequence is graphically described in Fig. 1.5, borrowed from [43].

<figure><img src="image_10.png" /><figcaption>Figure 1.5: Transition among states of a disrupted system as proposed in Henry et al., 2012.</figcaption></figure>

The resilience $R(t_r)$ of the system at time $t_r \in (t_d, t_f)$ is thus defined as the ratio of recovery at time $t_r$ to loss suffered by the system at time $t_d$:

$$ \mathcal{R}(t_r) = \frac{\text{Recovery}(t_r)}{\text{Loss}(t_d)} \qquad (1.1) $$

The main idea behind this generic definition of resilience is that if the recovery equals the loss, the system is entirely resilient. If no recovery action occurs, then the system has shown no resilience. The value of resilience can be computed based on a figure-of-merit $F(\cdot)$ representing a generic quantifiable indicator of whole system performance, such as
connectivity, throughput, reliability, etc<fnref n="3" />. Resilience can be thus computed at time $t_r$ as:

$$ \mathcal{R}(t_r|e_j) = \frac{F(t_r|e_j) - F(t_d|e_j)}{F(t_0) - F(t_d|e_j)}. \qquad (1.2) $$

In the previous equation, if $F(t_r|e_j) = F(t_0)$, the value of resilience $\mathcal{R}(t_r|e_j)$ equals 1, meaning a fully restored system. In a similar approach, Bruneau et al. [42] propose to compute the area under the curve $1 - Q(t)$ to quantify resilience and compare different recovery strategies, specifically:

$$ \mathcal{R}(t_r) = \int_{t_0}^{t_r} [1 - Q(t)] dt. \qquad (1.3) $$

where $Q(t)$ represents the fraction of nominal performance exhibited by the system during disruption, i.e., $Q(t) = \frac{F(t|e_j)}{F(t_0)}$, using the formalism from [43]. Sec. 4.1 will build upon this approach to propose a solution to simulate the impact of day-to-day perturbations on transport networks [55].

Similarly to Bruneau et al. (Eq. 1.3), Mattsson and Jenelius introduce in [46] the concept of conditional transport vulnerability, defined as the aggregate consequence of a disruption scenario. It is measured by the area between the dotted (blue) line of nominal behaviour and the relevant curve representing the reduced level of function, as shown in Fig. 1.6. The latter curves depend on ex ante mitigation or ex post adaptation actions. By knowing the fragility points of a transport system, adequate proactive actions can be envisioned, thus contributing to strengthening network resilience.

<figure><img src="image_11.png" /><figcaption>Figure 1.6: Resilience curve and conditional vulnerability as from Mattsson and Jenelius, 2015.</figcaption></figure>

Based on these general concepts, multiple approaches and metrics have been proposed for analysing and quantifying transport network vulnerabilities. As proposed in [46], they can be categorised into two main groups: topological and system-based, detailed in the following.

## Topological Vulnerability

In topological approaches, graph theory is used to model the transport network. The latter is represented as a graph, i.e., $G = (V, E)$, with $V$ denoting the set of nodes and $E$

<footnote n="3">³In our context related to transportation, candidate metrics could be traffic flow, the inverse of the total travel time, network efficiency [54], etc., depending on the specific properties one would like to highlight in the definition of resilience.</footnote>
the set of links connecting the nodes. When a transport network is composed of a single mode, simple graphs can represent it. However, when multiple transport modes (e.g., bike, car, bus/tramway/subway/railway lines, etc.) and/or the entire set of Public Transport Network (PTN)/railway lines need to be represented at once, more complex representations, such as multi-layer network models, are necessary [56, 57]. In this case, a model such as the one introduced in Sec. 3.3 for multi-modal path reconstruction via TRAjectory inference at scale with mobile Network Signaling data (TRANSIT) is used. For a mono-modal/mono-line transport network, nodes represent stations in the case of PTN/railway, or road intersections in the case of the road infrastructure. Edges represent the connections between transit/railway stops or road segments between pairs of road intersections, respectively. Note that multiple formalisms exist, such as *L-space* and *P-space*, for representing transit networks, as widely discussed in [58]. In some works, such as [59], a dual network representation is used, with streets identified as nodes and intersections identified as links of unit length. Similar approaches are used in modelling air transport networks (which are not covered in the research activities described in this HDR thesis), with nodes representing airports and edges representing direct connections between airports.

Graphs are traditionally unweighted or weighted by means of attributes, such as road length, segment capacity, free-flow travel times, etc., that are *static*, i.e., do not change (or change very slowly) over time. In topological approaches, it is typical to use complex networks metrics to identify the most important, and therefore vulnerable, components of the transport network topology. Similarly to a reverse percolation process [60], disruptions are traditionally modelled via removing edges or nodes from the network topology according to random or targeted attack strategies [61]. The goal is to identify the specific set or the critical ratio of components that, once removed, would make the system incapable of performing its operations. In targeted attack strategies, nodes or edges are removed based on degree, betweenness or closeness centrality-based<fnref n="4" /> ranking [61, 63, 64]. Indicators used to measure the impact of modelled disruptions include the size of the largest connected component, the average path length, the global clustering coefficient, global and local efficiency [64, 65, 66]. Topological approaches have been consistently applied to road, public transport and air transportation networks. These researches have usually indicated *small-world* properties<fnref n="5" /> of road networks [61, 67, 38], which thus appear very robust against attacks based on random and degree strategies, while very vulnerable against attacks based on the betweenness strategy, i.e., nodes and edges traversed by a high number of shortest paths should be significantly protected. Concerning transit, different works have proven the *scale-free* properties<fnref n="6" /> of some real-world transport networks (e.g., public transport of Beijing [68], public transport network of several European cities [69], subway system of Shenzhen [70]), which indicates resistance to random failures but vulnerability to attacks on high-degree nodes; absence of either scale-free or small-world properties for some subway networks [71, 72], which indicates

<footnote n="4">⁴Degree centrality is a node-based centrality measure that counts the number of edges incident to a node. Betweenness centrality, on the other hand, is an edge or node-based centrality measure that quantifies the extent to which a node or edge lies on the shortest paths between other pairs of nodes in the graph. Closeness centrality is another node-based centrality measure that quantifies how quickly a node can reach other nodes in the graph. It is the inverse of the sum of the shortest path distances from a node to all other nodes in the graph. For formal definitions, the reader can refer to [62]. Betweenness centrality is widely discussed in Sec. 4.1, 4.2.2 and 5.4. Degree centrality is instead more extensively studied in Sec. 4.2.1.</footnote>

<footnote n="5">⁵A small-world network is characterised by high values of global clustering coefficient, i.e., neighbours of a node tend to be neighbours in turn, combined with a small average path length between pairs of nodes. Small-world networks are often observed in real-world networks, such as social networks, where there is a high degree of local clustering, but also a relatively short path length between any two individuals in the network.</footnote>

<footnote n="6">⁶A scale-free network is characterised by a highly-skewed degree distribution, where a small number of nodes have a very high degree, while the vast majority of nodes have a much lower degree. Scale-free networks are often observed in real-world networks, such as the World Wide Web or biological networks, where a few highly connected nodes (known as hubs) play a disproportionately important role in the network's structure and function. Formally, a network is considered to be scale-free if its degree distribution follows a power law distribution, i.e., the probability $P(k)$ of a node having degree $k$ is proportional to $k^{-\gamma}$, where $\gamma$ is a parameter that characterises the degree distribution of the network.</footnote>
high fragility of such transport infrastructures. Scale-free properties have also been observed
in air transportation networks [73].

System-based Vulnerability

Despite the interesting results, the main limitation of topology-based vulnerability ap-
proaches is the oversimplified representation of the transport system that completely ne-
glects both travel demand and the dynamics of traffic operations (see Sec. 4.1). Resilience is
in fact not purely a structural problem: for instance, road networks can easily become grid-
locked in presence of natural disasters that do not impact at all the physical infrastructure
(roads, bridges, and railways), but that totally shatter the typical mobility demand patterns
or the supply-side of the transport system. In other words, there is strong evidence that one
must monitor and control the dynamic states of the system in order to properly address the
resilience of networked CI [38].

To capture the dynamic nature of critical infrastructure systems, time-varying graphs have been recently utilised to incorporate time-dependent interdependences into resilience metrics: studies by Adjetey-Bahun et al. [74] and Lu [75] have compared the performance of static and dynamic resilience metrics concluding that, under normal traffic conditions, both metrics are equally effective. However, in the event of a disruption, interdependences and passenger flows in the network render the static indicator less efficient and highlight the need to consider the time dimension in vulnerability analysis. These studies represent the basis of the modelling approach described in Sec. 4.2.

The system-based category of vulnerability analysis approaches involves the representa-
tion of transport systems through demand and supply models, utilising micro-, macro- and
mesoscopic traffic simulators to generate realistic time-varying variables such as travel times,
flow, and cost variables. These variables are then used to compute vulnerability indicators.
Additionally, network equilibrium is used to model route choice, taking into account conges-
tion and delays. Approaches in this category, mostly related to road network vulnerability,
consider disruption modelling via node or edge removal similar to topological-based method-
ologies. However, they differ in that they exploit the aforementioned demand-supply models
and simulation tools to generate realistic system dynamics and identify the most important
links or nodes with respect to such dynamics. Critical nodes or edges are defined as those
that, if removed, would have the most significant impact on either increasing the shortest
path duration or reducing the maximum flow between a given set of origin-destination pairs.
More advanced approaches consider the transport system’s performance with respect to fluc-
tuations [76] or significant variations [77] of travel demand or link capacity. Performance
is evaluated based on indicators such as travel time reliability (i.e., the probability that
a trip can be completed within a specified time interval) and capacity reliability (i.e., the
probability that a network can accommodate a specified level of travel demand).

Other approaches in this category propose different vulnerability indices. Researchers have used the increase in the user-equilibrium (total) travel time [78, 79], the reduction of the demand weighted global efficiency [80], the time to link re-opening [81] and the amount of unsatisfied demand [78] to measure the decrease of service caused by the loss of a road link. Similarly to what we propose in Sec. 4.1, some isolated works have also considered more flexible modelling of disruptions with partial link capacity reduction modelling [82]; aggregate indices of vulnerability to characterise the exposure to risks of an area or of the entire transport network [83, 84], (see Sec. 4.2.1); indicators of local vulnerability only accounting for travel time increase for trips originating in a given region, when links in the network are randomly closed [85]; indicators of global vulnerability derived from local events by computing the total travel time increase observed when all links in a region are closed [83]. The last approach is particularly relevant to model area-covering disruptions deriving from snowstorms, floods and earthquakes.

Vulnerability has been also measured in terms of accessibility reduction or remoteness
increase [86, 87], by also taking into account spatial data on population [88] in the definition of accessibility. Some authors have studied the correlation between link importance metrics and static topological properties of the link, (e.g., free flow speed, capacity, length, etc.) [85, 89]. According to probabilistic-based methods and historical data, some researchers have modelled the development of traffic flows after network disruption based on past experience and beliefs about future conditions [90]. Other approaches adopt mathematical modelling and optimisation techniques to identify worst-case scenarios and best responses to the latter. Solutions based on attacker/defender two-player games [91] and bi-level optimisation [92] have also been proposed.

System-based approaches for vulnerability assessment have been considered for rail and public transport networks. Subway and rail networks have been proven to be more sensitive to disruptions than road networks because of the lack of excess capacity and the limited amount of alternatives to redirect trains in case of disruptions. These features often lead to total loss of service and high delays on subsequent trains. For instance, Rodríguez-Núñez and García-Palomares [93], have evaluated among others, the importance of links in a subway system and quantified the amount of unsatisfied demand in terms of trips following the closure of the most important ones.

To account for the interactions between supply and demand and the accumulated effect of disruption on PTN, Cats and Jenelius [94] have introduced a novel measure of betweenness centrality that takes into account the expected number of trips and passengers between pairs of stops over time periods. This dynamic and extended definition of betweenness centrality is identified as a better indicator to evaluate the impact of disruptions on passenger welfare than the static betweenness centrality, which is based solely on network topology. In a follow-up work [95], the same authors investigated solutions for reducing the vulnerability of the public transport network by increasing the capacity on lines that can serve as alternatives when critical links are disrupted, and by identifying the lines where capacity increases are the most effective.

Peterson and Church [96] developed a framework for modelling rail freight transport vulnerability with similar approaches to those proposed in [78, 79] for road networks, based on the notion of link importance. A statistics-based framework is proposed by Hong *et al.* [97] to study the vulnerability of the Chinese railway system to floods and the effectiveness of alternative mitigation strategies. Both studies highlight the relevance of betweenness centrality as a means for selecting candidate links for vulnerability assessment and resilience enhancement. Solutions for air transport based on the quantification of delayed flights versus on-time flights during disruptions are considered in [98].

Overall, vulnerability analysis using system-based solutions is generally less straight-forward when compared to topological approaches, as it requires (i) more computational resources to simulate system dynamics, (ii) massive data to acquire knowledge on the transportation system's dynamic states. However, it is well known that system-based solutions typically yield more realistic and reliable results because they incorporate both supply and demand-side modelling in dynamic settings, and because they allow for a more accurate and wider-spectrum representation of the consequences of disruptive events. These advantages well-justify the growing interest of the research community towards these approaches, which tend however to be increasingly used in conjunction with topological and big data processing techniques for efficiency purposes. These ideas have been at the very core of the research described in the next chapters, which propose solutions to evolve vulnerability analysis (Chapter 4) and monitoring towards large-scale and real-time settings (Chapter 5).

In any case, both categories of approaches have traditionally ignored the complex multi-modal nature of the transport system. Topological approaches have the merit to be more easily adaptable to multi-layer networks that can be used to model multi-modal transport networks. Some recent works have thus considered the problem of performing vulnerability analysis on multi-modal transport networks [57, 99], using centrality metrics, extended to multi-layer networks, to perform structural static analysis of multi-modal transport networks
exposed to disruptions. Despite not explicitly addressing the problem of disruption analysis, Bellocchi, Latora and Geroliminis [100] have very recently proposed a system-based measure, called dynamical efficiency, to evaluate the performance of a multi-modal time-varying transportation network. The metric considers both the topology of the network and the temporal changes in traffic flow to detect critical zones for traffic congestion and bottlenecks in a transportation system. Dynamical efficiency for a given layer (i.e., a single transport mode) is defined by considering the average ratio between the minimum travel time (i.e., free-flow travel time) of a path between any two nodes in the network to the actual travel time of that path over a time period. The metric is extended to multi-modal settings by considering shortest *H* paths, introduced by the authors to reduce the complexity of multi-modal shortest path computation. Based on this notion, additional derived metrics, such as the layer performance gain and station centrality, are proposed to quantify the relevance of an entire transport mode or of specific stations of a multi-modal network. The effectiveness of this approach has been verified on a real-world multi-layered transportation system, from the city of Shenzhen, China. The introduced metrics appear to allow for the identification of spatio-temporal congestion patterns, the quantification of the expected usage of inter-modal junctions between two different transportation means, as well as the assessment of equivalent transportation alternatives. These ideas, being already partially addressed in the modelling framework of the PROMENADE platform described in Chapter 5, are also at the centre of the longer-term perspectives which will focus on extending the notion of resilience assessment and enhancement to a multi-domain context (Chapter 6).
# Chapter 2

## Mobile Traffic Profiling and Travel Demand Estimation

Over the last two decades, global mobile traffic has surged to unprecedented levels, with a compound annual growth rate higher than that observed for Internet traffic during the surge of the World Wide Web at the turn of the millennium. Current trends indicate this growth persists, primarily driven by the global deployment of 5G technology. Within the last two years alone, mobile network traffic has doubled. This is largely a result of increased video content consumption and the rise in mobile subscriptions [101]. Projections indicate that by 2028, 5G subscriptions will reach 5 billion, accounting for 55% of all mobile subscriptions.

The surge in mobile demand poses substantial challenges for mobile network operators, necessitating effective management of the initiation, modification, release, and relocation of network resources [102]. To address these challenges, there is an urgent need for data-driven algorithmic approaches that can analyse and predict mobile usage, specifically the demand for communication services from users. Simultaneously, the abundance of probe data collected by network operators, coupled with the richness of the associated spatio-temporal information, as discussed in Chapter 1, can significantly enhance our understanding of both communication and transportation demand.

This chapter explores the utilisation of mobile phone data from two closely related perspectives: firstly, understanding cellular service demand through analytical tools to effectively manage network resources, and secondly, investigating the potential of mobile phone data to enhance our understanding of travel demand. Due to the evolution of data collection technologies from mobile network providers, the research activities associated with the first perspective leveraged CDR data, while the second perspective focused on the use of new-generation passively collected mobile phone data, i.e., NSD.

During my postdoctoral research at the CITI-lab of INSA-Lyon, I developed unsupervised strategies to classify usages of aggregated data related to historical mobile phone traffic demand, specifically CDR. These insights are concisely showcased in the initial part of this chapter (Sec. 2.1). My investigations have shed light on the intricacies of mobile traffic demand and introduced methods for the sustainable roll-out of 5G telecommunication infrastructures. Such endeavours have formed the foundation for my subsequent research, predominantly centred on resilience modelling for urban transport networks.

The second part of this chapter pivots to the promising potential of mobile phone data, particularly NSD, in the field of transportation studies. The research discussed in Sec. 2.2, corresponding to the initial activities at the LICIT-ECO7 laboratory, had as objective to explore NSD to supplement traditional household travel surveys and provide a description of the expected mobility demand. Specifically, we studied the applicability of signalling data in generating OD matrices. This data was sourced from millions of anonymous mobile
phone users in the Rhône-Alpes region, France. Notably, unlike CDR data, signalling data
provides a broader spectrum of network-based records, ensuring superior spatio-temporal
granularity. The results presented in the second part of this chapter (Sec. 2.2) have been
the outcome of research collaborations with Orange Innovation and tutoring activity of one
PhD student (i.e., Mariem Fekih). The data used in the context of OD flow estimation were
made available by Orange Innovation in the context of Mariem Fekih's PhD thesis.

This chapter includes content from the following papers:

1. A. Furno, D. Naboulsi, R. Stanica, and M. Fiore, "Mobile demand profiling for cellular cognitive networking", IEEE Transactions on Mobile Computing, vol. 16, no. 3, pp. 772-786, 2016.

2. A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas", IEEE Transactions on Mobile Computing, vol. 16, no. 10, pp. 2682-2696, 2016.

3. A. Furno, M. Fiore, and R. Stanica, "Joint spatial and temporal classification of mobile traffic demands", in IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE, 2017, pp. 1-9.

4. M. Fekih, T. Bellemans, Z. Smoreda, P. Bonnel, A. Furno, and S. Galland, "A data-driven approach for origin-destination matrix construction from cellular network signalling data: a case study of Lyon region (France)", Transportation, vol. 48, pp. 1671-1702, 2021.

5. M. Fekih, L. Bonnetain, A. Furno, P. Bonnel, Z. Smoreda, S. Galland, and T. Bellemans, "Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data", Transportation Letters, vol. 14, no. 7, pp. 787-805, 2022.

## 2.1 Mobile Traffic Demand Profiling with Call Detail Records

This section describes the research aimed to improve the current understanding of the consumption of mobile network resources in space and time, and to show and describe the existence of correlations between mobile telephony data, the characteristics of human mobility and the socio-economic functions of urban environments. Orange, one of the most popular mobile operators in France, shared several months' worth of anonymised CDR for several French, Ivorian and Senegalese cities [103, 104]. Other data were made available for several Italian cities by Telecom Italia Mobile (TIM), one of Italy's leading operators in the sector. Such data were provided in the context of the TIM Big Data Challenge [105], to which our team has participated, being awarded as one of the top projects. The CDR used in this research described the total volume of mobile traffic in terms of the number of voice calls and messages exchanged between any two of the operator's base stations, aggregated on an hourly basis. It is interesting to note that this data is spatio-temporal: the information is provided with the latitude and longitude of the base station where the event was recorded, together with a timestamp indicating when a certain amount of traffic was observed.

### 2.1.1 Time Profiling of Mobile Traffic Demand

Based on previous work from the Urbanet team [7], we proposed in [6] a framework for the automated discovery of mobile demand profiles in large-scale cellular networks. The framework allows processing CDR for constructing sensible categories of the demand that
are associated to macroscopic spatio-temporal routines of the user population. As an interesting by-product, the framework can identify unusual behaviours in the demand, caused by events (e.g., sudden traffic surges, reduced overall communication activity, etc.) biasing the customary dynamics of a significant subscriber fraction. As further detailed next, such events are typically related to public celebrations, sport events, concerts, holiday breaks or strikes, showing the sensitivity of CDR to follow and, in some cases even anticipate, the unfolding of urban events that move masses of people within or outside the city.

In a first phase, named *the training stage*, the framework leverages basic data pre-processing techniques (i.e., data filtering and median aggregation) to extract hourly snapshots of *typical* user-generated mobile traffic over a city. Custom metrics of mobile traffic snapshot distance are employed to compute the similarity between pairs of snapshots by considering the geographical characteristics of the city. The framework makes use of hierarchical clustering with the average linking criterion to identify classes of related traffic snapshots. This hierarchical clustering outputs a whole family of solutions that can be represented as a dendrogram: it thus returns a richer information than a single-cluster set solution, as in the case of, e.g., *k*-means. However, this also implies that some criteria must be adopted to select the best clustering among all those in the family. To that purpose, a combination of well-known cluster analysis stopping rules determines the best number of clusters to retain. The approach is fully detailed in [6]. The training phase generates classes or schemes of typical network usages that can be used by a network operator to drive resource adaptation – via virtualisation solutions – in the mobile network. Moreover, such temporal classes have proven to possess a more general and social value, by showing insights on the regularities associated to human socio-economic activities, mobility patterns, and the different circadian rhythms that characterise the pulse of a city or a nation.

In its second phase, named *the classification stage*, the framework uses the previously identified schemes to classify the actual hourly CDR snapshots by relying on a modified implementation of the K-means clustering algorithm. Such step allows checking whether mobile traffic at given time belongs to the expected class and pinpointing anomalous situations otherwise.

The framework has been extensively evaluated over multiple CDR datasets collected from different world cities, including the data provided from Orange on Ivory Coast, Senegal and France, as well as the CDR data available for Italian cities from TIM. A few results are reported, as an example, in Fig. 2.1, for the case of a typical week of traffic in the city of Milan, Italy.

Fig. 2.1(a) graphically reports the network profiles identified by the framework during the training phase (i.e., clustering of the hourly snapshots from the median week). Such classes can be naturally mapped to activities taking place in dense urban areas during office time or weekend night-life (C1), weekend-residential usages (C2), early morning behaviours typically related to commuting (C0) and deep night with reduced communication activity (C3). Fig. 2.1(b) shows instead the results of the online usage of the framework (i.e., the classification stage). For each snapshot, the plot shows with a different colour the class it belongs to. Additionally, the plot includes two graphical representations for detecting outlying behaviours. First, snapshots that fall in a different category from the expected one are represented with big squares, surrounded by a black border (e.g., December 14th between 15:00 and 18:00. Second, snapshots classified in the expected cluster (from Fig. 2.1(a)) are represented with squares whose size is proportional to the normalised average distance from the expected class (i.e., the smaller the size, the more correct the classification). In the figure, most of the snapshots related to Christmas Day and other public holidays are classified as outliers in the C0 cluster, instead of C1. During such holidays, traffic activity is quite low in the city centre, with respect to typical working days, and mostly concentrated in transport and residential areas. This is explained by the fact that most of the business activities are closed and the presence of people in the urban area of Milan is significantly reduced with respect to typical days.
<figure><img src="image_12.png" /><figcaption>(a) Reference profiles obtained via clustering of median traffic 1-hour snapshots (on a weekly basis) and traffic distribution similarity. Each square represents one snapshot, and colours map to categories</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(b) Online usage of the framework and detection of outliers</figcaption></figure>

**Figure 2.1:** Mobile traffic profile categories for the CDR of the city of Milan.

Some other snapshots, which are very close in time to public holidays (e.g., Tuesday, December 24th or Monday, December 30th, 9:00 to 18:00), are classified in the C2 cluster instead of C1. They present a higher activity in the city centre and a quite regular traffic distribution in all the other zones and therefore clustered together with evening hours or weekends.

The framework is also capable of detecting special events that occurred in Milan in December 2013. For example, on December 7th afternoon, the “La Scala” theatre season opening traditionally takes place in the city centre of Milan. The same day is also the feast day of Saint Ambrose, the patron saint of Milan, with several public and religious events celebrated in the city centre. Similarly, on Sunday, December 22nd at 20:45, one of the most important football matches of the 2014-2015 season, i.e., Inter-Milan, was played in San Siro stadium, registering a record presence of 79,311 spectators.

It is worth highlighting that the described framework has been used as an input to subsequent research on 5G cognitive networking, specifically, on data-driven dynamic 5G-networks optimisation, in the context of a joint collaboration with CNAM Paris and the University of Milan [106]. Moreover, it has marked the initiation of a long-lasting collaboration with Orange France, which has proven crucial for the research results reported in Chapter 3.
2.1.2 Spatial Profiling of Mobile Traffic Demand

The research experience outlined in the previous section has been pivotal in consolidating my
interest in large-scale data processing and sparked my curiosity to investigate further mobile
phone data towards automatic detection of recurrent patterns and anomalies of human
presence and activities in urban environments.

It is well known that individuals exhibit highly repetitive yet distinctive mobility and activity patterns influenced by factors such as family life, work obligations, hobbies, occupations, and personal habits, as well as the availability of infrastructures, services, and amenities. This naturally applies to mobile network subscribers and their communication patterns as well. The uniform yet varied behaviour of mobile users results in heterogeneity in subscribers' profiles [107], periodic temporal demand [7], load fluctuations during large-scale social events [108], and geographic diversity in mobile communications [109].

The next stage of my postdoctoral research was thus focused around these research questions: can CDR data be leveraged to automatically infer the multiple urban fabrics of a city? Is it possible to observe the same patterns from CDR in multiple cities or even countries? Urban fabrics relate to the combination of infrastructures, such as transportation and telecommunication systems, sports centres, educational institutions, and healthcare facilities, and land use, including residential, industrial, touristic and commercial areas, that characterise different zones within a metropolitan area. Previous research has confirmed significant correlations between mobile demand and city cartography, including the diversity of mobile activity within urban areas [110], high similarity in the temporal dynamics of traffic in residential zones [111], as well as the existence of relevant shifts in communication load peaks between different areas of the city throughout the day and during weekday-to-weekend transitions [112]. Furthermore, during the time the activities described in this section were undertaken, the use of mobile phone data, and particularly CDR, was gaining momentum as a means of validating theories on creating liveable cities in urban planning [113].

With these findings in mind and the experience of my latest postdoctoral research,
I decided to further explore the spatial heterogeneity of mobile communication activities
and investigate unsupervised solutions to automatically identify relevant partitions of large
metropolitan areas from CDR. The central hypothesis was that the identified zones, distin-
guished by homogeneous patterns of mobile traffic usage, would exhibit equivalent or highly
similar urban fabrics. Another challenge consisted in defining a novel quantitative descrip-
tion of urban fabrics based on the identified regularities (i.e., a signature) of the observed
mobile traffic demand in the associated zones. Signatures could be helpful to unambiguously
and informatively describe the socio-economic activities in the identified areas, even in the
presence of mixed usages.

This research resulted in the development of a second framework built upon the concept
of mobile phone traffic signatures for the automatic identification of urban fabrics from
CDR. The framework is succinctly outlined below, along with some noteworthy outcomes.
The interested reader can refer to [9] for complete details.

The framework assumes the availability of a generic dataset $\mathcal{D}$ describing the communication activity of a mobile subscriber population during a set of days $\boldsymbol{\delta} = \{d\}$. For each day, the mobile demand is assumed to be known as the aggregate volume of the traffic generated by all users in the same area during a given time interval; the size of the area and duration of the interval determines the spatial and temporal granularity of the dataset, respectively. These assumptions are typically satisfied when working with traditional CDR data and their more recent extensions (e.g., NSD). We name unit area the spatial aggregation level: the whole geographic region under consideration $\mathbf{a} = \{a\}$ is thus divided<fnref n="1" /> into unit areas $a$. The time granularity is instead characterised by the duration of a time slot, i.e., the interval during which user activity is aggregated in each unit area. Each day $d \in \boldsymbol{\delta}$ is thus split into

<footnote n="1">¹The definition of unit area is general and can accommodate any tessellation of space. Unit areas can map to, e.g., cell sector boundaries, coverage zones of base stations, Voronoi cells, or elements of a grid.</footnote>
a set $t = \{t\}$ of time slots $t$. Overall, $\mathcal{D} = \{v_a(d, t)\}$, where every element $v_a(d, t)$ describes the total mobile communication activity observed within each unit area $a$ at time slot $t$ of day $d$. From such data, the framework includes two main stages aimed to: (i) building a representative profile of the typical activity taking place in each unit area of the region of interest; (ii) clustering unit areas with similar profiles for urban fabric detection.

Concerning the first phase, the raw traffic volumes of each unit area $a$ are transformed into the Median Week Signature (MWS) of the unit area. For the formal definition of the MWS of the generic unit area $a$, let us first introduce the notion of weekly support $\delta$, corresponding to the time span of one week, i.e., $\delta = \{\text{MON}, \text{TUE}, \text{WED}, \text{THU}, \text{FRI}, \text{SAT}, \text{SUN}\}$. Let us also denote as $\delta^\delta \subset \delta$ the set of days in the dataset $\mathcal{D}$ that correspond to the day of the week $\delta$, with $\cup_{\delta \in \delta} \delta^\delta = \delta$. For instance, $\delta^{\text{MON}}$ groups all Mondays in the dataset. Then, the generic element in the MWS of unit area $a$ is defined as:

$$s_a(\delta, t) = \mu_{1/2}(\{v_a(d, t) | d \in \delta^\delta\}), \quad \forall a \in \mathbf{a}, \qquad (2.1)$$

for time slots $t$ during day of the week $\delta$, and where $\mu_{1/2}(\cdot)$ represents the median of the set within parenthesis. The complete MWS is then defined as the concatenation of time-ordered samples on the temporal support $\delta$ of area $a$, or, formally:

$$s_a = \|_{\delta \in \delta} (\|_{t \in t} s_a(\delta, t)), \quad \forall a \in \mathbf{a}. \qquad (2.2)$$

where the $\|\|$ operator indicates the time-ordered concatenation of all elements in a set.

The second stage of the framework builds on the clustering approach described in Sec. 2.1.1 using the same linkage clustering algorithm with the average distance criterion. A distance metric based on the Pearson correlation coefficient is used to compute the dis-similarity between pairs of MWS related to different unit areas, i.e.:

$$\Delta_{a,a'} = 1 - C_{a,a'}, \quad \forall a, a' \in \mathbf{a}. \qquad (2.3)$$

with $C_{a,a'}$ corresponding to the Pearson correlation coefficient of $s_a$ and $s_a'$. The skewness of the cluster sizes is evaluated at the different levels of the dendrogram built by the hierarchical clustering to select the number of clusters to retain: selecting the level with minimum skewness allows grouping unit area signatures into classes of relatively comparable sizes.

The assessment of the framework has been carried out on a comprehensive collection of datasets, including the CDR supplied by Orange France for six French cities and by TIM for four Italian cities over several months and years. This represents a much larger dataset than those ever used in previous studies, enabling us to generalise our findings and examine similarities and differences across a significant number of diverse cities. Moreover, the comparative evaluation reported in [114] has shown that our solution outperforms the related work [115, 116, 117], by identifying mobile demand profiles that better agree with reference data.

In Fig. 2.2, we report three major classes ($c_0$, $c_1$ and $c_3$) identified by our framework from the CDR records of the ten analysed cities. The characteristic signature $s_0^*$ of class $c_0^2$ is portrayed in Fig. 2.2(a). The comparison with Points of Interest (PoIs) and land use data from OpenStreetMap (OSM)<fnref n="3" /> highlights the absence of any noticeable infrastructure and a dominant residential land use in the areas associated to cluster $c_0$, only related to Italian cities. This is outlined, in Fig. 2.2(b), Fig. 2.2(c) and Fig. 2.2(d), which show the extent of unit areas in Milan, Turin and Rome whose signatures are in class $c_0$. The corresponding regions include suburban and mainly residential zones of these cities and exclude city centres and well-known PoIs of the cities. Class $c_0$ can be thus associated with residential urban fabrics in Italy, often denoted by a mixture of private housing and small business activity.

<footnote n="2">²The characteristic signature of a cluster is computed as the average of all MWS signatures of unit areas belonging to the given cluster.</footnote>

<footnote n="3">³https://www.openstreetmap.org</footnote>
<figure><img src="image_14.png" /><figcaption>**Figure 2.2:** Residential urban fabrics. Characteristic signatures (with standard deviation) and maps of related unit areas in representative city scenarios.</figcaption></figure>

The characteristic signature $s_0^*$ in Fig. 2.2(a) exhibits two comparable traffic peaks, at 11:00 and 17:00, repeating on all working days. The mobile activity in most urban areas in Italy is comparatively reduced during the weekend (but higher on Saturdays) with a morning peak higher than the afternoon one, which is also shifted towards later hours.

A similar discussion holds in the case of signatures classes $c_1$ and $c_3$, this time related to French cities only. These classes designate residential urban fabrics in France, as exemplified by the geographic coverage of the associated unit areas in Lyon and Marseille, shown in Fig. 2.2(g) and Fig. 2.2(h), respectively. Concerning the characteristic signatures $s_1^*$ and $s_3^*$, in Fig. 2.2(e) and Fig. 2.2(f), respectively, relevant similarities emerge in the semantics of the two signatures. Both feature two traffic peaks, the afternoon one standing over the morning one; the activity during weekends is comparable in the two cases, just scaled up in $s_3^*$. The main difference between $s_1^*$ and $s_3^*$ appears thus to be the afternoon-to-morning peak ratio, higher in the latter. We conclude that both $c_1$ and $c_3$ are representative of residential and small business areas in France, although $c_3$ is associated with a higher concentration of residential land use than $c_1$: indeed, the darker unit areas in Fig. 2.2(g) and Fig. 2.2(h), mapping to $c_3$, are more present in the urban outskirts and less so in city centres.

Comparing $c_0$, $c_1$, and $c_3$ is also intriguing. The disparities between the baseline profiles of mobile traffic demand in Italy and France are remarkable. The activity peaks are uneven and shifted by approximately one hour in France, and the ratio is even inverted during weekends. This diversity can be attributed to diverse routines in the two countries, raising interesting sociological questions.

Fig. 2.3 presents the characteristic signatures and maps associated with another set of major clusters identified by our framework, i.e., $c_2$, $c_5$ and $c_7$. Interestingly, this time the clusters are not separated between nations, with unit areas from both France and Italy appearing in each of them. We relied again on OSM data as a source of information to understand the kind of urban fabrics associated with the detected clusters. When superposing the urban surface covered by unit areas associated with $c_2$ to OSM data, we notice a good match with locations mostly related to office-hour work activities, highlighted with blue-coloured polygons in the pictures$^4$. Maps of exemplar case studies are provided in Fig. 2.3(b), Fig. 2.3(d), and Fig. 2.3(f) for Turin, Milan, and Paris, respectively. The anal-

<footnote>$^4$Relevant Pols are also marked with letters in the figures. The interested reader can refer to [9] for an exhaustive description of this Pols.</footnote>
<figure><img src="image_15.png" /><figcaption>(a) Signature $\mathbf{s}_2^*$</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(c) Signature $\mathbf{s}_5^*$</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(e) Signature $\mathbf{s}_7^*$</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(b) Cluster $c_2$, Turin 2015</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>(d) Clusters $c_2$ and $c_5$, Milan 2015</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>(f) Clusters $c_2, c_5$ and $c_7$, Paris 2015</figcaption></figure>

**Figure 2.3:** Office fabric signatures $\mathbf{s}_2^*, \mathbf{s}_5^*$ and $\mathbf{s}_7^*$ and maps of the related unit areas in Italian and French cities, with OpenStreetMap data.

ysis of the related signatures ($\mathbf{s}_2^*, \mathbf{s}_5^*$ and $\mathbf{s}_6^*$) indicates a fairly constant and comparatively higher activity during office hours, especially during morning time, and drastically reduced during evening time, early morning and weekends, when a very small fraction of offices is open. Based on these considerations, it appears reasonable to consider these clusters as representative of *office urban fabrics*, i.e., urban areas featuring socio-economic activities related to development, commercialisation and fruition of services and goods, with a typical European working time during weekdays, 9:00 - 18:00.

Finally, in Fig. 2.4, we report clusters that have shown a strong connection to *transportation urban fabrics*. This is, for instance, the case of cluster $c_4$ (Fig. 2.4(b)): this class
<figure><img src="image_21.png" /><figcaption>(a) Signature $\mathbf{s}_{4}^{*}$</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(b) Cluster $c_{4}$, Paris 2015</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(c) Signature $\mathbf{s}_{36}^{*}$</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>(d) Clusters $c_{9}$, $c_{10}$, other classes, Paris 2015</figcaption></figure>

**Figure 2.4:** Transportation fabric signatures $\mathbf{s}_{9}^{*}$, $\mathbf{s}_{10}^{*}$ and $\mathbf{s}_{36}^{*}$, and their maps for French and Italian cities, with OpenStreetMap data.

regroups unit areas with a very peculiar signature with two major peaks (Fig. 2.4(a)), clearly related to commuting, and a minor peak at noon, probably related to lunch activities around offices and schools. The analysis of PoIs retrieved from OSM indicated a striking match with subway stations of the city of Paris, reported in the figure as black dots. Similarly, the other clusters reported in Fig. 2.4(d) have an almost perfect match with train stations and appear associated with long-range commuting behaviours.

The aforementioned approach has been further refined, at the beginning of my activity in the LICIT-ECO7 research laboratory, to achieve finer spatial granularity by means of an original approach based on multi-source data, by fusing CDR and GPS taxi data. In [118], we proposed an original stage-based fusion technique, in which the clustering approach proposed for mobile phone data signatures is first applied to CDR to achieve a coarse-grained classification of the city. Then, inside each retrieved cluster, the same technique is re-applied over signatures built from taxi pick-ups and drop-offs, as obtained from the GPS data. Such a combined use of mobile phone data and GPS traces has outperformed previous approaches when confronted with ground-truth information and allows characterising land use in greater detail.

### 2.1.3 Spatio-temporal Profiling of Mobile Traffic Demand

Both the framework for mobile traffic temporal classification and the approach for urban fabric detection focus on one single dimension (either the temporal or the spatial one) to discover hidden structures in mobile phone data, by means of clustering. In the last part
of my postdoc, I focused on devising a novel solution for concurrently inspecting both the space and the time dimensions, which is a much more challenging problem. Also, clustering is traditionally limited by the fact that each element may belong to only one group, while there can be situations where an object has mixed behaviours and should be, therefore, partially associated with multiple classes. Such situations are not detectable with simple clustering-based techniques.

Therefore, I've explored the application to mobile phone data of a data reduction technique, known as Exploratory Factor Analysis (EFA). EFA is a well-established instrument in psychology research [119], but it was first applied to mobile phone data mining by our research group [10]. EFA aims at identifying, in a fully automated way, latent factors that cause the dynamics observed in the data. More precisely, given a set of random variables ($X$) observed over a large population of samples, a reduced number of dimensions, or latent common factors ($F$), is assumed for decomposing the vector of observed variables into the product of the hidden factors ($F$) and a matrix of loadings ($\Lambda$). The loadings describe the importance of each observed variable with respect to the assumed factors.

When tailored to the specific use case of mobile traffic classification, EFA offers the possibility of exploring the space and time dimensions of the data at once, depending on how the observed variables are selected. This yields two significant advantages. First, the same methodology can be cast to recognise factors that are temporal or spatial in nature, solving the two problems of network activity temporal profiling (time intervals modelled as the EFA variables) and urban fabric detection (base stations modelled as the EFA variables). Second, our proposed EFA methodology allows immediate extrapolation of the structures hidden in the secondary dimension of both problems above. In other words, it provides, at no additional cost, knowledge of the spatial patterns that characterise each network activity profile, and of the precise temporal dynamics that distinguish each land use. This plays an important role in the interpretation of classification results.

<figure><img src="image_25.png" /><figcaption>**Figure 2.5:** Land use detection. (a)-(d) EFA of the total communication activity (sum of incoming/outgoing calls and SMS) in the Orange-2014 dataset. Loadings of the 1596 Voronoi cells (i.e., EFA variables) on four (out of fourteen) representative classes (i.e., EFA factors). (e)-(h) Thurstone's scores of the 91×24 hours (i.e., EFA samples) on a selection of the 16 classes (i.e., EFA factors). Figure best viewed in colours.</figcaption></figure>

In Fig. 2.5, we report the results obtained via EFA (cast for solving the urban fabrics detection problem) when classifying the Orange data available for the city of Paris. First, the approach shows results that are comparable to our previous results on urban fabric classification in terms of spatial partitioning (compare Fig. 2.5(a):(e) with Fig. 2.3(f), Fig 2.4(b) and
Fig 2.4(d)). Second, the methodology automatically outputs the temporal classification (in Fig. 2.5(e):(h)), which provides insights for understanding how the factors are characterised with respect to time. Finally, the methodology allows the identification of areas exhibiting mixed usages, i.e., with high loadings on multiple factors.

## 2.2 Origin-Destination Flows at Regional Scale via Network Signalling Data

Spatio-temporal data are extremely valuable for studying human mobility for transportation and urban planning purposes [120, 121]. At large scales (e.g., regional levels), traditional approaches rely on household travel surveys to collect mobility data, typically recording one day of travel diaries per household. However, surveys are increasingly confronted with issues during the sample construction phase [18], declining response rates [122], and unreported trips [123], all of which reduce the quality of the mobility indicators derived from them. Additionally, travel surveys typically involve high costs that limit their frequency (once or twice per decade) and prevent tracking the dynamics of population mobility over time. While travel surveys still provide highly useful data for constructing behavioural transport models (e.g., route and transportation mode choice models) and coarse estimations of people's mobility in a city, they are less suitable for deriving reliable and time-varying mobility indicators due to the aforementioned issues. This is particularly true for OD matrices, whose accuracy and completeness can be severely compromised by the quality of the household surveys used.

Data collected from cell phones have become one of the most important new data sources to study travel behaviour [124]. Their attributes, such as large coverage of the geographic area, significant penetration in population and highly detailed location information, have attracted researchers to analyse them to support transportation studies. Several researches have been conducted to use different types of mobile phone data (e.g., CDR, cellular network data); but, few have attempted to validate the results with external sources due to the different nature of mobile phone footprints. Yet, the validation process allows to identify possible biases and to gain a clearer idea of their potential. Moreover, the quality and accuracy of data is essential to ensure that investment or transport policy decisions are based on reliable analyses. Therefore, considerable efforts are needed to pre-process mobile phone data and to validate the related research outputs.

We pioneered the exploration of NSD for the accurate estimation of both static and time-varying OD matrices. We worked on a small (and limited) test dataset, related to one typical working day from 2017 (Thursday, June 1st 3:00 to Friday, June 2nd 3:00) provided by Orange Innovation. This work was performed in the context of Mariem Fekih's PhD thesis, in collaboration with the LAET laboratory of ENTPE and CNRS, the Hasselt University, Belgium, and the University of Bourgogne Franche-Comté, France. The primary objective of this study was to assess the capability of NSD to capture the major spatio-temporal mobility patterns of a mobile-device-equipped population at a large scale, by producing realistic static regional OD matrices. An additional objective was to validate the consistency of the reconstructed matrices with the ones obtained from household travel surveys, used as a reference. It was paramount to confirm this hypothesis before exploiting massive NSD for reconstructing mobility information at much finer spatio-temporal granularity based on the inference of individual GPS-like trajectories of human mobility (Chapter 3).

### 2.2.1 Static OD Matrix Estimation

To achieve the aforementioned objectives, we developed a simple methodological chain aimed to derive OD matrix via pre-processing of raw NSD data, and performed an extensive validation of the obtained results using the conventional OD matrix generated from a local travel
<figure><img src="image_26.png" /><figcaption>(a) Location of the cell towers and their Voronoi tessellation</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>(b) The 77 sectors of the EDR (labelled with unique numerical identifiers) and their aggregation in 14 macro-zones (represented with different colours)</figcaption></figure>

**Figure 2.6:** Cell tower locations (a), and (b) EDR sectors in the region of interest (Rhône-Alpes).

survey related to the region of Rhône-Alpes, France. The explored NSD dataset consisted of 2G and 3G individual signalling records<fnref n="5" /> of over two million anonymous mobile phone users in June 2017, totalling about 300 million records of device transactions. Concerning the spatial dimension, this dataset covered the whole extent of the Rhône-Alpes region in France, with 2,230 cell towers in the study area, and each cell tower possibly handling several antennas.

Fig. 2.6(a) presents the cellular network coverage within the Rhône-Alpes region and the aggregation in 3G Location Areas. The Rhône-Alpes household travel survey, called Enquête Déplacements Régionale (Regional Travel Survey) (EDR) 2015, contained data from 37,450 individuals aged over 11 years, identifying 143,000 trips. The sample was constructed using geographical stratified random sampling of the population of 77 zones in the region, graphically reported in Fig. 2.6(b). These 77 zones were spatially aggregated into 14 macro-zones represented in Fig. 2.6(b) with different colours. The survey contains socio-demographic characteristics and trip information, including transport mode, start and end time, activity at origin and destination, and location. Lyon is the largest metropolitan area in the region, and the survey only refers to working day trips during late autumn to early spring.

## The Framework: Basic Definitions and Main Steps

The proposed framework, graphically shown in Fig. 2.7 and extensively described in [125], aggregates and transforms individual mobile phone signalling data (also called NSD user trajectory) into an OD flow matrix, formally denoted as $M = \{\delta(z_i, z_j) \mid (z_i, z_j) \in Z \times Z\}$, with Z representing the set of 14 macro-zones, and $\delta(z_i, z_j)$ being the expanded daily count

<footnote n="5">⁵It is worth underlining that this first test release of NSD did not include any 4G communication data, which as from 2019, constitutes more than 50% of the worldwide mobile telecommunication technologies market. As detailed in Chapter 1, the absence of 4G data represents a substantial limitation in terms of richness of spatio-temporal information, thus further explaining the rather coarse grain of the results obtained in this preliminary study.</footnote>
<figure><img src="image_28.png" /><figcaption>**Figure 2.7:** The OD matrix reconstruction framework.</figcaption></figure>

of trips estimated via NSD between zone $z_i$ and zone $z_j$^6. The framework operates according
to the following main steps:

1. Filtering of (mobile) robot devices and identification of the most frequently observed cell tower to detect the *home location* of each user device^7 via time-based heuristics;

2. Identification of target resident users based on a condition of minimum required mobile phone activity (e.g., each resident user should have a mobile event at least every 3 hours);

3. Estimation of expansion factors for each zone $z \in Z$ to scale the detected population of target resident users to the full population of travellers in the region of interest;

4. Trip detection and scaling up of the detected trips according to the estimated expansion factors and reconstruction of the final OD matrix $\mathcal{M}$.

It is worth remarking that the approach solely leverages NSD for user filtering and trip extraction, while it depends on travel survey and census data in relation to zoning (for spatial aggregation) and trip scaling (for determining the set of expansion factors), respectively.

The adopted definition of trips for NSD is based on the standard for travel survey introduced by CEREMA (the French national agency for transport network and urban planning) [126], which is at the core of the considered EDR survey. A trip is defined as any movement between pairs of stationary activities, each performed at a given zone. Thus, for a given user *u*, the whole set of individual NSD trajectories is scanned to identify consecutive observations on the mobile network of user *u* at the same zone for a minimum time threshold $\tau$. For the choice of $\tau$, we have considered multiple values (e.g., 20, 30, 60 minutes) by

<footnote>^6We denote that in this work we did not take into account intra-zonal travel demand (i.e., $z_i \neq z_j$ in the equation above). This restraining hypothesis was removed in the work described in Chapter 3.</footnote>

<footnote>^7The framework assumes that a device corresponds to a single user. The two terms are used interchangeably in the following.</footnote>
<figure><img src="image_29.png" /><figcaption>(a) The NSD reconstructed OD matrix. The matrix has been obtained with a threshold τ of 30 minutes for trip detection and aggregated at the level of the 14 macro-zones</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>(b) The EDR OD matrix (on 14 macro-zones)</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>(c) The regression line has the following equation with τ=30 minutes for trip extraction: $y_{ij} = 0.70 \cdot x_{ij} + 2193$, with $y_{ij}$ representing the OD travel demand as seen via NSD data between zones $z_i$ and $z_j$, and $x_{ij}$ representing the EDR-based travel demand between the same pair of zones. The $R^2$ coefficient equals 0.96</figcaption></figure>

**Figure 2.8:** Graphical comparison between the NSD-reconstructed OD matrix (a) and the EDR OD matrix (b). (c) Linear regression of the NSD OD matrix with respect to the EDR one.

taking into account the size of the zones (average area of EDR sector is 582 km²). Based on such approach, each NSD user trajectory is ultimately converted into an ordered sequence of stationary activities ($A_1, A_2, ..., A_k$), each associated to a given zone. The converted user trajectory can thus be parsed to extract $k-1$ trips, each defined as a tuple $(u, O_i, D_i)$ where $u$ denotes the given user, $O_i$ the zone of the generic stationary activity $A_i$ and $D_i$ the zone associated to the following stationary activity $A_{i+1}$, being $O_i$ and $D_i$ the origin and the destination zones of the $i^{th}$ trip of user $u$.

Expansion factors have been determined on a per zone basis, by considering population data from census data provided by the INSEE (Institut national de la statistique et des études économiques), aggregated at the level of the 77 sectors of the EDR survey. The expansion factor $\epsilon(z)$ for the generic zone $z$ is calculated according to the formula: $\epsilon(z) = \frac{P(z)}{R(z)}$, where $P(z)$ and $R(z)$ represent the amount of population older than 11 in zone $z$ from census data and the total amount of resident users detected via mobile phone data in the same zone, respectively. Expansion factors are then associated to users based on the detected home location, i.e., $\epsilon(u) = \epsilon(z) | z = h(u)$, where $h(u)$ denotes the function that associates a home sector to user $u$. Finally, the static OD matrix $M$ is computed per pair of origin and destination zones $(z_i, z_j)$: the expanded trips between the given pair are summed up over the whole user basis, i.e., $\delta(z_i, z_j) = \sum_{u \in U} \epsilon(u) \cdot n_u(z_i, z_j)$, with $n_u(z_i, z_j)$ representing the total number of trips detected for user $u$ between zones $z_i$ and $z_j$.

The main outcome of the framework, i.e., the OD matrix $M$, is visualised in Fig. 2.8(a) together with the OD matrix corresponding to the EDR survey, shown in Fig. 2.8(b).

The visual comparison of the distribution of OD trips from mobile phone and EDR data shows very similar shapes even though the total numbers of trips are different. The resemblance of the two matrices has been quantitatively confirmed by considering the Spearman's rank correlation and the $R^2$ coefficient of determination for a linear regression of the NSD-reconstructed matrix as a function of the EDR one, at macro-zone level (i.e., 14 macro-zones). We obtained a satisfying result of $\rho = 0.95$ with ($p < 0.0001$) and an $R^2$ coefficient of 0.96, which provides a high-confidence indication that the distributions of OD flows are similar. Hence, although both signalling data and survey-based matrices are collected using different techniques and technologies, and despite the limitation of the test dataset used for this study (e.g., missing 4G data, one-day-only), they appear to resemble well, at least at the level of the 14 identified macro-zones, thus demonstrating the feasibility of a large-scale
Origin-Destination survey based on alternative passive real-world data, and motivating the research on the topic detailed in the next sections.

### 2.2.2 Dynamic OD Matrix Estimation

The preliminary study described in the previous section encouraged a deeper exploration into the use of NSD for OD matrices reconstruction by focusing on the challenge of using mobile phone data to generate time-dependent travel demand matrices.

Understanding the dynamics of human mobility patterns is a core notion in transportation studies related to, e.g., traffic congestion management and transport infrastructure planning [127]. However, traditional household travel surveys typically record only one or a minimal number of days of travel diaries per household. Moreover, the high costs of conducting travel surveys restrict their frequency and prevent from following the mobility dynamics. In this respect, they do not allow considering fine-grained and continual temporal analysis of, e.g., the hourly, weekly or special-events related variability of trips [128].

Communication data have instead the potential to describe mobility trends over long periods and with various time resolution scales, due to the ubiquitous and continuous use of mobile devices by the moving population. Hence, it appears reasonable to leverage mobile phone data to generate dynamic origin-destination flows by assigning the inferred trips into target time windows. Some studies have been conducted along this line to extract dynamic trip metrics using different forms of mobile phone data. Most of these studies have explored CDR and developed techniques to figure out the temporal distribution of user trips in limited geographical areas. However, it has been shown [129, 130] that these methods perform rather poorly, especially in urban zones, due to the very low spatio-temporal resolution of CDR data.

To answer the challenge of reconstructing dynamic OD flow at large-scale, we enhanced the workflow described in Sec. 2.2.1 for NSD. Specifically, we included a novel temporal component for estimating a start-time reference denoting the likely beginning of each observed trip.

#### The Framework: Basic Definitions and Main Steps

The extended framework is reported in Fig. 2.9(a), together with a schematic representation of the approach introduced to infer the trip start time (Fig. 2.9(b)). In the following, we focus on the description of the temporal component, while the full approach, largely based on the framework described in Sec. 2.2.1, is detailed in [131].

First, let us recall that in the approach of Sec. 2.2.1, each user's trip is reconstructed from NSD as an ordered sequence of stationary activities, i.e., ($A_1, A_2, ..., A_k$). In this representation, no intermediate point (i.e., way-point) between consecutive stationary activities is preserved from the original NSD user trace. This was a deliberate choice due to the reported limitation of the dataset (no 4G data). Moreover, this approach did not account for a proper characterisation of the positive bias existing between the last observed event of a stationary activity and the actual beginning of the trip. The problem of quantifying and removing the bias on trip start (and end) time will be discussed in detail (see Sec. 3.1) in the work [132].

In the framework, the trip taking place between two consecutive stationary activities ($A_i, A_{i+1}$) is supposed to start at any specific time instant between the timestamp $t_i$ of the last observed mobile network event associated to activity $A_i$ and the timestamp $t_{i+1}$ of the first mobile network event associated to activity $A_{i+1}$. The absence of any way-point between the two activities does not allow, in fact, any further assumption on the moment the trip has started, apart from considering that the start time is uniformly distributed in the time window $[t_i, t_{i+1}]$. An enhanced approach relying on way-points will be described in Sec. 3.1. This procedure allows removing the biases on trip start and arrival times and
<figure><img src="image_32.png" /><figcaption>**Figure 2.9:** Reconstruction of time-dependent OD matrices: (a) the extended framework; (b) The approach for imputing trip starting time.</figcaption></figure>

makes possible a more robust estimation of the trip features. In the restrictive settings defined above, we were instead compelled to define the start time of a trip as the midpoint of the time interval separating the two activities, specifically $\frac{t_i+t_{i+1}}{2}$, which corresponds to the mean of the uniform distribution. Based on this approach, the trip can be finally represented as the tuple $(u, t_i, O_i, D_i)$ where $u$ denotes the given user, $t_i$ the estimated start time of the trip, $O_i$ the zone of the generic stationary activity $A_i$ and $D_i$ the zone associated to the following stationary activity $A_{i+1}$, being $O_i$ and $D_i$ the origin and the destination zones of the $i^{th}$ trip of user $u$.

Thus, the final dynamic OD matrix can be defined as a temporally-sorted sequence of timestamped OD matrices $(\mathcal{M}(t) | t \in T)$, with $T$ the ordered set of time slots considered within a typical day (e.g., 24 hourly time slots) and the generic timestamped OD matrix $\mathcal{M}(t) = \{\delta(z_i, z_j, t) | (z_i, z_j) \in Z \times Z\}$. In the previous equation, $\delta(z_i, z_j, t)$ represents the number of expanded trips observed between zones $z_i$ and $z_j$, i.e., $\sum_{u \in U} \epsilon(u) \cdot n_u(z_i, z_j, t)$, with $n_u(z_i, z_j, t)$ the total number of trips detected for user $u$ between zones $z_i$ and $z_j$ with start time within time slot $t$.

## Empirical Evaluation

As a case study of the extended framework, we applied the methodology to the same NSD records of Sec. 2.2.1 related to the city of Lyon, France (i.e., from 1st June 3:00 to June 2nd, 2017 3:00). As a comparative source of data, we used again the regional travel survey EDR 2015 conducted in the Rhône-Alpes region. The dynamic OD matrix was reconstructed at the spatial resolution of the 77 EDR sectors.

The analysis of the reconstructed dynamic OD matrix $\mathcal{M}(t)$ unveils interesting insights about the potential and the limitations of NSD for dynamic travel demand reconstruction.

The hourly total travel demand profile (i.e., the number of hourly trips generated from all zones) is reported in Fig. 2.10(a) for both signalling and survey data. The total demand observed from signalling data (i.e., SD) appears to be lower than the one reported in the
<figure><img src="image_33.png" /><figcaption>**Figure 2.10:** (a) Temporal demand profile from signalling data (SD) and survey data (EDR) and demand difference between EDR and SD (red bars indicate an hourly EDR demand higher than the SD one, and blue bars otherwise) (b) correlation between the hourly demand estimations from SD and EDR.</figcaption></figure>

survey (i.e., EDR), with less sharp morning and afternoon peaks, as clearly shown by the hourly difference ($M_{EDR} - M_{SD}$) reported in the bottom part of the same figure. This result could be explained by the existence of a large fraction of users in the mobile phone data, referred to as *static people* in the following, for whom it is possible to detect the home sector, but no trip can be observed, as the associated stationary activities produced are all (or mostly) located at the home sector. The proportion of such static people in our mobile phone dataset amounts to 46%. Travel demand reconstructed via NSD is unavoidably correlated to the number of events generated by resident users. Therefore, even though a certain portion of the identified static users could actually be stationary (e.g., elderly people), it appears highly likely that another large portion of them is wrongly seen as static. The framework fails in associating trips to such sub-population, probably due to their very low mobile phone activity (e.g., during morning hours). Such reduced device usage inevitably leads to an underestimation of the travel demand, which appears to be especially important during morning time, and requires a proper bias removal procedure. However, despite this underestimation, the hourly global demand profiles estimated from both data sources are highly correlated (Pearson coefficient equal to 0.898), as shown in Figure 2.10(b). This confirms that signalling data can provide a travel profile comparable to the well-known typical demand profile for a working day.

The analysis of the differences between dynamic OD matrices generated via signalling and survey data was performed not only from a temporal perspective but also from a geographical point of view. Fig. 2.11(a) shows the number of trips emitted from each sector of the study area, over the 24-hours reference period (i.e., trips are counted with respect to the sector location of the trip's origin and the related start time). The heatmap provides a spatio-temporal representation of the absolute difference between the demand generated by each zone from the survey and mobile phone data. Sectors are sorted from left to right by decreasing density of urban land use, as retrieved from the CORINE Land Cover 2012⁸ dataset. In highly-dense urban zones (on the left of Fig. 2.11(a)), the demand is higher with EDR survey compared to that reconstructed via mobile phone, regardless of the hour of the day. On the other hand, the hourly travel demand during the morning peak period is higher

<footnote n="8">⁸https://www.statistiques.developpement-durable.gouv.fr/corne-land-cover-0</footnote>
<figure><img src="image_34.png" /><figcaption>(a) Spatio-temporal distribution of the emitted demand difference between survey and signalling data</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(b) Correlation between the emitted demand difference and urban land use percentage per sector</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(c) Hourly demand distribution of EDR and Network Signalling Data (SD) after application of the de-biasing procedure</figcaption></figure>

**Figure 2.11:** Analysis and correction of the biases in temporal NSD reconstructed travel demand.

in the survey compared to mobile phone regardless of the zone. This is confirmed by the plot reported in Fig. 2.11(b) showing that the emitted demand difference between survey and mobile phone is abnormally highly-correlated to the urban density of the sectors. In rural areas, we can reasonably assume that signalling records provide more consistent estimations since long-distance trips from/to these areas are typically better captured with mobile phone passive data than surveys and for a larger sample of the population [133]. Instead, in urban areas, the figures let us conclude that the proposed trip extraction method is unable to capture short-distance trips, which are expected to occur with higher frequency in urban areas than in rural ones. These preliminary analyses confirmed the existence of systematic spatio-temporal bias in the data.

Two simple heuristics were proposed to cope with these biases and used to correct the NSD-estimated travel demand. Concerning the geographical bias, we computed a spatial correction (multiplicative) factor, which was applied to the NSD-estimated travel demand on a per-zone basis. The correction factor was calculated using the following regression equation:

$$D_{Survey}(x) - D_{SD}(x) = 94835 \cdot U_{la}(x) - 7395 \quad (2.4)$$

The regression equation was obtained by fitting a linear equation to the data visualised in Fig. 2.11(b), and expresses the demand difference between survey demand and NSD-estimated demand as a function of the urban land use density. The correction factor is therefore derived from Eq. 2.4 to remove the dependence on urban density from the differ-
ence. The factor is defined as follows:

$$S(x) = 1 + \frac{94835 \cdot U_{la}(x)}{D_{SD}(x)} \qquad (2.5)$$

where $U_{la}(x)$ and $D_{SD}(x)$ represent, respectively, the urban density (as computed from land use data) and the emitted demand associated to each zone $x$ (as estimated from the signalling data). By applying this correction factor, we can decorrelate the difference with respect to urban density.

Concerning the temporal bias, we have noticed that the observed underestimation (i.e., between 5 and 9 am) of the travel demand also affects the temporal distribution of Location Area Update (LAU) events (i.e., a kind of communication passively generated by the device when changing the Location Area zone), which should not be normally affected by the time of the day. Therefore, to address this bias, a uniform correction factor has been applied on all mobile phone-based trips with a start time estimated during and around the morning peak period [5–9 am]. This factor has been calculated as the ratio of the afternoon and morning peaks in the LAU profile. Based on these considerations, the applied temporal correction factor results equal to a value of 1.3.

<figure><img src="image_37.png" /><figcaption>**Figure 2.12:** Average temporal demand profiles for a selection of clusters (a-b). Land use distribution in all clusters (e).</figcaption></figure>

Fig. 2.11(c) shows the final bias-corrected temporal profile comparatively to the EDR one, which clearly exhibits a higher correlation (Pearson coefficient equals 0.94) with respect to the survey-based one.

As a final validation step, we have developed a clustering methodology to identify groups of zones showing similar temporal patterns in terms of the emitted, NSD-estimated hourly demand<fnref n="9" />. The methodology detects a variety of temporal demand patterns within the study

<footnote n="9">⁹The clustering approach was in all similar to the one used for grouping MWS of mobile phone traffic</footnote>
area, partially reported in Fig. 2.12. The analysis of the average NSD-estimated emitted demand in each cluster has been performed comparatively to the EDR one (Fig. 2.12(a)-2.12(b)), and by taking into account the land use composition in each of the identified clusters (Fig. 2.12(c)). The results of the clustering clearly show an agreement in terms of demand profile between the two sources, as well as easily explainable dynamics with respect to the land use configuration of each cluster. As an example, cluster 2 (Fig. 2.12(a)) is mostly associated with rural areas, featuring a strong residential land use with a limited presence of businesses or leisure activities. The emitted demand confirms this conclusion with a profile unequivocally describing a large number of people leaving this cluster to reach working places at the morning peak and coming back at the late-afternoon peak. Conversely, cluster 5 (Fig. 2.12(b)) covers a large amount of industrialised urban areas. These zones are characterised by a mixture of residential (high population density) and industrial/office fabrics: they tend to generate a high number of commuting trips in the morning but also attract a significant amount of demand from the surrounding areas. The latter will massively leave the cluster (thus generating trips) later on, as confirmed by the higher afternoon/evening peak.

These results confirmed the interest in using NSD for dynamic travel demand recon-
struction but also highlighted the absolute need for more structured processes that might
account for bias modelling, trajectory cleansing and reconstruction of missing observations,
towards estimating finer-grained mobility indicators.

<footnote>activity detailed in Sec. 2.1.2), with the only difference that it was applied to 24-hours time series of emitted travel demand.</footnote>
# Chapter 3

## Zonal Traffic Speed and Urban Trajectory Reconstruction

The outcomes of the research described in the previous chapter demonstrated the possibility to leverage individual massive mobile phone data to estimate OD matrices at coarse regional scale, with accuracy consistent with that of traditional household travel surveys. Moreover, these activities provided evidence that passively collected mobile phone data could enable fine-grained inspection and continuous monitoring of human mobility.

This chapter describes the research conducted on mobile phone data to further advance the understanding established in Chapter 2. Specifically, this chapter focuses on achieving a higher level of spatial granularity in the estimation of traffic variables and mobility trajectories by referring to small areas of the urban territory, ranging in size from a few tens to hundreds of square meters. It also aims to capture these variables and mobility trajectories at a shorter temporal scale, in the order of minutes or tenths of minutes. Moreover, all the approaches developed in this chapter enable large-scale reconstruction of mobility information, i.e., encompassing the whole urban area of a city, and, in some cases, even a whole region or country.

In this direction, as part of Manon Seppecher's PhD thesis, we investigated the possibility of using individual massive mobile phone data to estimate time-dependent zonal mean spatial speeds<fnref n="1" /> of a partitioned urban road network. It is worth mentioning that, in this research, we did not explicitly work on NSD or CDR. Instead, we opted for an abstract data representation that we denoted as UAPD<fnref n="2" />. This representation can be customised to suit a variety of datasets, such as CDR, NSD, LBSN data, or any similar dataset generated by users who, while static or in motion, irregularly interact with a device to perform a task (such as communicating, posting content on social media, etc.). The main outcomes of this research are reported in Sec. 3.1.

In parallel to this research activity, we had the opportunity to work on an extensive, complete, and exceptionally large-scale NSD dataset, already presented in Sec. 1.1.2 to showcase the increased accuracy, from both the temporal and spatial points of view, of NSD in comparison to CDR. This dataset was collected and made available by Orange Innovation and provided a realistic representation of what a network operator could collect with state-of-the-art network event probes<fnref n="3" />. The unique features of such datasets enabled

<footnote n="1">¹The zonal mean spatial speed represents an essential variable for macroscopic traffic modelling, according to the theory of the Macroscopic Fundamental Diagram [134].</footnote>

<footnote n="2">²This choice was also imposed by a delay in the acquisition of real CDR data, which were supposed to be available in the context of Seppecher's PhD thesis, and the impossibility of using Orange data which was not a partner of the project supporting these activities. The experimentation was therefore conducted on a UAPD dataset obtained from a massive GPS data, artificially biased to resemble CDR and NSD data.</footnote>

<footnote n="3">³For more details on the probe technology and approach adopted by Orange Innovation on the French national network to collect NSD datasets, we refer the interested reader to [135].</footnote>
us to investigate the problem of utilising NSD to reconstruct human mobility trajectories in
urban environments with spatio-temporal accuracy of a few hundred meters, i.e., comparable
to the one that can be obtained with GPS data. Solving this problem at the scale of a whole
city, and for a large sample of the city population<fnref n="4" />, allows estimating high-quality and highly
detailed mobility patterns, leading to a better understanding of human mobility in urban
settings. In Sec. 3.2, we describe the framework implemented to accurately reconstruct urban
mobility trajectories, while in Sec. 3.3, we present the enhanced solution adopted to perform
map-matching of the reconstructed trajectories on a multi-modal transport network.

The increased accuracy and resolution of the retrieved mobility information, although
beneficial from the perspective of city management and planning, also raises fundamental
questions about users’ privacy. It could become in fact relatively easy to retrieve sensitive
information about people, such as the precise location at a specific time, their home and
work positions, and even to unveil a user’s identity when such sensitive information are cross-
referenced with other datasets. Therefore, we also dedicated significant research efforts to
the problem of devising anonymisation solutions aimed at reducing the sensitivity of the
reconstructed mobility indicators, while preserving the spatial and temporal granularity of
the mobility patterns at the finest possible level of detail. Sec. 3.4 reports on the methodology
developed to anonymise OD matrices derived from massive spatio-temporal trajectories of
human mobility.

The activities on mean speed dynamics estimation have been conducted in collaboration
with CITEPA, in the context of Manon Seppecher's PhD thesis and the Green City Big
Data project (Jan. 2018 - Apr. 2022). The analysis and processing of massive NSD datasets
to enable the estimation of fine-grained mobility patterns has been one of the central topics
of the ANR JCJC PROMENADE (Feb. 2019 - July 2023) project, which I have led. The
framework presented in Sec. 3.2 has been a major contribution of Loïc Bonnetain's PhD
thesis, which I co-directed, and the result of a successful collaboration with INRIA CITI Lab,
CNR Italy, and Orange Innovation. The framework has also been recently patented [136]<fnref n="5" />.
The research activities related to the anonymisation of mobile phone trajectories and of
the derived mobility indicators have their origins in the work described in [137], performed
as part of my post-doctoral activities. In such work, we introduced a novel algorithm,
called GLOVE, for the anonymisation of highly detailed trajectories derived from mobile
phone data. Based on these results, a novel approach to the anonymisation of mobile phone
signalling traces has been proposed in the context of the ANR PRCE MOBITIC (Jan. 2020 -
Dec. 2024) project, and are part of Benoit Matet's PhD thesis, which I currently co-advise in
collaboration with researchers from the GRETTIA laboratory of University Gustave Eiffel.
They have also been part of a collaboration with the IMDEA Networks Institute of Madrid.

This chapter includes content from the following papers:

1. M. Seppecher, L. Leclercq, A. Furno, D. Lejri, and T. V. da Rocha, "Estimation of urban zonal speed dynamics from user-activity-dependent positioning data and regional paths", Transportation Research Part C: Emerging Technologies, vol. 129, p. 103183, 2021.

2. L. Bonnetain, A. Furno, N.-E. El Faouzi, M. Fiore, R. Stanica, Z. Smoreda, and C. Ziemlicki, "Transit: Fine-grained human mobility trajectory inference at scale with mobile network signaling data", Transportation Research Part C: Emerging Technologies, vol. 130, p. 103257, 2021.

3. L. Bonnetain, A. Furno, and N.-E. El Faouzi, "Multi-modal fine-grained map-matching of mobile phone network signaling data in urban areas", 101st Transportation Research Board Annual Meeting (TRB), 2022.

<footnote n="4">⁴Around 35% to 40% of people in France are customers of Orange. This represents a significantly larger sample compared with 1% of persons reached by mobility surveys.</footnote>

<footnote n="5">⁵https://data.inpi.fr/brevets/FR3125197</footnote>
4. B. Matet, A. Furno, M. Fiore, E. Côme, and L. Oukhellou, “Adaptative generalisation over a value hierarchy for the k-anonymisation of origin-destination matrices”, Transportation Research Part C: Emerging Technologies, vol. 154, p. 104236, 2023.

These results are currently supporting further ongoing research related to resilience-driven design and reconfiguration of multi-modal transport networks as well as multi-source data fusion for travel demand anomaly detection and synthetic population generation, which will be described in Chapters 4 and 6, respectively.

## 3.1 Estimation of Mean Spatial Speeds in Urban Areas

Estimating the dynamics of traffic speed in urban road networks is crucial for many applications, including traffic simulation and control, or greenhouse gas emission estimation [138]. Targeting speeds from irregular and low-frequency positioning data is a challenging task. The traditional bottom-up speed estimation methods from GPS floating vehicle tracks [139, 140] rely on averaging individual speeds calculated at the road segment level. These approaches cannot be easily transposed to mobile phone data such as CDR or NSD. As highlighted in Chapter 2, the accurate estimation of start and end times of individual trips – and thus travel times and speed values – from mobile phone trajectory data is problematic even at a coarse commune scale due to the inherent spatio-temporal biases of mobile phone data. However, these data provide an unprecedented spatio-temporal coverage of the urban territory that cannot be efficiently achieved with other sources, such as GPS or pollution sensors, thus justifying significant research efforts in the investigation of methods for reliable speed estimation with UAPD.

One of the primary objectives behind the Green City Big Data project was in fact to develop methods for estimating traffic variables and assessing, with the highest possible level of details, traffic-related pollutant emissions at the scale of a whole metropolitan city. To that end, we decided to approach this issue from an intermediate regional scale suitable for precisely studying the dynamics of large urban road networks, but coarser than the road segment level to properly handle the sparsity of mobile phone data. Based on such intermediate scale, we identified specific emission models (i.e., the COPERT [141]) tailored to the data characteristics, the urban scale of emission assessment, and the decision-making context in which the tool was intended to operate. The COPERT model leverages mean traffic speeds and vehicle features, such as motorisation and emission standards, to quantify the emissions produced per unit of distance travelled. Consequently, estimating global emissions at a regional and urban scale requires multiplying these factors by the total traffic volume<fnref n="6" />. Specifically, the emissions of pollutant k can be estimated using:

$$E_k = TTD \cdot F_k(V) \qquad (3.1)$$

where TTD represents the Total Travel Distance (in km), V stands for the traffic speed (in km/h), and $F_k(V)$ denotes the emission factor (in g/km).

Within the context of Manon Seppecher's thesis, we developed a comprehensive modelling chain that uses UAPD data, road network data, and census and survey data as inputs. This chain produces the traffic variables required for emission estimation, including traffic speeds, travelled distances and traffic volumes. The chain is structured around the speed estimation process and employs different approaches for reconstructing the traffic volumes related to two distinct user categories, i.e., regular and non-regular travellers of the selected urban territory. In the following, we only focus on the framework for the estimation of the mean spatial speeds. The methodologies related to the estimation of traffic volumes for the different user categories and the overall framework for pollutant estimation can be found by the interested reader in [142, 143].

<footnote n="6">⁶The total travelled distance $TTD_r$ within a region $r$ is linked to the traffic flow $q_r$ in the region and the average trip lengths $L_r$ in $r$ according to the following equation $TTD_r = q_r \cdot L_r$ [134].</footnote>
To estimate speeds, we considered $\mathcal{R}$ as the given regional partitioning of the urban road network, with $r$ being the generic region within this partitioning. These regions must be characterised by homogeneous city fabric, demography, road network topology, and, most importantly, traffic dynamics. Homogeneity of traffic dynamics is an essential requirement for a robust estimation of aggregate traffic variables, as reported by the literature on the Macroscopic Fundamental Diagram (MFD) theory [144], which underlies large-scale traffic modelling and simulation. Following the network partitioning guidelines provided by this literature, one can typically divide a city into a set of regions ranging from 5 to 20. This new spatial scale determines the final spatial resolution of traffic speed estimates. Therefore, it must be adapted to the precision requirements of the case study and to the resolution of the available data, as mentioned above. Concerning the temporal scale, we assumed time to be discretized into equal-length time slots $t$, drawn from a temporal set $\mathcal{T}$, which corresponds to the entire observation period (e.g., one day). The length of the time slots imposes the time granularity of the speed estimates and must be chosen accordingly. In must be small enough to reproduce the rapidly changing speed dynamics during peak hours, but sufficiently large to ensure an adequate size of the trip sample population. We chose 15-minutes time slots in this study, as commonly used in the literature.

Under these hypotheses, our aim was thus to accurately estimate the mean spatial speed $V_r^t$ for each $r \in \mathcal{R}$ and $t \in \mathcal{T}$, which is defined as follows:

$$V_r^t = \frac{TTD_r^t}{TTT_r^t} = \frac{\sum_i L_r^{i,t}}{\sum_i T_r^{i,t}} \qquad (3.2)$$

In this equation, $TTD_r^t$ represents the sum of the individual distances (i.e., network lengths) travelled in region $r$ during time slot $t$, i.e., $L_r^{i,t}$. Meanwhile, $TTT_r^t$ represents the sum of the individual travel times in region $r$ during time slot $t$, i.e., $T_r^{i,t}$.

The fundamental principle behind the speed estimation method we proposed is that the overall sample size of the data can compensate for the low data quality at the individual trip level. The method relies on the fusion of individual trip information and statistical considerations to provide a reliable traffic speed estimation.

## Basic Definitions

Similarly to the approach followed in Sec. 2.2, we adopted a definition of trip based on the observed individual UAPD trajectory. Specifically, the trip is defined as the mobility phase of a user between two consecutive stationary activities, named stays in the following. We remind here that an important distinction must be made between the observed trip departure and arrival times and the exact (but unknown) ones, as the varying communication rates of users provide sparse information on their mobility. Based on these considerations, we used the following definitions:

* The regional path is defined as the sequence of the successive regions travelled by a user in a trip from its origin to its destination [145, 146]). It corresponds to a coarser representation of the path followed by the traveller with respect to the road segment scale, based on the considered partitioning into regions of the urban road network.

* We call trip the ternary structure defined by a regional path, an observed travel time and an observed arrival time. Differently from the approach described in Sec. 2.2.2, this definition of trip does not ignore way-points, i.e., events observed for a given user between two successive stays. Intermediate user positions, represented as detection of the user at specific regions from the chosen partitioning, can provide precious information to infer accurate speed estimations.

* The observed departure time of trip $i$ is defined as the time when the last static event