# A Node-Place-Accessibility-Ridership-per-Time Model to Evaluate Micromobility-friendly Transit-Oriented Development Potential in the Hauts-de-France Region

Dylan Moinse, Alain L'Hostis, Ahad Amini Pishro, Ndèye Aïta Cissé, Xavier Lehmann, Shiquan Zhang, Olivier Theureaux, Heythem Adjeroud, Yuetong Liu, Qixiao Hu

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Dylan Moinse, Alain L'Hostis, Ahad Amini Pishro, Ndèye Aïta Cissé, Xavier Lehmann, et al.. A Node-Place-Accessibility-Ridership-per-Time Model to Evaluate Micromobility-friendly Transit-Oriented Development Potential in the Hauts-de-France Region. 2025. hal-05397212

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# A Node-Place-Accessibility-Ridership-per-Time Model to Evaluate Micromobility-friendly Transit-Oriented Development Potential in the Hauts-de-France Region

Dylan Moinse¹, Alain L'Hostis¹, Ahad Amini Pishro², Ndèye Aïta Cissé¹, Xavier Lehmann¹, Shiquan Zhang³, Olivier Theureaux¹, Heythem Adjeroud¹, Yuetong Liu⁴, Qixiao Hu⁴

¹ Univ Gustave Eiffel, Ecole des Ponts, LVMT, F-77454 Marne-la-Vallée, France

² School of Civil Engineering, Sichuan University of Science and Engineering, Zigong, 643000, Sichuan, China

³ Sichuan University, Chengdu, 610065, China

⁴ School of Mathematics, Sichuan University, Chengdu, 610065, China

## Abstract

### Introduction

This study investigates the development potential of station areas in the Hauts-de-France region through an extended version of the Node-Place Model (NPM), aiming to support micromobility-friendly Transit-Oriented Development (TOD) strategies.

### Methodology

We introduce the Node-Place-Accessibility-Ridership-Time (NPART) model, a multi-criteria framework that captures the interplay between public transport provision and the socio-economic, spatial, and functional characteristics of station environments. The model incorporates advanced geostatistical analysis and machine learning tools.

### Key Innovations

NPART advances the original NPM framework through four major extensions: (i) a novel application in a polycentric and under-researched region; (ii) the integration of design quality and dynamic ridership data; (iii) a differentiated analysis of walk-and-ride and bike-and-ride station areas; and (iv) the development of a spatial typology based on accessibility and intermodal potential.

### Results

Our findings highlight the ability of the model to reveal hidden development opportunities, especially in intermediate station areas. A typology of rail-served zones was established, distinguishing three classes: rail-oriented, adjacent activity, and car-centered areas. Half of the stations fall into the intermediate category, showing promising potential for M-TOD initiatives supported by walking and cycling.

### Implications

The NPART model demonstrates strong potential as a planning support tool for regional authorities. By identifying spatial disparities in multimodal accessibility, it provides actionable insights for refining TOD strategies and prioritizing investment. This contribution is particularly relevant in the French context, where quantitative TOD and M-TOD assessments remain rare despite pressing sustainability goals.

**Keywords:** Transit-Oriented Development; Node-Place Model; Urban Planning; Data Science; Bike-and-ride; Station Areas; Classification; Predictive Modeling; Machine Learning
# —1 Introduction

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Transit-Oriented Development is strongly considered a strategic lever for sustainable urban planning, offering a response to the challenges posed by the car-centric policies that continue to shape urban development (Sebban, 2003, p. 14). Overall, the theoretical and operational framework of TOD provides a robust and relevant foundation for addressing current environmental and socio-economic challenges, both at regional and local scales. The objective is not merely to promote better public transport provision—although this remains a necessary condition—but rather to embed this provision within a broader logic of interaction with territorial structure (Bernier, 2023, p. 9). The primary appeal of a network of TOD neighborhoods does not lie solely in their ability to attract new users to public transport, but also in their capacity to create urban environments that are pleasant to live in and visit, marked by high density and conviviality (Bentayou, 2015, p. 40). In other words, only a deep overhaul of territorial organization will make it possible to “wrench the car from the city” (Ducharme, 2021, p. 184).

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The integration of micromobility within the TOD framework supports a strategic positioning that complements the transit network, thereby enhancing the overall efficiency of the alternative mobility system and contributing to the emergence of a “post-carbon city” (Schultz and Grisot, 2019, p. 2). Driven by the rise of new types of vehicles, by electromobility, and by the development of shared mobility services, micromobility addresses emerging mobility needs that transcend the traditional dichotomy between personal vehicles and mass transit. It emerges as a practical and flexible solution. It is thus important to recall that TOD remains a flexible model, whose application varies according to specific contexts and challenges. Its guiding principles do not constitute a rigid framework, but rather an *ethos*, as emphasized by Calthorpe (1993, p. 11). In this regard, it has been observed that *Transit Metropolises* have evolved over the past decades by incorporating hybrid approaches, combining interventions on both mobility supply and demand (Cervero, 2020, pp. 137–143).

Bike-and-ride potential is particularly evident within the modal chain of travel, where it strengthens the efficiency of public transport while offering a fluid and adaptable alternative to meet mobility needs (Molino et al., 2015, p. 4). This “hybrid” model—efficient, flexible, and competitive with the private car (R. Wang and Chen Liu, 2013, p. 107)—promotes a diversified “mobility bouquet” that helps break the “car reflex” and supports a transition toward more sustainable modes (Bertolini, 2017, p. 81; 6t-Bureau de recherche, 2019, p. 42). In this perspective, Amar (2016, p. 225) stresses the need to build bridges and synergies between new forms of mobility and emerging urban planning models, an approach developed by J. Lee et al. (2016, p. 979) through their conceptualisation of a *Bicycle-based Transit-Oriented Development*.

One of the recent evolutions of the TOD explored in this chapter concerns the integration of micromobility, such as electric scooters. These modes complement traditional transport infrastructures by offering effective first- and last-mile solutions. However, knowledge about this form of intermodality—particularly regarding its articulation with urban form—remains limited. This gap is even more pronounced when it comes to the emerging mobilities within this category, whose interactions with TOD are still largely

unexplored. This deficiency is also highlighted by Kang (2020, p. 90), who notes the limited number of studies focusing on the combination between new micromobility services and public transport, despite their widely reported intermodal potential (Richer, 2021). Conducting rigorous empirical studies on these forms of intermodality appears crucial to assess and understand behaviours related to bike-and-ride and scoot-and-ride practices (Bortoli and Christoforou, 2020, p. 13). Based on this assessment, we have chosen to study what we refer to as an *Micromobility-friendly TOD* (Moinse, 2025b)<fnref n="1" />.

The Node-Place Model (NPM) constitutes both an analytical and operational framework within the broader field of research on TOD. It is based on the assumption that transit infrastructures, when integrated into their urban environment, can foster compact, accessible, and economically dynamic forms of development. This perspective invites us to consider stations as catalysts for local development, depending on their connectivity and the intensity of the activities organized around them. The analytical framework relies on two fundamental axes: the *node* index and the *place* index, whose “balance” ensures the integrated development of station neighborhoods (Bertolini, 1999, p. 202). This systemic approach makes it possible to grasp the complexity of interactions between networks and territories—in other words, the urban integration of stations—on the basis of functional and structural indicators (Albertelli, 2024, p. 25), within a quantitative evaluation framework (Chorus and Bertolini, 2011, p. 47).

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## —1.1 A Methodological
Framework to Assess the
Coordination Between the
Node and the Place

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Understanding the potential of station areas to support sustainable urban development remains a critical challenge in transport and land-use planning. In this context, NPM offer a structured, empirical approach for evaluating the articulation between transport provision and urban form. Their flexibility and replicability have made them popular across diverse contexts, particularly for supporting TOD strategies. This study applies an extended NPM-named Node-Place-Accessibility-Ridership-Time (NPART)—to the Hauts-de-France region, a polycentric and under-studied area in northern France where TOD remains marginal in planning practice. The region’s high car dependency and strong spatial disparities in public transport access present specific challenges that justify the use of a detailed, spatially-explicit model. The goal is to go beyond generic recommendations

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<footnote n="1">¹In this way, this work is based on the Moinne (2025b, p. 621)'s PhD thesis that demonstrated that the integration of micromobility into the conceptualization of an M-TOD is not a mere improvement of the TOD, but rather a structural transformation of the rail-oriented urbanism model. By adopting a multi-level approach, his work laid the foundations for an M-TOD, integrating walk-and-ride and intermodal practices, in order to redefine the functional proximities of station neighborhoods. Theoretically, this research follows two main lines of thought: on one hand, it extends the reflections on "network urbanism" (Dupuy, 1991); on the other hand, it contributes to the discussions around the "mobility turn", repositioning urban planning around renewed proximities (Sheller and Urry, 2006; Sheller and Urry, 2016, p. 8; Randell, 2020, p. 13). In this perspective, stations should no longer be seen as mere functional connection points, but rather as strategic intermodal spaces, located at the interface of the regional urban system. They are accessibility hubs, where cycling plays a fundamental role in the network and expansion of accessible territories. These new mobility practices offer the opportunity to move beyond a segmented, mode-based, and strictly infrastructure-focused view of public transport, by improving the "relational capacity of networks" (Richer, Meissnner, et al., 2016, p. 15) through a systemic "door-to-door" approach. From this point of view, "altermobility" becomes inseparable from multimodality and intermodality (Vincent-Geslin, 2012, pp. 91-92), fostering a contextualized approach to TOD. A shift in reference frameworks is necessary, moving from an attractiveness logic to a territorial "hospitality" (Talandier, 2023, p. 2), which calls for a reconfiguration of urban strategies based on proximity relationships and quality of life expectations, paving the way for a new way of conceiving the interactions between urbanism and mobility.</footnote>
and provide operational insights for local planning actors.

The methodology adopted to analyze the station-neighborhood pair is based on the characterization of the *node* and its surrounding *place*, using two composite indicators (Bertolini, 1996, p. 343; Bertolini, 1999, p. 199). The articulation of these two dimensions allows us to address the still underexplored multiscalar interaction between public transport network nodes (*nodes of networks*) and urban places (*places in the city*), as conceptualized by Bertolini (1996, p. 344). This model is primarily understood as an "an analytical tool to help identify the potential for public transport-oriented urban-regional development." (Bertolini, 1999, pp. 199-201). Based on a feedback-loop model, this analytical framework examines how land use reflects and benefits from the advantages of public transport, and vice versa (Chorus and Bertolini, 2011, p. 47). NPM relies on the idea that improved public transport provision creates favorable conditions for urban development, which in turn increases mobility demand, thereby supporting the development of the mobility system itself (Chorus and Bertolini, 2011, p. 47). Exploring the interrelations among these various factors can then support public policy by promoting TOD and offering a classification that helps guide investment more effectively according to the type of station neighborhood.

NPM is fundamentally designed to address the concept of accessibility, examining the combined effects of transport networks (*network science*) and urban environmental characteristics (*spatial science*)—an approach that echoes the perspectives outlined by Ducruet and Beauguitte (2014, p. 300). Indeed, the model draws on the notion of accessibility<fnref n="2" /> based on the premise that an accessible node also requires an accessible place—and vice versa (Bertolini, 1999, p. 203). This approach strategically positions the public transport station at the heart of the network to which it belongs, emphasizing the mobility opportunities and accessible functions it enables (Amini Pishro, Q. Yang, et al., 2022, p. 3). This vision aligns with the paradigm shift driven by TOD (Banister, 2008, p. 75), promoting a transition from planning for mobility to planning for accessibility, in which the model is firmly anchored (Caset, Vale, et al., 2018, p. 496). This comparative perspective on accessibility is also supported by Caset, Vale, et al. (2018, p. 521), who argue that the NPM effectively highlights the development potential of stations in terms of urban planning and mobility, by articulating transport networks with territorial dynamics. In doing so, the NPM proves to be a powerful analytical tool for guiding public policies and urban planning strategies by placing accessibility at the core of transport infrastructure planning and urban development.

The analytical framework of the NPM relies primarily on the development of a node typology, structured around the creation of a spatial index designed to evaluate levels of urban development and mobility systems within a region. The classification exercise proposed by the model thus proves to be an effective tool for identifying and addressing barriers to the implementation of TOD (Kamruzzaman et al., 2014, p. 55). Moreover, classifying stations into typological categories simplifies the understanding of territorial configurations, facilitating comparisons and the formulation of cross-cutting strategies that benefit urban planners, transport authorities, and both public and private actors involved in urban production (Kamruzzaman et al., 2014, p. 55). The growing interest in developing "normative TOD typologies" of nodes reflects a desire to provide public policy with a practical tool to inform and guide regional planning (Higgins and Kanaroglou, 2016, p. 307). This typology aims

to evaluate a set of station neighborhoods at the regional scale, allowing for comparison between stations that do not share the same territorial development potential nor the same intervention needs (IAU, 2017, p. 2).

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<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Two-Dimensional Diagram of the Node-Place Model.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

NPM proposes a diagrammatic and two-dimensional framework for classifying public transport nodes based on observed characteristics both at the level of the physical stops and their surrounding environments. In this diagram, initially developed by Bertolini (1996, p. 344), the intensity of land use around the station is represented along the horizontal axis, while the vertical axis measures the accessibility of the node. This schematic allows for the identification of five distinct typological configurations (see Fig. 1, p. 2):

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* The "balance zone" lies along the central diagonal of the diagram, indicating a relative equivalence between node-related and place-related values<fnref n="3" />;

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* The "stress zone" describes a context in which both transport provision and the diversity of human activities are intense, generating maximum interactions;

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* The "dependence zone" characterizes station neighborhoods where both node and place values are coherent but remain underutilized;

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* The "unbalanced node zone" is identified when transport infrastructure and services exceed the surrounding land use;

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* The "unbalanced place zone" refers to the opposite situation, where activities outpace the available public transport supply.

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A model like the NPM provides an empirical framework to explore diverse urban patterns, challenging the notion of a

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<footnote n="2">²It is important to specify that the model focuses specifically on the notion of "relative accessibility" (Dalvi and Martin, 1976, p. 27).</footnote>

<footnote n="3">³The definition of this zone has been debated in the scientific literature, with L. Yang and Song (2021, p. 243) instead referring to it as an "accessible zone," combining mobility and urban environment considerations. When classifying stations in Brisbane, Kamruzzaman et al. (2014, p. 55) argued that nodes should be classified independently of the concept of balance. The authors illustrate this point by presenting two stations identified as having "TOD potential," one of which is balanced while the other is not. Furthermore, according to Reusser et al. (2008, p. 194), if a balance—such as defined by Bertolini (1999, p. 202)—is to be considered, it should be contextualized according to station size, to better account for local specificities.</footnote>
"one-size-fits-all" approach—particularly in the implementation of TOD principles. The need to tailor recommendations in order to effectively guide public policies requires a thorough analysis of the territorial context surrounding public transport stations (Zemp et al., 2011, pp. 678-679). Conversely, excessive simplification—neglecting the specific context in which each station operates—can lead to misjudgments, as questioned by Cao et al. (2020, p. 2). The NPM modelling framework emphasizes the local specificities of each site for a given type of TOD, rather than addressing the binary question of general suitability, as argued by Kamruzzaman et al. (2014, p. 55). The model's ability to adjust and better reflect the complexity of the urban environments in which transport stations are embedded is thus validated by the scientific literature, demonstrating its applicability across a wide range of urban contexts.

## -1.2 State of the Art of the Node-Place: An Advanced and Gradually Enhanced Model

Since 2007, the number of studies dedicated to this approach has continued to grow significantly. This trend is highlighted in the systematic literature review published by Ibrahim, Ayad, and Saadallah (2022, p. 383). Over the past two decades, the model has been implemented in a variety of geographical contexts—primarily in East and South Asia as well as Northern Europe (Caset, Blainey, et al., 2020, p. 1)—and has been adapted to different application scales, ranging from railway corridors to regional and even national levels. The NPM has been developed both by academic communities and various planning authorities, thus demonstrating its transcontinental flexibility (Caset, Blainey, et al., 2020, p. 1).

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Chronological Evolution of Studies on the Node-Place Model.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

The literature review was conducted through a targeted search of peer-reviewed articles and planning documents published between 1996 and 2025, focusing on the NPM tool,

and more generally on integrated station area typologies, intermodal accessibility, and the role of micromobility in TOD frameworks. The review was conducted using major academic search engines and bibliographic databases, including *Google Scholar*, *Scopus*, *Web of Science*, and *ResearchGate*. References were organized using *Zotero* and synthesized in an *Excel* matrix, allowing for thematic comparison across methodological approaches, indicator sets, key findings, and prospective insights.

As part of the literature review conducted on the NPM, we identified a total of 79 bibliographic references explicitly applying this approach. These contributions were published between 1996—the year marking the conceptualization of the model—and early 2024, when the literature search was carried out. The corpus thus includes 69 journal articles, book chapters, and conference proceedings, as well as 5 technical reports, and 3 Master and 2 PhD theses. This initial chronological analysis confirms a growing interest in the NPM starting in 2014, with a particularly marked acceleration between 2020 and 2024, during which the number of publications doubled compared to the period spanning 1996 to 2019 (see Fig. 2, p. 3). Moreover, this progression is part of a broader trend of increasing studies focusing on TOD (Moinse, 2023, p. 185).

### An Underutilized Method in the French Context

Studies reveal significant disparities in the application of the NPM depending on geographical contexts. Among the 63 study areas identified, contributions are concentrated in China (11 references) and the Netherlands (8 references). In contrast, the United States—with only 2 studies—are almost entirely absent from this overview. Asia thus emerges as the leading continent in the development of this quantitative tool (see Fig. 3, p. 4). Furthermore, no scientific publication employing this model has been identified in France, apart from a study conducted by the *Institut Paris Region* (IAU, 2017, p. 2).

### An Assessment at a Limited Geographical Scale

Very few studies focus on the application of the NPM in regions that are not part of an international metropolitan area. This observation is consistent with that of Nigro et al. (2019, p. 111), who highlight the scarcity of research addressing the coordination between public transport and land use in low- or medium-density areas, economically declining territories, or those integrated into high-speed rail networks, due to their extensive coverage and associated constraints.

Another demonstration of the model's flexibility lies in the diversity of public transport network sizes examined, ranging from 3 to 1,700 stations within a defined study area<fnref n="4" />. This quantitative approach generally covers regional railway or metro systems, with an average of 189 and a median of 63 nodes. Indeed, half of the studies applying the NPM to metro systems examine fewer than 52 stops, while those focused on railway networks encompass around 103 nodes.

The adaptability of the model to various geographical contexts and to different forms of public transport systems illustrates its flexibility and its capacity to evolve in line with both theoretical and empirical developments since its initial formulation. This ongoing enrichment is also reflected in the

<footnote n="4">⁴It is worth noting that two national-level Swiss studies successfully adapted the model to a network of approximately 1,700 stations (Reusser et al., 2008, p. 194; Zemp et al., 2011, p. 672).</footnote>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Geographic Distribution of Studies Employing the Node-Place Model.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

diversity of composite indicators used to define it, as presented in the following section.

## A Model Gaining Precision Through Iterative Refinement

At present, there is no consensus regarding the selection of indicators to assess the functionality of nodes and places in public transport stations (Chen and Lin, 2015, p. 446; Reusser et al., 2008, p. 194). Several adaptations of the original NPM model have been introduced (J. B. Lee and

Salih, 2024, p. 2; Lukman and Singh, 2014, p. 20). According to these authors, the ideal indicators for this model should meet five key principles:

* Relevance and significance to TOD, by capturing differences across public transport stations;

* Practicality, meaning that the indicator should be measurable and applicable;

* Clarity of interpretation;

* Efficiency, with respect to data availability and measurement method;

* Reproducibility, allowing for general use across contexts

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beyond the original study area.

At the early stage of the analytical model's development, Bertolini (1999, pp. 202-203) proposed the integration of a set of synthetic indicators, classified according to the dimensions of the "node" and the "place." For its part, the Transportation Research Board emphasized the need to rely on the most quantifiable aspects of TOD to aggregate them into indices (Transportation Research Board of the National Academies, 2007): the most relevant indicators for calibrating the NPM model are namely public transport ridership, density, quality of public space design, amount of mixed-use structures, pedestrian activity, property value increases, public perception, intermodal connectivity, and parking layout configurations.

However, the model was soon enriched with new indicators to address certain limitations identified in the scientific literature. First, M. Zhang and J. B. Lee (2023, pp. 4-5) argued that the current model fails to optimally identify the functional connections between the urban environment and the node. They point to a persistent confusion in distinguishing between a TOD-type environment and a Transit-Adjacent Development (TAD)<fnref n="5" />. To address this confusion, Z. Li et al. (2019, p. 271) propose integrating a new dimension related to place connectivity and extending the NPM by adding a third dimension focused on the notion of design.

In parallel, research has steered the model towards integrating public transport station ridership—an aspect paradoxically overlooked despite being one of the pillars of TOD. Cao et al. (2020, p. 2) and Amini Pishro, Q. Yang, et al. (2022, p. 3) emphasize that previous studies have rarely considered ridership as a key dimension of the NPM. Yet ridership reflects how passengers actually use transit infrastructure and how this usage is influenced by the quality of the urban environment. The aim of such an extension of the model is therefore to systematically integrate mobility demand, as advocated by Liao and Scheuer (2022, p. 2).

Among the 57 studies detailing the parameters defined in the development of their NPM, we identified 49 indicators used at least twice, which we grouped into four dimensions: "public transport service," "urban development," "local accessibility," and "socio-demographic characteristics of the population." In total, 421 occurrences of indicators were recorded, corresponding to an average of 9 indicators selected per model. More specifically, 13 variables are included in the first dimension, accounting for 131 occurrences; 12 variables in the second, accounting for 143 occurrences; 15 variables in the third, with 132 occurrences; and 7 variables in the last dimension, totaling 15 occurrences (see Fig. 4, p. 6). This analysis highlights a tripartite distribution of indicators across dimensions related to public transport supply, the degree of urban development, and the quality of public spaces combined with local accessibility. This finding aligns with the analysis of Lyu et al. (2016, p. 42), who identified, based on a systematic literature review, a total of 94 indicators, including 24 focusing on "transport," 53 on "urban development," and 17 on "design."

The most frequently used criteria include population density (31 occurrences) and employment density (28 occurrences), functional diversity (28 occurrences), the provision of urban public transport (23 occurrences), service frequency (22 occurrences), service coverage in terms of stations accessible

within a given time-distance threshold (22 occurrences), car parking availability (22 occurrences), and network connectivity, expressed as the number of directions accessible from a given station (20 occurrences). By examining the intensity of the links between these indicators, we observe that the most frequent connections appear, on the one hand, between population density and, on the other hand, employment density (26 links), functional diversity (23 links), car parking availability (21 links), and the presence of urban public transport systems (21 links), as well as between employment density and functional diversity (20 links).

Despite the identification of a wide range of models adapted to specific research objectives and geographical contexts, Ibrahim, Ayad, and Saadallah (2022, p. 394) argue that the literature reveals a gap in the quantitative measurement of TOD potential. Furthermore, Olaru et al. (2019, pp. 10-12) recommend integrating mobility solutions related to the first and last mile (FLM), which have received insufficient attention in existing NPM models. They also suggest broadening the scope of analysis to encompass an expanded station area, as mentioned by Z. Li et al. (2019, p. 280). For their part, Robillard et al. (2024, p. 80), as well as Nigro et al. (2019, p. 120), emphasize the importance of distinguishing between the pedestrian and cycling environments around stations, noting that the latter remains largely overlooked in both the model and the broader TOD literature. Following these recommendations, along with those of Motieyan and Mesgari (2018, p. 9), who advocate for the development of a model that incorporates spatiotemporal dynamics, we developed an NPM model enriched by insights from previous studies, while refocusing our core research question. We therefore chose to calculate an extended model, aiming to cover the entire railway network to ensure internal consistency, as recommended by Chorus and Bertolini (2011, p. 58), and at a broad regional scale, in line with the recommendations of Wei and L. Wang (2023, p. 643).

The Hauts-de-France region presents a particularly relevant case for applying and extending the NPM. As a structurally polycentric and socio-spatially fragmented territory, it combines dense urban cores such as Lille with a majority of medium-sized cities, peripheral areas, and rural zones, many of which are marked by economic vulnerability and reduced access to services. Public transport provision is heterogeneous, with major disparities in frequency, coverage, and modal integration. In this context, developing a nuanced station area classification is critical to identifying zones with latent redevelopment potential and to reorienting planning priorities. The proposed model addresses this challenge by linking transport accessibility with urban form and ridership patterns, thereby supporting a more strategic approach to TOD. Beyond its regional application, the model offers a transferable framework to inform integrated urban and transport policies in contexts facing similar accessibility and equity concerns.

## -2 Study Area

As presented in this section, the choice of a regional geographic perimeter for our research topic, rather than more narrowly defined intervention levels, is justified by the desire to adopt a systemic view of the interactions between mobility systems and territorial dynamics. This approach provides the opportunity to examine strategic areas associated with railway corridors, as defined by the concept of "Urban Corridors" (L. Liu, 2016, p. 63), while covering a variety of urban contexts. This orientation is also explained

<footnote n="5">⁵Using the San Francisco Bay Area as a case study, Renne (2009, p. 3) conceptualizes *Transit-Adjacent Development*, describing real estate or urban development projects located near public transport stations but not designed to maximize ridership. Although adjacent to stations, these projects promote car use through low density, a spatial predominance of car parks, limited access to active modes, and a lack of functional diversity. Unlike TOD, TAD is neither morphologically nor functionally integrated into public transport infrastructures.</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Network Analysis of the Indicators Selected in Node-Place Models in Literature.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

by the alignment between the regional scale and transportation planning competencies, allowing us to integrate institutional issues and policy levers. With a multiscalar perspective, we focus on station districts as spatial units, linked with the scales of corridors and the rail network, and embedded within a regional framework. The Hauts-de-France region, resulting from a recent administrative merger<fnref n="6" />, offers a polycentric laboratory suited

to studying the applicability of a revisited TOD.

In France, the region is an appropriate level for deploying a TOD strategy, due to its diverse competencies, reinforced by a legal framework for transport and planning. Its primary virtue is to create a project territory by uniting, within a common reference framework, initiatives that were

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<footnote n="6">⁶Following the law of January 16, 2015, regarding the delineation of regions, regional and departmental elections, and the modification of the electoral calendar, the Nord-</footnote>

<footnote>Pas-de-Calais Picardie region, now known as Hauts-de-France, was born from the merger of the former regions of Nord-Pas-de-Calais and Picardie. The city of Lille became the regional capital, where the regional council is seated, by a decree from the Council of State issued on September 28, 2016.</footnote>
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previously conducted separately. For example, the "contracts of axis" allow for overcoming the "counter" logic that has long characterized the relationships between regions and municipalities (Bentayou et al., 2015, p. 118). It is since the enactment of the first legislative instrument positioning the region at the center of transport policies, the *Loi d'orientation des transports intérieurs* (LOTI)<fnref n="7" /> which established the principle of shared responsibilities between the state and local authorities. But it is especially the *Loi relative à la solidarité et au renouvellement urbains* (SRU) of December 13, 2000⁸, which enshrines the region as the "organizer of collective public transport of general interest", excluding Île-de-France and Corsica (Commission nationale du débat public, 2024). Act III of decentralization is materialized by the *loi de modernisation de l'action publique territoriale et d'affirmation des métropoles* (MAPTAM) of January 27, 2014, which includes the *loi portant sur la Nouvelle Organisation Territoriale de la République* (NOTRe) of August 7, 2015⁹ which establishes the region as the "leader of intermodality and complementarity between transport modes" (GART, 2024). Recently, its intervention framework has been reinforced by the *loi d'orientation des mobilités* (LOM) of December 24, 2019¹⁰ which makes it responsible for strategic planning and coordination of intermodality and spatial planning policies (Barone and Thébert, 2020; Sajous et al., 2020, p. 174). Thus, the region, with its integrated approach, benefits from considerable decision-making power, significant investment resources, and a long-term strategic vision. We can even assert that, supported by a territorial narrative, transport policies at this political level help legitimize their recent merger (Revelli, 2019, pp. 260, 575-577).

## -2.1 Defining the Geographic Scope: The Hauts-de-France Region

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Located in the northern part of France, the Hauts-de-France region spans approximately 32,000 square kilometers and is home to 6,000,000 inhabitants, representing 9.2% of the metropolitan population according to the latest population census (see Fig. 5, p. 8). The population is distributed across five departments: Aisne, Nord, Oise, Pas-de-Calais, and Somme. This makes it the fifth most populous region in the country, with the Nord department alone concentrating nearly half of the regional population, thus making it the most populous department in France. The Lille metropolitan area, with a population of 1,520,000, ranks fourth nationally. The second largest metropolitan area in the region is that of Paris, on the Picardy side, with a population of 513,000 inhabitants (Leroux and Tieng-Majcherczak, 2023).

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The Hauts-de-France region is characterized by dense urbanization, supported by a network of key hubs and a fabric of medium-sized and small towns. Firstly, the European Metropolis of Lille is marked by "a relatively balanced quadricentric form" in terms of urban structures (Mignot et al., 2007, p. 37), although the "Capital of French Flanders" is gradually asserting itself under the effects of ongoing metropolization. In addition to major urban areas such as Lille, Amiens, Arras, Calais, and Dunkirk, the region is structured by a conurbation forming a dense industrial arc, a legacy of its mining past. This "banana-shaped" area (Mission Bassin Minier Nord-Pas-de-Calais, 2024) stretches from Béthune to Valenciennes, passing through Lens, Hénin-Beaumont, and Douai. Although each of these cities has fewer than 50,000 inhabitants, their organization into a dense multipolar urban network brings together a population approaching that of Lille agglomeration, nearly one million residents. A comparable urban organization, though on a smaller scale, can be found in the Oise Valley, between Creil and Compiègne, considered an extension of the Parisian suburbs (see Fig. 5, p. 8).

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The Lille metropolitan area appears to have the capacity to transmit its growth to surrounding territories through a high degree of polarization. It occupies a central position in a polycentric regional system where secondary hubs act as relays for Lille and Parisian activity, relying on cross-functional relationships that structure the territorial network (ADULM and Cattan, 2016, pp. 43, 46). The regional objectives are thus oriented towards a balanced functional specialization of the territory, promoting a logic that is both multifunctional and multipolar, in order to surpass the consolidation of the monocentric character of metropolises (Lo Feudo, 2014, p. 144). The significant regional endowment in railway infrastructure constitutes a key resource for fostering a truly multicentric territorial functioning. However, despite the density of the regional network, mobility practices remain heavily reliant on the automobile. Indeed, 77% of the trips made by employed individuals are done by private car, compared to only 4% by public transport. This proportion even rises to 83% in the Bassin Minier, despite its well-developed railway services (Michel and Werquin, 2016). To better understand the underlying reasons for automobile dependence (Motte-Baumvol et al., 2014, p. 74; Gallez, 2018, p. 4), despite the density of the railway network, we will delve into the mobility and urban planning issues in the Hauts-de-France region, closely tied to our research topic.

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The railway network in Hauts-de-France is one of the most dense in France, representing nearly 10% of the national rail network, with approximately 2,900 kilometers of track (Région Hauts-de-France, 2024a, p. 11; CESER

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<footnote n="7">⁷Following an initial experiment on the regionalization of railways conducted in the former Nord-Pas-de-Calais region (as part as Hauts-de-France region nowadays) in 1978 (Chauvineau, 2001, pp. 1-3; Passavant-Guion, 2016, p. 424). The LOTI of December 30, 1982, initiated a decentralization process by establishing a new distribution of responsibilities between the state and local authorities. This law opened the door to the regionalization of "rail links of regional interest" through agreements between the regions and the SNCF (Deimon, 2024, p. 4). The beginning of this long process, initiated as early as 1974 with regional transport plans, culminated in the launch of regional railways (TER networks) in 1987. However, the decentralization of rail services remains optional (Commission nationale du débat public, 2024). Following the enactment of the *loi d'orientation pour l'aménagement et le développement du territoire* (LOADT) of February 4, 1995, later adopted and modified by the *d'orientation pour l'aménagement et le développement durable du territoire* (LOADDT) of June 25, 1999, seven regions, including Nord-Pas-de-Calais, applied starting in 1997 to experiment with the management of regional rail transport (Burlando and Guihery, 2004, p. 132).</footnote>

<footnote n="8">⁸The generalization of railway regionalization was scheduled for 2002, in light of the provisions applied by the SRU and the publication of the decree on "transfer of competencies in regional collective transport" on November 27, 2001 (Burlando and Guihery, 2004, p. 132). The law then planned the development of cooperation between the region and urban authorities to organize intermodality: the creation of public transport partner committees thus aims to improve the continuity between the rail system and urban transport systems. It is within this framework that the regional level tends to assert itself as the leading authority on "intermodal transport and the complementarity between transport modes" (Chauvineau, 2001, pp. 1-18).</footnote>

<footnote n="9">⁹With the MAPTAM, the regional level is tasked with coordinating its actions with those of other mobility organizing authorities while defining general rules related to intermodality between transit services, as part of the *schéma régional de l'intermodalité* (SRI) (GART, 2024). Furthermore, the law redraws the boundaries of certain French regions, including Nord-Pas-de-Calais and Picardie, by merging them. These changes present an opportunity for the newly merged regions to rethink their transport systems offerings and policies within their expanded territories (Cerema, 2017). Simultaneously, the NOTRe law strengthens this competency transfer aspect by creating a planning tool at the regional level, the *schéma régional d'aménagement, de développement durable et d'égalité des territoires* (SRADDET).</footnote>

<footnote n="10">¹⁰Forty years after the foundational text organizing public transport services (LOTI), the LOM seeks to establish a distribution of responsibilities between the 1,200 local authorities, called AOM, and the 12 regional *autorité organisatrice de la mobilité* (AOM) responsible for TER and intercity buses (Richer, Pitout, et al., 2024, p. 29). In the decentralization movement, the region remains a relatively young actor whose political weight is steadily increasing as territorial reforms grant it new competencies: today, the region is becoming "the all-encompassing actor in mobility" (Richer, Pitout, et al., 2024, p. 34). Thus, the LOM promotes a "right to mobility", referring to the previous "right to transport" established by the LOTI, as it aims to reduce "the strict boundary, once clear, between individual mobility and public transport" (Izembard, 2020, p. 283).</footnote>
HAUTS-DE-FRANCE REGION

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Mapping of the Rail and Cycling Networks in the Hauts-de-France Region.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

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Hauts-de-France, 2021, p. 51). This extensive network includes 363 stations and stops, as well as coverage of 74 commercial commuting lines (SNCF Réseau, 2024). This network is further enhanced by high-speed lines, with sections crossing the region, connecting Paris to Lille and extending directly to Brussels, London via the Channel Tunnel, and other French and European destinations.

Today, the density of the regional rail network allows 96% of the regional population to be within 10 kilometers of a rail stop, although the 15 largest regional stations account for 55% of the network's ridership (CESER Hauts-de-France, 2021, p. 58). However, this picture of a region well-equipped with rail infrastructure contrasts with observed mobility patterns that indicate intensive car usage. Today, 10,400,000 daily car trips, of which 5,200,000 are characterized by spatial distances of less than 5 kilometers, with an average vehicle occupancy rate of 1.1 (Région Hauts-de-France, 2024b, p. 171). However, only 150,000 motorized trips are potentially transferable to train or bus, regardless of the level of service within the regional territory, emphasizing that the monomodal solution of the train alone is insufficient (Observatoire des territoires, 2019, pp. 7-8). This finding calls for the consideration of intermodal alternatives and a reevaluation of territorial organization.

Cycling remains marginal in the Hauts-de-France region, accounting for only 2.2% of all trips. This proportion varies by department, ranging from 1.1% in the Oise to 2.7% in the Nord (Insee, 2023). This regard, the region boasts a cycling network of over 3,107 kilometers in 2022, far from the

66,000 kilometers of roads in the administrative area. These dedicated facilities mainly consist in bike routes extending over 1,562 kilometers, 22% of which have been completed so far, in line with the objectives set in its 2024-2028 Bicycle Plan, with 57% being on dedicated lanes, totaling 861 kilometers of bike paths (Vélo & Territoires, 2023). In comparison, France as a whole had over 76,500 kilometers of cycling infrastructure in the same year, including 37,000 kilometers of bike lanes, 23,000 kilometers of greenways, and 15,000 kilometers of cycle tracks (Geovelo, 2024).

In parallel with the individual use of bicycles, several areas in the Hauts-de-France region offer bikesharing systems. In 2024, five municipal areas benefit from these mobility services: 20 communes around Lille with 260 stations<fnref n="11" />, 5 communes around Calais with 41 stations, the city of Amiens equipped with 25 stations, 5 communes around Soissons with 15 stations, and the city of Laon with 11 stations (Région Hauts-de-France, 2023, p. 41). Additionally, 44 communes in the Lille agglomeration offer an electric-assisted bikesharing service, while 20 of these communes also provide e-scooter schemes, mainly operated by Lime, which manages a fleet of approximately 2,000 vehicles. Similarly, in the Valenciennes agglomeration, the local start-up Urban Labs Technology provides 200 shared e-scooters under the Jerico brand.

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## -2.2 Representing Station Areas Accessible by Walking and Cycling

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The interactions between urban fabric and the deployment of modal chains are explored from the perspective of spatial references associated with the station-system, understood through the concept of station neighborhoods nested within different patterns and scales (Menerault and Barré, 2001, p. 14). Defining and delimiting what constitutes a station area, within the context of its urban integration, represents a methodological milestone in this research. Stations, both junction nodes and threshold spaces (Serviant, 2015, p. 40), require special attention to outline the contours of their spatial footprint in relation to our research topic. The established boundaries serve as the foundation for constructing our empirical studies. Therefore, in this section, we focus on the parameters influencing the size and shape of station neighborhoods, as well as the nature of the data utilized (Forsch and Haunert, 2023, p. 383).

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### Station Catchment Areas

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The first challenge guiding the spatialization of the station neighborhood network in the region focuses on their size. By "station neighborhoods," we refer here to two configurations: walk-and-ride for the pedestrian level (P) and bike-and-ride for the cycling level (C). These areas are further differentiated based on their type of transit service. We find pedestrian and cycling station neighborhoods around multimodal exchange hubs, high-speed rail stations, and regional commuting stops. Additionally, to prevent excessive overlaps, especially in areas where the distances between stations are short, we have defined a theoretical maximum radius for the station neighborhoods, using a geometric

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<footnote n="11">¹¹V'Lille system, launched in 2011, is a major component of the cycling policy in the Lille metropolitan area, with 2,600 public bicycles. It has seen a 20% increase in usage over the past 10 years (Métropole Européenne de Lille, 2021, p. 1). With a core of 15,700 annual subscribers and 302,000 occasional users in 2023, the service averages 9,700 rentals per weekday (Bergès, 2023). This corresponds to a usage rate of 4 rentals per bike per day, a figure that rises to 6 daily uses in Lille, but drops to 0.5 in Roubaix and Tourcoing.</footnote>
tessellation diagram that confines each area of influence within a cell specifically associated with it. This work on the spatial identification of station catchment areas builds on our previous scientific publication, based on the same research field, namely the Hauts-de-France region, with the determination of pedestrian and cycling zones (Moinse and L'Hostis, 2024, pp. 3-9).

To determine the size of the station districts, we chose to use data from a questionnaire distributed to users, with the aim of analyzing their relationship to spatial-temporal distances. Our approach is thus user-centered, considering that the relevant geographical scale of the station influence areas is directly linked to the social acceptability of the users. This acceptability, in turn, results from observed mobility practices. The exercise consists in identifying the effective range of walk-and-ride and bike-and-ride, within the context of the Hauts-de-France region, in order to define station areas whose size is adjusted based on territorial realities and existing practices (Moinse and L'Hostis, 2024, pp. 3-9). To do this, we relied on a commonly used threshold to measure the socially acceptable distance for access and egress stages (Wenxiang Li et al., 2021, p. 5): the 75th percentile and the 85th percentile of the cumulative distribution of traveled distances, for walking and cycling respectively (J. Lee et al., 2016, p. 982)<fnref n="12" />. This methodology allows the calibration of station area sizes based on empirical criteria derived from users' practices and perceptions, thus ensuring a better match between the defined areas and actual usage.

While the first distinction made between station areas is based on the scope of the transfer modes, we also took care to differentiate the catchment areas of the various public transportation systems. This approach aims to refine the precision of the delineation of these station neighborhoods, as transfer distances to the high-speed rail network (HSR) tend to be broader than those of conventional commuting rail networks. Hasiak and Bodard (2016, pp. 5-6) inquire about the factors influencing the geographic forms of periurban station areas in the Hauts-de-France region. The authors conclude that the area of these zones expands as rail frequency, PnR capacity, and distance to other stations increase. Thus, most traditional rail systems serve shorter distances, with influence zones typically limited to a regional or departmental territory. In contrast, HSR greatly extends the influence areas of the stations it serves. These stations become true regional, national, and even international hubs, attracting passenger flows over much larger distances. Taking these differences into account, the methodology allows for a more refined definition of station areas based on the actual reach of the transportation systems involved.

Furthermore, this approach incorporates an additional goal: the consideration of stations that, beyond being simple railway stops, benefit from enhanced connectivity through urban public transport networks on rail, such as metro and tramway systems. These complementary infrastructures tend to improve the accessibility and attractiveness of these stations, justifying a differentiated analysis of their influence areas. Aware that there is no standardized method for generating multimodal isochrones (Krismer, 2017, p. 5), we collected and utilized data from the GTFS format of the various public transport networks in the region, excluding bus

networks. This choice was motivated by algorithmic performance considerations, as integrating bus networks could significantly slow down the calculations. Our multimodal accessibility analysis is based on travel time, adopting a time propagation method. This method propagates travel times from a starting point to determine the zones that can be reached within a given time interval<fnref n="13" />.

To implement this approach, we used the multimodal buffer and isochrone generation tool from the open-source project OpenTripPlanner (OTP). This tool allows for the calculation of accessible areas from a given point, taking into account different modes of transport as well as possible combinations of these modes (Krismer, 2017, p. 15), with several adjustable parameters. By utilizing the OTP Analyst module, we were able to generate isochrones representing the station areas based on their multimodal connections. In total, 5 stations in the region had their influence areas extended to encompass the entire multimodal approach due to their immediate proximity to metro or tramway stations: *Euraflandres*<fnref n="14" />, Lille CHR, Roubaix, Tourcoing, and Valenciennes. These multimodal exchange hubs were designated as multimodal hubs. In parallel, 13 other stations belong to a second category, associated with a HSR service, while a third category includes a total of 300 stations exclusively served by commuting rail.

Given the large number of nodes we are studying within the regional railway network, a legitimate concern arises regarding our approach to extending the walking and cycling catchment areas for the stations: the potential for territorial redundancy in spatial analyses, resulting from an excessive overlap of the catchment areas, especially in certain urban areas characterized by a high density of stations. While, in the case of a rail system, the inter-station distance is typically significant, the expansion of the catchment areas through the integration of cycling raises the legitimate question of an overabundance of overlapping areas. This problem is not only a matter of aesthetics or cartographic clarity but also concerns the risk of excessive analytical repetition of the same territorial configurations. In response to this issue, we propose combining the definition of the catchment areas—modulating their size based on the mode of transfer and the type of rail service—with a partition into polygonal patterns used to delimit each catchment area.

Assuming that intermodal cyclists, entering or leaving a train station, tend to choose the shortest path (Héran, 2009, p. 116), based on the results of our survey (Moinse and L'Hostis, 2024, p. 8), we opted for a partitioning of the regional rail network to define the areas that are theoretically closest, from a geometric perspective, to each station. This division, based on a Euclidean plane, allows us to model and visualize the theoretical maximum coverage of each station's catchment area (Moinse and L'Hostis, 2024, p. 8), represented by a cell derived from a standard Voronoi diagram (Mota et al., 2014, p. 479). This approach, which

<footnote n="13">¹³The time propagation method is used to determine the areas that can be accessed from a given point within a certain time, from a multimodal perspective. Starting from a point called the "source," various travel times are calculated based on the constraints of each mode of transportation, such as schedules, frequency, or speed. The modeling of the allotted time budget also considers transfer breaks, such as transfer and waiting times. In this way, the time propagation approach provides influence areas that are adapted to real-world conditions.</footnote>

<footnote n="14">¹⁴We define the *Euraflandres* hub as a merger between the Lille Flandres and Lille Europe stations. These two hubs are located 500 meters apart from each other, thus forming a true "station orchestra" (Le Bot, 2019, pp. 415-416). This is a remarkable example of station-to-station cooperation, and this configuration could be described as the "Flandres hub," representing the "ultimate" evolution of metropolitan station expansion, beyond the mere scale of passenger buildings. This "hybrid" territorial configuration serves as a showcase for the agglomeration and the region (Heddebaut and Di Ciommo, 2018, p. 2). This configuration, described as having "international dimensions" (Bertolini, 1996, p. 334) is part of a larger urban project, including the pedestrianization of Place des Buisses and the enhancement of Place François Mitterrand, now grouped under the name *Euraflandres*. This designation aims to characterize the functional and spatial links between the Lille Flandres and Lille Europe exchange hubs and the Euralille CBD (ADULM, 2017, p. 71).</footnote>

<footnote n="12">¹²The scientific literature seems to agree on the threshold for the social acceptability of distances, placing this criterion at the 75th percentile for walking and at the 85th percentile for non-motorized and motorized vehicles. According to Zuo et al. (2018, p. 5), the distances to public transport stops deemed acceptable by users correspond to this threshold in relation to the intermodal use of bicycles. J. Lee et al. (2016, p. 982) argue that the influence of public transport nodes for cyclists should be considered from the 85th percentile of the observed distances, asserting that cycling behaviors are closer to those of car use than walking, traditionally evaluated at the 75th percentile (O'Sullivan and Morrall, 1996, p. 23). Many studies on M-TOD refer to this threshold to calibrate their analyses (Hu et al., 2022, p. 4; Wei Li and Joh, 2017, p. 3491; Ma et al., 2018, p. 64; Rabaud and Richer, 2022, p. 62; Moinse and L'Hostis, 2024, p. 7).</footnote>
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avoids any form of overlap, is based on the assumption that each cell corresponds to a zone where all points are closer to the associated station than to any other, regardless of the hierarchical structure of the rail network (Lebedeva et al., 2018, p. 429).

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## Morphology and Boundaries of Station Neighborhoods

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Even today, most NPM and more largely TOD studies—whether academic or operational—rely on the use of theoretical circles (buffers) to represent the catchment areas of public transport stations. These buffer zones are typically drawn as circles at a fixed distance, either spatial or temporal, around transport nodes, without taking into account surrounding networks or geographical obstacles. This method has the advantage of being simple to produce and easy to interpret, as it requires neither complex data nor advanced computations. However, this technique for delineating station neighborhoods faces major limitations. By relying on straight-line distance representations, it disregards both transport networks and the geographical constraints of the physical environment. As a result, buffer zones fail to reflect the actual accessibility of infrastructures. Such simplification may lead to inaccurate conclusions, especially in contexts where the configuration of networks and territorial features plays a decisive role in defining station neighborhoods.

From this perspective, isochrones are often considered a more realistic representation of accessible areas. Unlike buffer zones, they delineate the spaces that can be reached within a given time frame, from or to a station, while accounting for infrastructures and modes of transportation. By incorporating the road network and the specific characteristics of various modes of travel—primarily their range, speed, and the types of areas they are allowed to traverse—isochrones offer a more accurate depiction of accessibility. Thanks to their ability to reflect actual spatial dynamics, isochrone modelling constitutes a precise tool for evaluating effectively accessible areas.

We favored the combined use of buffer zones and isochrones. Their integration within spatial analysis provides a comprehensive perspective on accessibility. By superimposing isochrones onto buffer zones, it becomes possible to assess accessibility differentials—that is, the gap between actual and theoretical accessibility (see Fig. 6, p. 11). This comparison also enables the calculation of a service coverage rate, which expresses the ratio between the area that is actually accessible and the area reachable in a straight line (Héran and Pouillaude, 2009, p. 13). The cartographic diagnosis of these various representations of station neighborhoods makes it possible to systematically identify the obstacles encountered by travellers.

Although the advantages of isochrones are widely acknowledged in research practices, their outputs often vary significantly and frequently lack transparency regarding the underlying modelling parameters. This opacity can be attributed to the fact that most isochrones are generated using online platforms that restrict control over modelling settings. In response to this issue, we developed our own station neighborhood delineations using a custom-made script, allowing us to define geographic boundaries tailored to our specific research objectives.

A thorough reflection is required to establish appropriate cartographic representation strategies for accessibility areas. This approach is not merely a matter of aesthetics or communication; it also aims to ensure both conceptual and visual control over the geographical foundations underpinning

the entire analysis. As a first step, it is essential to clarify the concepts involved, notably by identifying the parameters that influence the structure of these representations and the analytical interpretations that follow. Given the wide range of available tools for generating buffers and isochrones, we chose to carry out our analysis using the Python programming language. This decision is based not only on the richness of its available libraries, but also on the transparency afforded by this open-source environment, which allows for complete control over the defined parameters.

An initial parameter that challenges the geographical construction of station catchment areas lies in the directionality of flows. At first glance, it may seem intuitive to assume that, regardless of the flow direction—whether the trip involves approaching the station (inbound) or departing from it (outbound)—the station neighborhood would retain a similar size and shape. However, this assumption tends to overlook two key dimensions: road network connectivity, which is especially critical for cycling and governed by traffic regulations, and temporality. Indeed, an isochrone drawn toward or from a station does not necessarily hold the same validity depending on factors such as service frequency, operating hours, or served directions. In the context of our empirical material, we chose to consider that, by default, both buffers and atemporal isochrones are designed to analyze areas accessible to the station, rather than from it, which is more commonly the case. This choice is justified g given that cycling, when used in connection with rail service, is far more frequently employed for access than for egress.

The issue of cartographic representation of isochrones—whether from an analytical or communicative perspective—also involves choices regarding which structuring elements should be emphasized. Should the focus be on a visual representation of the accessible versus non-accessible surface areas? Or would it be more relevant to highlight the street network, which materializes the connections used as the basis for isochrone computation? Another option would be to emphasize the built environment or the Points of Interest (POIs), which ultimately represent the destinations individuals seek to reach. These cartographic choices are not neutral, as they shape both the interpretation of the data and the way the results are communicated to various target audiences<fnref n="15" />.

From this perspective, we chose to combine the various forms of cartographic representation explored throughout our work by producing hybrid isochrones: primarily surface-based, yet whose shape is determined first and foremost by the street network, while also being influenced by the surrounding built environment (see Fig. 7, p. 12). This technique enabled us to generate cartographic representations with significantly more accurate contours than the approximations often observed in traditional isochrone outputs. In this approach, we relied on the innovative method developed by Butts (2017), based on the OSMnx tool, specifically designed for modelling street networks (Boeing, 2017, p. 132). This method aligns with the principle of an isochrone map built on the street network (lines), following a graph-based logic (edges), while incorporating a linear buffer zone around each accessible segment and intersection (buffers). This process thus makes it possible to integrate urban elements adjacent to the street

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<footnote n="15">¹⁵From a comparative perspective, isochrones traditionally represented as accessible surface areas offer the advantage of high visual clarity, generally require a limited amount of geographic data, and are well suited for broad-scale analyses. In contrast, representations based on the street network—although more precise—are geared toward more specific uses such as navigation, which are less relevant to our research objectives. Finally, representations focused on the built environment or on accessible facilities and services within a given area have the merit of being more user-centered. They are particularly appropriate for urban analyses, especially those concerning urban form and accessibility to targeted services. However, this type of cartographic representation is more demanding in terms of data requirements and involves higher mapping costs.</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Spatial Distribution of Station Areas Accessible by Walking and Cycling in the Hauts-de-France Region.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

network (Boeing, 2017, p. 135).

## -2.3 Geostatistical Analysis of Mapped Station Neighborhoods

The effort devoted to the geographical delineation of station neighborhoods with varying sizes and shapes would be meaningless if the integrated data were aggregated at overly broad scales, such as that of the municipality. Restricting analysis to such administrative boundaries would neither reflect the individuality of each station area nor capture distinctions between different types or internal variations. In response to this issue, we established a methodological rule to rely exclusively on disaggregated datasets, in the form of point, line, or polygon vectors, independent of any grids or administrative borders. However, due to the limited availability of open-access data—despite France's

advantageous position thanks to a strong data centralization—it is sometimes necessary to use aggregated data, particularly for anonymization purposes. Consequently, in a second phase, we propose a method for estimating values based on data aggregated at the scale of regular grids. This approach makes it possible to circumvent these limitations while maintaining sufficient analytical precision to meet the needs of our research.

### Desaggregated Geodata

The importance of adopting a detailed reading of station neighborhoods—independent of municipal boundaries—has been widely emphasized (Moretti and Vacheret, 1999, p. 7). The data used in our study come from various official sources, combined to form a coherent dataset that can be processed within a GIS environment or through computer code to generate graphical outputs suitable for spatial analysis. In our case, geocoding refers to scripts written in Python. These sources include open-access data,
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Cartographic Representation of the Isochrone Accessible by Cycling to Amiens Station, Overlaid with the Road Network.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

disaggregated socio-economic databases, and cadastral records. This data integration ensures a detailed and contextualized representation of station neighborhoods.

Some data grids, although essential to this research, are unfortunately not available in disaggregated form. To address this limitation, we chose to rely on databases expressed through standardized grids<fnref n="16" />.

## Integration of Regular Geographic Data Grids

To reduce the approximations associated with applying data grids, we implemented a calculation method aimed at enhancing their accuracy and ensuring their relevance within the framework of our study. We relied on a *ModelBuilder*<fnref n="17" /> developed in *ArcGIS*, as part of the SOFT research project (Bonin et al., 2020, p. 123). This model generates buffers while adjusting calculations based on the surface areas of the grid cells, whether partially or fully contained within the buffer. In practice, some cells may have only a fraction of their area included in the buffer zone. The proposed method then consists in estimating an adjusted value for each cell to better reflect the reality of the catchment area, accounting for the effective proportion of surface included. Following this

approach, we adapted and systematized the methodology within our own scripts. This adaptation, applied automatically to the buffers and isochrones generated, makes it possible to refine calculations whenever a data grid is used.

# —3 Materials and Methods

The exploration of the scientific literature on NPM highlights how this quantitative approach has evolved as research progresses. This technical framework, whose main goal is to classify nodes and their surroundings to identify TOD potential, continues to improve by progressively integrating new dimensions related to the concept of urban planning, enriched by increasingly in-depth knowledge. Even though new indicators are proposed and integrated into the developed models, our first section identified certain gaps. These limitations concern, on one hand, the delineation of restricted geographic perimeters around station areas, based on a limited conception of walk-and-ride. This research practice also supports the near-total absence of models accounting for the contribution of cycling. On the other hand, at a larger scale, the defined geographical areas are often limited to a single municipality or agglomeration. Furthermore, the lack of case studies based in France, despite the wealth of open databases, calls for such an investigation on this area, aiming to capture national or local similarities or even specificities.

Building on this observation, we opted for an innovative approach by choosing to explore an uncharted territory, while integrating the latest contributions related to NPM. This includes a variety of indicators introduced in the most recent models published, as well as original parameters focusing on the still under-documented integration of cycling. In other words, the modeling we adopted relies on a revised version of NPM, incorporating the contribution of cycling. This approach aims to determine whether nuances emerge in comparison to walk-and-ride, within the expanded context of a region.

In this regard, the originality of our NPM lies in the integration of two innovative dimensions. On one hand, we ensured the inclusion of the links between the station object and the urban object in the form of public spaces. On the other hand, we aimed to connect these themes with the mobility demand expressed through the dynamic ridership of public transport stations. Thus, four dimensions structure the modeling we have chosen to name NPART.

## —3.1 Selection of Synthetic Indicators to Quantify and Characterize the Four Modeling Dimensions

### Quality of Rail Service (N)

The first dimension, related to "quality of rail service," is divided into four categories: "level of service," "system performance," "network metrics" from a graph perspective, and "accessibility to centralities" in Lille and Paris (see Table 1, p. 13). The aim of this first dimension, in line with the original NPM, is to represent the rail service denoted by N in the region, by examining its various aspects structured around sixteen key indicators. It should be noted that, unlike

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<footnote n="16">¹⁶Data grids come in several types, distinguished by their geometry and scale. Among the most common are regular rectangular or square grids, frequently used for demographic and climate datasets, as well as hexagonal grids (hexbins, H3), characterized by hexagon-shaped cells that offer more homogeneous territorial coverage due to their equidistant edges. Conversely, this research excludes the use of irregular grids, such as administrative or geodesic grids, as well as raster formats.</footnote>

<footnote n="17">¹⁷A *ModelBuilder* is a visual tool designed for the creation, automation, and execution of geospatial processing chains. It functions as a graphical programming language, where users assemble tools and processes in the form of diagrams to create reproducible, adaptable, and editable workflows. This approach facilitates the standardization of analyses and streamlines complex operations while ensuring their traceability.</footnote>
the other three dimensions, the values for each of the synthetic indicators are similar across the four types of geographic perimeters studied (PI, PB, CI, and CB).

This first category, part of the “Transit” component of Transit-Oriented Development, aims to comprehensively assess the quality of service offered by the rail infrastructure. It is established that the frequency of public transport networks significantly influences station ridership (Sung and Oh, 2011, p. 79). Similarly, Kashfi et al. (2015, p. 63) emphasize that the intensity and operating hours of services promote increased ridership. In our modeling, we quantified the service level by defining the frequency of high-speed and regional train networks (N₁, N₂, N₃, and N₄), as well as the operating hours (N₅ and N₆). We also considered the system performance, including commercial speed (N₇), which influences passenger volumes (Vuchic, 2007, pp. 149-201) and the number of directions served (N₈), identified as a key indicator in various NPM models for station ridership (Cao et al., 2020, p. 9). We explored the network structure through graph analysis, evaluating aspects such as degree centrality<fnref n="18" /> (N₀) and closeness centrality<fnref n="19" /> (N₁₁), which, according to Shi et al. (2018, p. 14), play a crucial role in station attractiveness, as well as betweenness centrality<fnref n="20" /> (N₁₀), which is associated with an increase in the number of passengers (Jang and An, 2022, p. 339). Finally, the last branch examines accessibility to urban centers, focusing on distances to urban poles (N₁₃, N₁₄, N₁₅, and N₁₆), which are inversely correlated with station attractiveness (Chao Liu et al., 2016, p. 8; Kuby et al., 2004, p. 241).

By proposing these sixteen indicators for the dimension related to rail service, our model draws inspiration from previous NPM models while refining them. This first dimension, focusing on regional mobility infrastructure and service, seeks to address the three major challenges faced by the development of public transport accessibility models: system performance, the configuration of the public transport network, and access to various destinations (Malekzadeh and Chung, 2020, p. 744).

From the perspective of rail service quality, we first sought to broaden the temporal scope of statistical analysis by projecting more complex graphs that go beyond the simple consideration of a single time interval or a single day, as is the case in almost all the studies reviewed. Indeed, most of them calculate these variables based on a typical day, usually a Tuesday, or even just a single time slot, most often during peak hours. While frequency is among the most commonly used indicators, the originality of our model lies in the distinction between different public transport systems, as well as the separation between weekdays and weekends. This concern for differentiation is reminiscent of the models developed by Ivan et al. (2012, p. 149), who isolated the frequency of trains from that of suburban trains, trams, and buses, as well as by Nigro et al. (2019, p. 117), whose frequency was evaluated based on work and vacation periods.

Similarly, the number of stations accessible within a given distance-time has been widely incorporated into NPM models. However, we chose to extend the time interval, which is regularly limited to twenty minutes and seems inadequate for a regional rail system. Regarding the time span between the

first and last trains in service, to the knowledge of the authors, only one model considers this aspect (Caset, 2019, p. 132). The issue of the commercial speed of trains was addressed by building on the work of Debrezion et al. (2009, p. 271) and Zheng and Austwick (2023, p. 6), who focused on the theoretical speed of trains. To this end, we chose to calculate the actual average operational speed on the commercial network, rather than using theoretical speed.

While the quality of rail service is generally well accounted for in previous models, it appears that measures related to network configuration are rarely used appropriately (Papa and Bertolini, 2015, pp. 72-74). However, as demonstrated by Dou et al. (2021, p. 8), who introduced this concept in a third dimension called “Node-Place-Network” (NPN), it proves effective in better understanding the performance of the studied network. With this in mind, we integrated three indicators: degree of centrality, betweenness centrality, and closeness centrality, which, based on our observations, have not yet been jointly analyzed in existing studies.

## ID Node Dimension

### Level of Service

N₁ Daily frequency of high-speed rail on weekdays

N₂ Daily frequency of high-speed rail on weekends

N₃ Daily frequency of regional rail on weekdays

N₄ Daily frequency of regional rail on weekends

N₅ Operating hours on weekdays

N₆ Operating hours on weekends

### System performance

N₇ Commercial speed

N₈ Number of directions served

### Network metrics

N₉ Degree centrality

N₁₀ Betweenness centrality

N₁₁ Closeness centrality

### Accessibility to centralities

N₁₂ Number of stations accessible within one hour

N₁₃ Number of stations to reach Lille's urban core (Euralille hub)

N₁₄ Travel time to reach Lille's urban core (Euralille hub)

N₁₅ Number of stations to reach Paris's urban core (Châtelet hub)

N₁₆ Travel time to reach Paris's urban core (Châtelet hub)

<caption>**Table 1.** Set of Indicators Grouped within the Dimension related to Node (Transit).</caption>

Authors: Moinne, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

## Degree of Urban Development (P)

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The second dimension, related to “degree of urban development,” is divided into five categories: “density,” “functional diversity,” “attraction sites,” “land value,” and “demographics,” (see Table 2, p. 14). The aim of this category, also in line with the original NPM, is to measure urban forms, facilities, and territorial attractiveness, as well as the sociological profile of resident populations, denoted by P, by examining its various aspects structured around thirteen key indicators.

We incorporated and developed indicators already present in most NPM models, such as population and employment

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<footnote n="18">¹⁸Degree centrality measures the number of direct connections a node has with other nodes in a network. The more connections a node has, the higher its degree centrality. By direct connections, we mean the number of stations reached by rail services, without transfers, from the starting station.</footnote>

<footnote n="19">¹⁹Closeness centrality measures how close a node is to all other nodes in a network. It is defined as the inverse of the sum of the shortest distances between a node and all other nodes.</footnote>

<footnote n="20">²⁰Betweenness centrality quantifies how often a node appears in the shortest paths between other nodes. A node with high betweenness centrality plays a crucial role as a mediator in a network.</footnote>
density, as well as the functional diversity of territories, in addition to new variables. The use of POIs refers to relatively few studies that have considered these urban elements generating flows (Robillard et al., 2024, p. 82; Zhou et al., 2023, p. 2). Furthermore, we chose to reflect the diversity of POIs<fnref n="21" />, following the recommendations made by M. Zhang and J. B. Lee (2023, p. 7).

Although the Transportation Research Board of the National Academies (2007) recognizes land value as one of the ten key indicators in building an NPM model, few studies have addressed this issue so far. Kim et al. (2018, p. 135) suggest examining property values around stations to assess urban dynamics, although obtaining these data presents certain challenges. Ibrahim, Ayad, Turki, et al. (2023, p. 242) were able to capture the land value-added resulting from private investments in Alexandria. However, Singh et al. (2014, p. 263) raise concerns about the need to improve the accuracy of statistical data related to investments in order to enrich such a model.

In addition to these reflections, the issue of social housing supply, in relation to land pressures, is raised (Singh, 2015, p. 27). This socio-spatial dimension is largely underrepresented in the literature, except for the model undertaken by Zhou et al. (2023, p. 2). Finally, the consideration of household economic resources, particularly income, remains an underutilized indicator in this model, despite being an important factor according to the results derived from the modeling process of Cummings and Mahmassani (2022, p. 3).

## ID Place Dimension

### Density

*P₁* Population density

*P₂* Employment density

### Functional diversity

*P₃* Residential land use

*P₄* Commercial and public services land use

*P₅* Office and industrial land use

*P₆* Green space land use

### Points of interest

*P₇* "Proximity" points of interest

*P₈* "Intermediate" points of interest

*P₉* "Superior" points of interest

### Attraction sites

*P₁₀* Land value of residential properties

*P₁₁* Land value of industrial, commercial, and office properties

### Demographics

*P₁₂* Share of social housing

*P₁₃* Average household income

<caption>**Table 2.** Set of Indicators Grouped within the Dimension related to Place (Development).</caption>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

## Design and Public Space Management (A)

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The third dimension of this study, labeled "local accessibility and public space management" and falling under the "Oriented" component of Transit-Oriented Development, focuses on examining the interactions between urban development and the mobility system. It specifically explores link scale, investigating the quality of urban design and its contribution to the qualification of the built environment (Yushan Zhang et al., 2023, p. 294). This dimension encompasses the concepts of walkability, bikeability, and local connectivity in relation to urban public transport networks, as well as demand management, focusing on the space allocated to private car (see Table 3, p. 15). It should be noted that the last category is approached from the perspective of car competitiveness relative to public transport use, in line with TOD principles. Thus, the values assigned to the presence of cars in urban space are inverted in the modeling: the greater the importance given to cars, the lower the station area's ranking relative to others. Through this component, labeled *A*, eleven indicators have been integrated in total to assess the four defined geographic perimeters (*PI*, *PB*, *CI*, and *CB*).

Existing documentation has revealed a substantial positive association between the indices related to the node, place, and design, thus highlighting the determining influence of place connectivity, which manifests through pedestrian and cycling links around mobility spaces and human activities (M. Zhang and J. B. Lee, 2023, pp. 4–5). The morphological and functional interrelationship between transport and land use is therefore fundamental in this model (Z. Li et al., 2019, p. 271), as this dynamic interaction within the station area must be considered as an independent variable (Peek et al., 2006, p. 20).

Although walkability is frequently integrated into NPM models, indicators related to bikeability are much rarer. Some research published by Reusser et al. (2008, p. 195), Lukman and Singh (2014, p. 26), Huang et al. (2018, p. 312), Maheshwari et al. (2022, p. 1.018), Arliani et al. (2023, p. 10), M. Zhang and J. B. Lee (2023, p. 6), and Robillard et al. (2024, p. 82) have explored this aspect by examining the length of cycling networks and the number of bike parking facilities.

Furthermore, we chose to focus on the car system, particularly speed limits and car ownership rates, which have been scarcely addressed in previous studies, while considering all car parking areas within the territory, without limiting the study to park & ride. In this sense, we opt for an inversion of the values associated with car usage measurement, considering that the development of the car system contradicts the principles of TOD. This approach is supported by Maheshwari et al. (2022, p. 1.020), who demonstrated that car parking density is inversely proportional to metro ridership in Ahmedabad, India.

## Ridership per Time (RT)

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The fourth dimension of our study, labeled *RT*, models station ridership, an indicator directly linked to mobility demand. Based on the assumption that station classification only gains legitimacy when it is based on their size, i.e., their ridership, allowing for a comparison based on relative "accessibility," we opted for the innovative addition of this component (Reusser et al., 2008, p. 194). To assess the ridership of each station, we extracted data from SNCF (2024) open data. This database provides information on the number of passengers per year and per station. In order to

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<footnote n="21">²¹These POIs are taken from the *Base Permanente des Équipements* (BPE), a database that lists a wide range of facilities and services—both commercial and non-commercial—available to the public throughout France (Insee, 2021). Our classification of POIs into three levels of importance corresponds to the categories defined by the BPE. "These groupings make it possible to construct summary indicators that reflect the hierarchical organization of territories in terms of services available to the population". (Insee, 2021). From the 188 listed facilities in the BPE, we chose to exclude twelve categories that, in our view, are not relevant to our research topic: automobile and agricultural machinery repair (A301), automotive technical inspections (A302), professions of masonry (A401), plasterer, painter (A402), carpenter, locksmith (A403), plumber, roofer, heating technician (A404), and electrician (A405), as well as general building companies (A406), ambulances (D303), taxis, ride-hailing service (F101), train stations (E107, E108, and E109), and sports and health courses (F109).</footnote>
## ID Accessibility Dimension

### Walkability

A₁ Length of pedestrian network

A₂ Intersection density

A₃ Spatial efficiency rate (pedestrian and cycling isochrones)

### Bikeability

A₄ Length of cycling network

A₅ Capacity of bicycle parking facilities

A₆ Public bikeshare fleet

### Local connectivity

A₇ Metro and tramway stops

A₈ BRT and bus stops

### Travel demand management

A₉ Motorized speed limit

A₁₀ Area of car parking facilities

A₁₁ Household motorization rate

<caption>**Table 3.** Set of Indicators Grouped within the Dimension related to Accessibility (Oriented).</caption>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

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make the model more precise and dynamic, the lack of real-time traffic data led us to estimate the passenger flow for each station at daily intervals and hourly slots. We chose to use the ridership data published by Google Maps (2024), known as GPT<fnref n="22" /> for the week of March 18 to March 24, 2024. Thus, we were able to determine the estimated ridership for each station at each hour over a typical week<fnref n="23" />, unaffected by holidays, public holidays, or local or national disruptions.

We carried out the automated extraction and organization of data from this online site, concerning visits recorded by location and search type. To this end, we developed and implemented a Python script to collect the total flow by hour and by day for each station. Based on estimated trips, we segmented time into six distinct periods, applying a classification method that allowed us to group hourly ridership based on observed similarities.

The first five periods, from RT₁ to RT₅, are dedicated to weekdays, while the RT₆ period is reserved for Saturday and Sunday. The periods of high ridership are designated RT₂ and RT₄, while off-peak hours are identified as RT₁, RT₃, RT₅, and RT₆. This statistical method allows us to precisely characterize the ridership of each station according to these six time intervals. Thus, this dimension illustrates the mobility demand, which we treat, in this analytical framework, as dependent variables, influenced by the forty other indicators considered as independent. It should be noted that the values associated with the resulting time segments were normalized.

The determination of hourly ridership patterns for stations during a specific week provides a precise insight into the mobility behaviors of rail passengers. As shown in Fig. 8 (p. 16), weekday activity is strongly marked by commuting,

with significant peaks in ridership between 6:00 and 9:00 AM and between 3:00 and 7:00 PM. This trend results in generally higher ridership during these days. In contrast, weekend ridership profiles show more moderate variations, with secondary peaks at noon and between 4:00 and 7:00 PM on Saturday, and between 3:00 and 6:00 PM on Sunday<fnref n="24" />.

Although station ridership is known for a specific week, this information remains both limited and static. To address this challenge, prediction allows us to go beyond this snapshot by anticipating the evolution of usage patterns and external contexts. The value of predicting station flows lies, from a statistical perspective, in the continuous improvement of the model, and from a planning perspective, in optimizing decision-making.

The prediction of passenger volumes primarily aims to anticipate flows in order to highlight potential levers for encouraging modal shift to public transport, while enhancing its management. The development of predictive models addresses the need to continuously improve these models and better predict fluctuations due to events, seasonal variations, or long-term trends. To this end, we applied several statistical techniques from Machine Learning by adopting an Ensemble Learning model<fnref n="25" />, after normalizing the collected data:

* Multiple Linear Regression (MRL) models the linear relationship between explanatory variables and the target variable, i.e., the number of rail passengers;

* K-Nearest Neighbors (KNN) predicts ridership by identifying the k most similar moments in the past and calculating an average of the corresponding riderships;

* Artificial Neural Networks (ANN) capture complex and non-linear relationships between variables, inspired by the architecture of the human brain.

The evaluation of these models was carried out using metrics based on Mean Squared Error (MSE)<fnref n="26" /> and Adjusted Prediction Error Reduction (APRE)<fnref n="27" />. In both metrics, a low value indicates a performant model, capable of predicting real values accurately while minimizing relative errors.

The results obtained from these predictive models indicate that all three Ensemble Learning techniques significantly reduce APRE, suggesting an improvement in performance, particularly for stations with low ridership. While the KNN method shows satisfactory performance before the weighting of explanatory variables, both MLR and ANN models demonstrate notable improvements after this weighting is applied. We compared the evolution of MSE and APRE values across different geographic perimeters and periods. This analysis showed that KNN and the three Ensemble Learning-based predictive models provide a more accurate fit in predicting station ridership. It appears that these predictive models particularly benefit less frequented stations, for which the accuracy of the forecasts is improved.

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<footnote n="24">²⁴Wednesday also stands out due to an intermediate peak between 10:00 AM and 12:00 PM, likely due to the afternoon traditionally given to children, while Friday records a more pronounced peak in ridership during the evening rush hour. The identification of an intermediate peak in our observations is supported by the distribution analysis of trips conducted by Munch (2017, p. 66) on the SNCF Transilien network for 126 stations in 2015.</footnote>

<footnote n="25">²⁵Ensemble Learning involves combining multiple models, known as "weak learners," in order to improve accuracy over a single model. This Machine Learning approach enhances overall performance by reducing generalization error, i.e., the difference between performance on training data and new data (i), model variance by smoothing individual predictions (ii), and model bias (iii).</footnote>

<footnote n="26">²⁶MSE is a commonly used metric for measuring the gap between observed values and predicted values by a Machine Learning model, particularly in regression models. One drawback of this measure is that it amplifies large errors due to squaring the differences, making the model more sensitive to outliers.</footnote>

<footnote n="27">²⁷APRE quantifies the relative error between observed and predicted values, normalizing it by the actual values. This metric is particularly useful for evaluating the accuracy of prediction models when data exhibit significant variations in amplitude. Unlike MSE, APRE is less sensitive to outliers as it takes into account the scale of the actual values.</footnote>

<footnote n="22">²²Given the scarcity of public data on this subject, this innovative method has certain limitations, notably that the recorded visits do not necessarily reflect the actual ridership at the station, which would ideally be measured by physical counters or validated tickets at the station. However, to validate this approach, we compared the results obtained with our regional quantitative observations ((Moinse, 2025a)), finding a certain consistency between the two datasets.</footnote>

<footnote n="23">²³In 2023, Vongvanich et al. (2023, p. 5) reused GPT data as innovative resources, available in many cities around the world. They used this real-time data to analyze and predict ridership in ten stations in Kyoto, Japan, and also explored the number of visits to surrounding services and businesses. To our knowledge, this is the only scientific publication that has used GPT data to determine ridership at public transport stations.</footnote>
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Estimation of the Total Ridership for all the Stations Examined, by Hour and by Day, between March 18 and March 24, 2024.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

After defining the NPART based on a set of indicators organized around four key dimensions – the Node, Place, Accessibility, and Ridership – we focus on describing the steps involved in calibrating the model. The objective is to adjust and validate

the parameters of the tool used.

## -3.2 Data Collection and Analysis Protocol

The design of our NPART model is based on the selection of forty quantifiable explanatory indicators and six indicators related to station ridership. The goal is to classify and characterize the stations and their immediate environment within the framework of developing an M-TOD, for which a methodological protocol has been established. This includes data collection and geostatistical analysis. This analysis protocol led to the generation of 288 specific typologies, depending on several factors: the analyzed geographic area (PI, PB, CI, and CB), the periods studied (from RT₁ to RT₆), variable weighting strategies, and classification methods applied to the stations (see Fig 9, p. 17).

### Data Collection Techniques and Validation

This sub-section describes the steps of collecting and analyzing statistical data related to stations as well as geographical information, in connection with the defined isochrones and buffers. The data preparation preceding the modeling work is primarily based on the search for disaggregated data sources, which are then transformed and scaled to ensure their consistency. Furthermore, the model validation is based on the analysis of interdependencies between the different indicators. Following this statistical analysis, we can adopt a classification approach based on various parameters outlined.

Once the indicator grid was defined in detail, we proceeded with the spatial extraction of the available data. For each exchange point, we selected the defined station areas, integrating both isochrones and buffers for pedestrian (PB and PI) and cycling (CB and CI) areas. We deliberately restricted ourselves to the use of disaggregated geographic data to maximize the precision of the location of the determined variables, except the node-related values, which rely exclusively on GTFS data provided by institutions and mobility managers. Furthermore, this spatial granularity requirement led us to adopt data estimation methods when data is aggregated within spatial grids.

In this regard, we chose to use the programming language Python exclusively, particularly its various libraries dedicated to geospatialization to extract and analyze the data. This choice is explained by several comparative advantages over the traditional use of GIS<fnref n="28" />.

The normalization of input data is a critical step before any modeling, a fortiori in the context of NPM. In this context, the data collected for each station must be homogenized to prevent the scale differences between variables from disproportionately influencing the model's results. To this end, the Min-Max normalization method was applied before the training phase. This technique involves transforming the values of each variable so that they fall between 0 and 1.

After the data collection and normalization, it is important to ensure that the transformed variables are ready to be integrated into the model. A key preparatory step before

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<footnote n="28">²⁸First, Python offers greater flexibility in data processing, being both more adaptable and easily customizable. Additionally, it allows for the handling of large datasets and the automation of data extraction and transformation processes through the various scripts generated. Moreover, Python facilitates complex calculations, enabling the retrieval of required data, particularly when combinations of diverse datasets are needed. Finally, the analysis of GTFS data is greatly simplified, especially by allowing the modeling of complex networks and graph analysis.</footnote>
<figure><img src="image_11.png" /><figcaption>Fig. 9. Flow Diagram Representing the Main Methodological Steps of the Spatial Model.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)
modeling is to check for multicollinearity. The presence of multicollinearity can compromise the interpretation of the model's results and hinder its accuracy, particularly in analyses based on relationships between variables. Therefore, before moving to the actual modeling phase, a multicollinearity analysis is essential to validate the consistency of the data and ensure their relative independence.

We assessed multicollinearity between the independent variables using two statistical approaches: the simple correlation coefficient, employing Pearson's correlation coefficient $R^2$, and the variance inflation factor (VIF). By combining these two methods, we can identify and remove independent variables exhibiting very high collinearity issues. Appendices (p. 40 and 41) illustrate the interrelationships between the explanatory criteria.

As a general rule, we consider an independent variable to pose a significant collinearity issue when $R^2$ reaches 0.90 or when VIF exceeds 100.00. In consequence, the variables we may consider excluding are high-speed rail frequency ($N_2$), betweenness centrality ($N_{10}$), the "proximity" POIs ($P_7$), and the car ownership rate of households ($A_{11}$) for all station area configurations ($PB$, $PI$, $CB$, and $CI$). The method used to adjust this model is Ordinary Least Squares (OLS) regression. This technique aims to determine the optimal regression line by minimizing the sum of squared differences between the observed and predicted values. Mean Squared Error (MSE), is used to assess the accuracy of the model's predictions and, thus, measure its overall performance.

After eliminating the indicators $N_2$, $N_{10}$, $P_7$, and $A_{11}$ for each geographic and temporal perimeter, the parameters of the different regression models showed that the adjustment performance barely changed. As a result, we decided to retain all of the initially defined indicators since, from a geographic perspective, they more accurately reflect the complexity of urban reality. In this regard, we draw on the conclusions of Cao et al. (2020, p. 16), who demonstrate that the NPM model, which eliminates variables with too high a VIF, ultimately proves less accurate than the complete model that retains all indicators.

After performing the analysis of multicollinearity among the variables, it is now possible to proceed with clustering and then classifying the stations. Proper management of multicollinearity is a key prerequisite to ensure a reliable and interpretable typology, resulting in homogeneous and representative groups.

## Double Exploratory and Predictive Approach by Partitioning and Classification

Classification, as an analytical framework, aims to group stations into different homogeneous sets based on similar characteristics, to better guide urban planning decisions (Cao et al., 2020, p. 2). The main objective of this approach is to organize a set of data into clusters<fnref n="29" />, in order to facilitate understanding and concisely summarize the similarities and differences present in the data. The value of classification often lies in its practical utility rather than its absolute truth (Everitt et al., 2011, p. 4). According to Gordon (1999, p. 5), such an approach helps synthesize complex datasets and aids

in detecting structures and interactions within this information. If distinct classes of objects are identified, they can then be named, and their properties defined, thus facilitating the assignment of new objects. In this sense, the notion of "similarity" linked to classification techniques is both central and subjective, as the definition of clustering algorithms and the interpretation of results depend on the modelers' judgment.

To determine the optimal number of clusters $k$, we chose to use the methods of the silhouette coefficient (SC) and the Calinski-Harabasz index, also called the variance criterion (CH). These two measures are commonly used to evaluate the quality of data groupings in the three considered unsupervised algorithms (k-means, AGNES, and GMM). The silhouette coefficient quantifies the similarity of objects within the same cluster while evaluating their distinction from objects in other clusters. It allows for an analysis of the internal cohesion and separation of clusters, providing a fine evaluation at the level of each data point, with values ranging from -1 to 1. As for the variance criterion, it measures the ratio between inter-cluster variance and intra-cluster variance, with unbounded values. This measure is particularly useful for comparing different numbers of clusters and various clustering models, relying on the global dispersion of points. For all these indices, a higher value indicates better performance.

The goal of these indicators is to identify the point at which adding new clusters no longer significantly improves their values (Jahaniana et al., 2022, p. 23). This point indicates that the clustering model is balanced and that the number of clusters is reasonably chosen. By identifying the optimal $k$ threshold, it is possible to avoid an excess of clusters while ensuring a coherent segmentation of stations and their surroundings. According to our measurements, the optimal number of $k$ is set to three for the three clustering methods. As an illustration, when $k=3$ and the parameters are defined at $RT_2$, it appears that the k-means algorithm presents the best scores in terms of cluster separation, both for $PI$ and $CI$.

Subsequently, the classification of stations, following a clustering step, involves assigning new observations to pre-existing clusters. This phase relies on the use of a supervised prediction method, where the objective is to predict the membership of each new station to one of the clusters defined during the initial clustering phase (James et al., 2013, pp. 134–135). This approach also allows for the continuous integration of new data, keeping the classification updated without the need to restart the entire modeling process. It thus aims to automate the categorization of stations, based on the clusters identified during the exploratory analysis.

To achieve this, we used neural networks. The training of the neural networks was based on consistently labeled data points. As the previous results show, different clustering methods produce varied results. However, we consider that the points consistently labeled by the three algorithms legitimately belong to these clusters. On the other hand, for points labeled inconsistently, it is not possible to directly and explicitly classify them into these clusters. Therefore, we decided to perform a second clustering on these inconsistent data. To do this, we used the consistently labeled data to train the neural network, hoping that it would have a better ability to classify the unclassified points. We allocated 80% of this data to train the model, and the remaining 20% was used as a validation set to test the model's performance. Once the predictions were made by the neural network, each station was assigned to a cluster. This cluster is considered to stem from the combination of the results of the three clustering algorithms, with the model having been trained on

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<footnote n="29">²⁹A cluster is defined as a group of stations sharing common attributes, identified by clustering algorithms based on a measure of distance or similarity in a multidimensional space (Everitt et al., 2011, p. 7). According to Cormack (1971, p. 321) and Gordon (1999, p. 15), a cluster is characterized by its internal cohesion, expressed in terms of homogeneity, and its external isolation, defined by the separation between groups. Indeed, a classification algorithm will theoretically aim to ensure minimal intra-class variability, meaning minimized distance between individuals within the same group, and high inter-class variability, i.e., maximizing the distance between individuals of different groups.</footnote>
data labeled with relatively high accuracy. We therefore consider this classification result sufficiently precise to be compared to the results of the clustering algorithms, thus allowing us to reassess their effectiveness.

To validate the clustering and classification processes, we employed the Adjusted Rand Index (ARI) and the Jaccard Coefficient (JC). These two measures are used to evaluate the similarity between two partitions in order to assess the quality of clustering in relation to a reference classification. The performance evaluation of the Machine Learning model is commonly referred to as ground truth. ARI is an adjusted version of the Rand Index that measures the similarity between two partitions by considering the pairs of elements correctly grouped together or separated in both partitions. The ARI adjusts this measure to account for random matches. It ranges from -1 to 1 and provides a more reliable measure than the base Rand Index, as it incorporates random matches. JC, on the other hand, is a similarity measure between two sets. When applied to clustering, it compares two clusters by measuring the percentage of common elements relative to the total size of the sets. It is particularly relevant when clusters are of different sizes. This indicator, which ranges from 0 to 1, focuses solely on the intersection and union of clusters, without adjustment for randomness. As an example, when k = 3 and the parameters are set to RT₂, it appears that both the k-means and AGNES algorithms perform best overall for both PI and CI. AGNES produces, at this stage, the clusters most similar to the ground truth.

After extracting data related to mobility and urban systems and ensuring their reliability, we applied clustering and classification techniques. These methods allowed us to segment the data into groups and develop predictive models. However, the analysis would not be complete without considering the relative importance of each variable used. By assigning specific weights to the defined indicators, we can adjust their influence on the final results, which improves the accuracy of predictions and better reflects the reality of the phenomena being studied. In the next section, we will discuss the strategy for adjusting the weights of our data, alongside the equal-weight approach.

## Weighting Indicators

Aware of the common practice of assigning equal weight to indicators in the NPM model across various urban contexts, the research conducted by Liao and Scheuer (2022, p. 9) proposes a revision of this approach by considering the relative weight of each indicator. Monajem and Ekram Nosratian (2015, p. 15) observe that the relative importance of the variables integrated into the model has often been overlooked in previous studies, whereas some criteria exert a more pronounced influence than others. This factor plays a decisive role in the calculation of the NPM, as it significantly impacts the classification of stations (Lukman and Singh, 2014, p. 10).

When discussing the relative weight of indicators from a statistical perspective, it is important to refer to the specific effects of each criterion on station attendance. Indeed, attracting individuals to public transport systems is one of the key goals of TOD, justifying the extension of the NPM to analyze the impacts of the various factors considered. In this context, Cummings and Mahmassani (2022, p. 2) applied multivariate regression to identify the factors influencing the attendance of the Amtrak railway network in the United States. Similarly, L. Yang and Song (2021, p. 248) used multi-criteria decision analysis (MCDA) to assign weights to the variables studied on metro station usage in Ningbo,

China. Amini Pishro, L'Hostis, et al. (2023, p. 4) optimized the use of Information Entropy Weighting (IEW) by measuring the relative importance of attributes (Shannon, 1948, p. 4), not only in predicting attendance but also in the final process of node classification.

Driven by the intersection of geography, statistics, and data science, we chose to integrate this recent advancement by adopting the IEW technique in our modeling. This approach aims not only to identify the criteria influencing station attendance but also to more realistically classify the stations within the studied network. In this perspective, within this research, we present the relative weights of each indicator on passenger flows at each station, based on the three independent dimensions: node (N), place (P), and accessibility (A). These results will allow us, in a subsequent phase, to propose a classification of the stations and their surrounding environments, taking into account these various influences, in relation to pedestrian and cycling station areas.

In the context of our multi-criteria model, where the values of each variable have been normalized, the first step consisted in constructing a decision matrix, within which we calculated the entropy for each indicator in each dimension. The entropy calculation formula relies on the weighted sum of the logarithms of the normalized values (Formula 1, p. 19). Ultimately, the detailed calculation of entropy for each criterion allows us to measure the amount of information an indicator contributes to the model. In other words, the weighting of the independent variables depends on their ability to discriminate the rated alternatives i, leading to the analysis of their influence on station attendance.

$$S_j = -k \sum_{i=1}^{n} p_{ij} \ln(p_{ij}). \quad (1)$$

where:

* $S_j$ is the entropy of criterion j;

* n represents the total number of stations;

* $p_{ij}$ is the normalized value of criterion j for station i;

* $k = \frac{1}{\ln(n)}$ is a normalization factor.

The next step consisted in calculating the degree of dispersion, referred to as the degree of diversification, for each criterion. A high variability, denoted as $d_j$, indicates that the criterion provides a more significant amount of information for the model (Formula 2, p. 19). Indicators with greater dispersion are identified as having a stronger influence on the dependent variable, namely the passenger flows.

$$d_j = 1 - S_j. \quad (2)$$

where:

* j represents an independent indicator;

* $d_j$ reflects the variability of criterion j;

* $S_j$ is the entropy of criterion j.

The final step consisted in determining the relative weight of the indicators, denoted as $w_j$, ensuring that the sum of all weights is limited to 1 for each independent dimension (Formula 3, p. 19).

$$w_j = \frac{d_j}{\sum_{j=1}^{n} d_j}. \quad (3)$$
where :

$w_j$ is the normalized weight of criterion j.

# —4 Results and Discussion

Once the model's calibration parameters have been established and the methodological protocol outlined, we present the statistical results derived from its application. First, we seek to identify the most influential factors driving the increase in rail traffic, a critical goal of *Transit-Oriented Development*. Based on this analysis, we highlight the key components on which public policies should focus to achieve a *Micromobility-friendly Transit-Oriented Development* (see the subsection on the *effects of examined factors*, page 20). Next, we assess each station and its urban environment by positioning them within the model's diagram to determine imbalances or mismatches (see the section on the *evaluation of the rail network*, page 21). Finally, this analysis is complemented by a classification process that identifies strategic areas with development potential (see the section on the *regional classification*, page 23).

## —4.1 Influence of Parameters Defining a Micromobility-friendly Transit-Oriented Development

Analyzing the indicators influencing station patronage provides a key lever for understanding the mechanisms underlying their attractiveness and, by extension, their ability to drive positive dynamics in mobility and urban systems. By cross-referencing statistical results with stakeholders' representations, the objective is to identify points of convergence and divergence, thereby providing a fact-based vision of the priorities to be adopted in planning strategies (see Appendix, p. 38).

### Impacts of Node Factors

From the perspective of network structure and station service levels, passenger traffic is primarily determined by the frequency of the high-speed rail network, both on weekdays ($N_1$) and weekends ($N_2$), according to the entropy-based weighting method (see Fig. 10, p. 20). Factors related to rail service frequency (from $N_1$ to $N_4$) also play a predominant role among the criteria evaluated by urban planners. Moreover, transit network connectivity—measured through closeness centrality ($N_{11}$) and degree centrality ($N_9$)—positively influences ridership. Conversely, service schedule temporal coverage ($N_5$ and $N_6$), accessibility to urban centralities (from $N_{13}$ to $N_{16}$), and betweenness centrality ($N_{10}$) exert a lesser influence.

In light of previous NPMs, these statistical outcomes appear consistent with the conclusions highlighted in the literature. Regarding the dimension *node*, Cummings and Mahmassani (2022, p. 8) established that the number of weekly *Amtrak* services in the United States is the most influential variable in

<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Statistical Influence of Criteria on Walking and Cycling Scales on Station Ridership.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

various scenarios analyzed. Similar to Caset, Blainey, et al. (2020, p. 14), our study combined service frequency and destination centrality based on GTFS data for regional trains (TER and TERGV) and high-speed trains (TGV). Therefore, Caset, Blainey, et al. (2020, p. 14) observed that an increase in 1% the frequency of regional trains in Flanders, Belgium, translates into a 0.6% increase in the patronage of the railway network. They also determined that degree centrality is an even more decisive indicator: a 1% increase in its value tends to increase station patronage by 2%. In this regard, Amini Pishro, Q. Yang, et al. (2022, p. 11) identified this metric as one of the seven most influential indicators for boosting passenger volumes in Chengdu. Likewise, Cao et al. (2020, p. 9) emphasized degree centrality and the number of *express* lines as the two most critical variables, in Tokyo.

The fundamental role of rail service frequency illustrates how this indicator can significantly enhance station attractiveness. Simultaneously, the strategic positioning of a node within networks underscores the importance of network connectivity. Closeness centrality corresponds to a classical rail network accessibility indicator, thereby facilitating intermodal transfers, while degree centrality confirms the station's role as a crucial node within the transport network. This suggests that *Transit-Oriented Development* should not be narrowly confined to the immediate surroundings of each station—its local urban environment—as some reductive analyses might imply (Cerema, 2015, p. 10). Instead, a multi-scalar approach is necessary, simultaneously

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considering the different spatial dimensions of the transport network, by rail and other means, and the urban system (Cervero, 1998, p. 11). From this, we can conclude that the primary factors explaining station patronage involve strengthening their nodality<fnref n="30" />, which involves interactions between centrality, accessibility, and attractiveness at different scales (Bavoux, 2005, p. 13).

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## Impacts of Place Factors

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Based on statistical evidence, the urban development indicators exerting the greatest influence on station patronage include the value of real estate designated for commercial and industrial activities ($P_{11}$), “superior” and “intermediate” points of interest ($P_9$ and $P_8$), green spaces ($P_6$), and employment density ($P_2$). In practice, a distinction emerges between walking ($PI$) and cycling ($CI$) catchment areas, with population and job densities ($P_1$ and $P_2$) playing a more significant role in the extended station areas (see Fig. 10, p. 20).

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For the *place* aspect, the results align with the observations of Amini Pishro, Q. Yang, et al. (2022, p. 11), who found that the average price of commercial and office land is positively correlated with station patronage. Regarding POIs, Pezeshknejad et al. (2020, p. 5) observed that areas around BRT stops with a high concentration of POIs tend to attract more passengers in Tehran. More broadly, commercial and office-dominated land use, along with employment density, have been shown to increase Amtrak service patronage (Cummings and Mahmassani, 2022, p. 9).

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The examination of urban development indicators influencing transit ridership highlights the role of urban and economic integration of stations and their surroundings. Commercial and industrial real estate, along with a high concentration of POIs and employment density, emerge as substantial drivers of station attractiveness.

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## Impacts of Accessibility Factors

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Regarding the management of public spaces and local accessibility, statistical analysis indicates that first- and last-mile connectivity issues are the primary factors influencing station patronage. More specifically, urban rail transit services ($A_7$) and three key components of the “bicycle system” (Héran, 2001; Héran, 2015)—the presence of public bicycle-sharing services ($A_6$), the provision of bicycle parking facilities ($A_5$), and the development of a cycling network ($A_4$)—hold a pivotal role. These statistical findings highlight the strategic importance of intermodality in enhancing the attractiveness of the railway network and stations at the local scale (Fig. 10, p. 20).

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These study's conclusions on the *accessibility* component support existing research. Caset, Vale, et al. (2018, p. 511) observed a weak association between active modes and other indicators in their model, with the notable exception of the presence of bike-sharing systems near stations.

Unlike the trends observed in the two previous dimensions, this third category draws attention to the relative importance of indicators that are rarely considered in literature, underscoring the critical issue of the first- and last-mile connection. While the design of public spaces plays a secondary role, it contrasts with the significance of

complementary transport modes to rail, which actively contribute to the development of rail flows. The relative importance of infrastructure and services dedicated to micromobility in station areas illustrates the need for better integration of this intermodal perspective, which is often overlooked in studies. Furthermore, while car parking near stations plays a more moderate role compared to bike-and-ride, it still exerts a significant influence. This suggests the coexistence of three modal chaining solutions: urban public transport systems, micromobility, and private car use. This context reveals a paradox: while it is crucial to reduce both car use and parking to promote a more balanced modal split, it remains essential to avoid discouraging train usage in favor of exclusive car reliance. These findings suggest that priority should be given to active modes while maintaining limited car accessibility in station areas.

The analysis of the individual influence of each indicator on *Transit-Oriented Development* and *Micromobility-friendly Transit-Oriented Development* scales has enabled us to quantify their effects on station attractiveness. To gain a more precise understanding of the development potential offered by public transportation in our regional case study, we will now focus on assessing the accessibility of stations and their immediate surroundings, examining the interactions between the four dimensions of the NPART modal.

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## -4.2 Accessibility Assessment of Stations and their Surroundings

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The next step consists in characterizing the stations and their surrounding neighborhoods in order to assess the “equilibrium” condition of each station’s functional configuration. This section presents a preliminary classification exercise of stations and their surrounding areas based on the values obtained for each indicator, while considering the balance between the three dimensions under analysis: regional connectivity, land use, and local accessibility. To this end, we rely on the notion of an “accessible” area, defined as a condition in which the intensity functions of rail service provision, activity density, and connectivity intensity are adequately coordinated.

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## A Networked of “Accessible” and “Dependent” Station Areas

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Across all the geographic perimeters analyzed, the majority of stations fall into the “dependence” category (see Appendix (p. 42). This is particularly true for both types of isochrones, with the proportion of stations considered “dependent” reaching 96% at the pedestrian scale ($PI$, 306 stations) and 93% at the cycling scale ($CI$, 295 stations), respectively. It is worth noting that no station area is in a state of marked “imbalance” in favor of one of the three dimensions, nor in a situation of “saturation;” however, the station area of *Euraflandres*, not visible in the diagram, tends to approach such a condition.

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At both the pedestrian and cycling scales, only a handful of nodes and station areas can be classified as “accessible” or “balanced,” to use the terminology of Bertolini (1999, p. 202). More specifically, only 12 stations fall into this category for $PI$, while 23 do so for $CI$. Notably, the 12 stations identified as “accessible” under the $PI$ scale are also classified as such under the $CI$ scale. These include the stations of Amiens, Arras, Compiègne, Douai, Dunkerque,

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<footnote n="30">³⁰The concept of nodality refers to “all characteristics related to the morphology, functions, and dynamics of transport nodes [...]” (Bavoux, 2005, p. 5). The author also proposes the term nodosity to describe various aggregation phenomena that may accompany circulatory nodality (Bavoux, 2005, p. 5).</footnote>
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Multidimensional Diagrams Positioning the Examined Stations (Excluding *Euraflandres* Station), based on Pedestrian and Cycling Isochrone Scales.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

*Euraflandres*, Lens, Lille CHR, Pont de Bois, Roubaix, Tourcoing, and Valenciennes (see Fig. 11, p. 22). As can be observed, the stations deemed "accessible" at the pedestrian scale are either located in Lille Metropolis or in the urban cores of certain sub-prefectures within the region.

It is therefore noteworthy that by expanding station areas, the number of "accessible" connection points in the region doubles. The 11 stations that join this category as a result of the cycling catchment areas are as follows: Beauvais, Béthune, Boulogne-sur-Mer, Boulogne - Tintelliges, Calais Ville, Creil, Croix - Wasquehal, La Madeleine, Lille Porte de Douai, Saint-Quentin, and Saint-Roch (see Fig. 11, p. 22). In this respect, the stations that become "accessible" following the isochrone extension are either located in other sub-prefectures of the region or in close proximity to the central stations of major urban areas.

The identification of a substantial majority of stations classified in the "dependence" category is hardly surprising at the regional scale, given the diversity of the territories covered and the variety of rail services within the studied system. In Brussels, Caset (2019, p. 46) highlights a predominance of "imbalanced" stations to the detriment of design. The observation of a larger share of stops exhibiting an imbalance at the expense of the place component is further supported by Kutty (2018, p. 190) and Monajem and Ekram Nosratian (2015, p. 19), who report this profile for 20% of metro stations in Tehran. Regarding BRT networks in Wates and Tehran, Alfyan and Widyastuti (2022, p. 15) and Pezeshknejad et al. (2020, p. 13) also identify a majority of stops in asymmetric situations, with poor assessment of urban development and walkability. We also observed that the number of stations characterized by strong coordination among the three analyzed dimensions doubles when the spatial extent of station areas is expanded, thereby validating the hypothesis formulated by Caset, Vale, et al. (2018, p. 518), which posits a marked contrast between rail service areas evaluated at 800 or 1,200 meters and those measured at 3,000 meters. This result suggests that increasing the spatial scale allows for a better capture of the local and regional dynamics that influence the "equilibrium" between transit network, territorial, and connectivity functions.

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## Profiles of "Accessible" and "Dependent" Stations based on Descriptive Indicators

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On average, the weighted indicator values (IEW) for stations classified as "accessible" (Acs) and "dependent" (Dpt) are as follows:

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* For the node dimension (N), the weighted average value reaches 0.01 for N<sub>Acs</sub> and 0.00 for N<sub>Dpt</sub> across both pedestrian and cycling scales. The gap in favor of the former category is thus 5.05 times greater at the pedestrian scale (PI), while it narrows to 3.65 at the cycling scale (CI);

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* Regarding the place dimension (P), the weighted average is 0.02 for P<sub>Acs</sub> compared to 0.00 for P<sub>Dpt</sub> at the pedestrian scale. This advantage in favor of accessible stations corresponds to a factor of 4.40 for PI and 3.64 for CI;

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* For the accessibility dimension (A), the weighted average reaches 0.03 for A<sub>Acs</sub> and 0.01 for A<sub>Dpt</sub> at the pedestrian scale. The difference is thus 3.52 for PI, and it decreases to 2.34 for CI;

* Concerning ridership (RT), the normalized average value is 0.15 for RT<sub>Acs</sub> and 0.01 for RT<sub>Dpt</sub>. The resulting gap is 23.53 for PI and 18.35 for CI.

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Stations classified as "accessible" exhibit significantly higher relative values in terms of high-speed rail frequency, regardless of the day of the week (N<sub>1</sub> and N<sub>2</sub>). Moreover, for pedestrian station areas, they benefit from better urban rail transit service coverage (A<sub>7</sub>), while for cycling station areas, they show stronger connections to bike-sharing services (A<sub>6</sub>). Conversely, nodes classified as "dependent" are characterized by higher median household incomes (P<sub>13</sub>), lower availability of car parking in public spaces (A<sub>10</sub>), and greater residential specialization (P<sub>3</sub>), regardless of the spatial scale considered. More broadly, the average indicator gap amounts to 12.10 at the pedestrian scale (PI) and 8.54 at the cycling scale (CI), suggesting that the cycling catchment area tends to somewhat mitigate disparities between "accessible" and "dependent" stations.

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## Strength and Direction of Relationships among the Model's Constitutive Dimensions

From the perspective of the four dimensions structuring the NPART framework, it appears that some of them are closely interconnected. By computing Pearson correlation matrices for each pair of dimensions, we first highlighted a particularly significant positive association between the dimension related to rail network quality (N) and that related to mobility demand for each station (RT). Across the entire initial dataset, the coefficient of determination R² reaches 0.93 between the rail node and station ridership dimensions, regardless of the geographic perimeters considered (PI and CI). The associated t-test, with a high value of 43.60, demonstrates that this correlation is statistically highly significant. However, this strong linear relationship between the Transit and Ridership per Time components is only verified for the subgroup of stations classified as "accessible," with a correlation coefficient of 0.96. Conversely, this coefficient drops to a moderate value of 0.53 when the analysis is limited to stations classified as "dependent." This observation results from a segmentation effect, indicating that the relationship between N and RT depends on the intensity and degree of articulation of values specific to each dimension, characterizing certain stations as so-called "accessible" nodes. Within this perspective, we also explored the existing relationships between the other dimensions.

A second significant positive relationship is established between the node dimension (N) and design, embodied by public space development and local connectivity (A). The Pearson correlation coefficient R² reaches 0.75 for the pedestrian station area (PI) and 0.64 for the cycling station area (CI). Even more markedly than in the first examined relationship, a considerable gap is observed between stations classified as "accessible" and those deemed "dependent." The former exhibit high coefficients of determination (R² of 0.77 for PI and 0.71 for CI), whereas for the latter, the correlation is almost non-existent (R² of 0.21 for PI and 0.02 for CI).

A final significant positive linear relationship is observed between the dimensions related to place (P) and design (A). This can be seen on the left-hand side of the cube diagrams (see Fig. 11, p. 22). This association remains significant at both pedestrian and cycling scales, with coefficients of determination of 0.80 (with a t-test value of 23.36) and 0.77 (with a t-test value of 21.47), respectively. These results suggest that the intensification of human activities within a given territory either drives or results from strong interdependencies with the quality of public spaces. While consistently distinguishing stations classified as "accessible" from those classified as "dependent," and unlike the two previously analyzed relationships, we observe minor variations concerning the pedestrian catchment areas, with correlation coefficients R² of 0.77 for the former and 0.72 for the latter. However, the gap is slightly more pronounced in cycling station areas, where "accessible" stations exhibit a coefficient of 0.80, compared to 0.65 for "dependent" ones.

Statistical regression analysis applied to the various studied dimensions reveals interesting interdependencies, particularly within the subsample of stations classified as "accessible." These quantitative measures echo the work on the extended NPM developed by Vale et al. (2018, p. 289), which, similarly to our study, highlight a significant positive correlation between the dimensions associated with place and design. Likewise, Yuerong Zhang et al. (2019, p. 10) have demonstrated the same important link, with an R² of 0.81, as well as a relevant relationship between node and place (R² of 0.70), thereby confirming the observation by Vale et al. (2018, p. 289) of a moderate correlation between node and design (R² of 0.60). However, this latter point differs from

our empirical observations, corroborated in literature (Papa and Bertolini, 2015, p. 79), which seem to indicate a strong correlation between N and A, particularly at the pedestrian scale. Finally, the major positive relationship between N and RT, notably during peak hours, constitutes a novel contribution to the NPM literature. This does not imply, however, that a simple improvement of railway infrastructure and service would suffice to increase demand. The bilateral nature of the relationship similarly suggests that demand induces infrastructure improvements, which evolve to meet this growing demand.

By adopting an initial approach based on zones of "accessibility" and "dependency," we were able to classify and assign specific values to each studied station. It thus emerges that stations classified as "accessible" benefit from feedback effects among these four dimensions, as expressed earlier in the current section. Nevertheless, as emphasized by M. Zhang and J. B. Lee (2023, p. 4), "the original node-place model's five types of categories do not encompass all possible typologies of station areas and TODs." Beyond the normative approach of the initial NPM model, in a data science approach, the purpose of this modeling is to propose a classification of stations and their catchment areas according to their respective performances. It aims to move beyond a binary categorization ("accessible" or "dependent") to develop a more nuanced typology of nodal points and their surrounding territories, taking into account their capacity to comply with the principles of M-TOD. The ambition is not limited to statistical classification; it also seeks, within a framework converging data science and geography, to contextualize and give meaning to these categories.

## -4.3 A Regional Classification to Guide Policy-making

The application of the employed method revealed three classes of stations (see Fig. 12, p. 25). In the context of our case study, the classification of the 318 stations and their surrounding areas, both at the pedestrian scale (PI) and the cycling scale (CI), led to the identification of a first class (C1) grouping the most attractive stations, whose urban environment is well-developed and well-planned; a second class (C2), comprising stations with developed infrastructure located in dynamic territories, but with a weak orientation towards the mobility hub; and a final class (C3), made up of stations with poorer service and located in territories where the automobile predominates.

### Classification of the Regional Railway Network and their Surroundings

Nearly all of stations classified as "dependent" in the region fall into the last two classes. For PI, only 5.7% of stations are assigned to class C1 (18 stations), while 47.5% are in C2 (151 stations) and 46.9% in C3 (149 stations). However, for CI, the number of stations in C1 doubles (10.7%, 34 stations), mainly at the expense of C2 (41.8%, 133 stations), while C3 retains a similar share (47.5%, or 151 stations). Thus, extending station areas using cycling modes allows various stops to progress from class C2 to C1. It is interesting to note that while the expansion of station areas doubles the number of stations in C1, among the 16 new stations integrated into this category, 7 of them remain classified as "dependent" (see Fig. 11, p. 22).

This observation suggests that the typology based on the original NPM model is not only less precise but also deviates from the statistical classification carried out. This finding

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aligns with contemporary critiques in the scientific literature, which advocate moving away from a simplistic interpretation based solely on a normative and strictly diagrammatic view. Thus, the classification approach allows us to deepen the characterization of stations and their surroundings, going beyond the simple dichotomy between “accessible” and “non-accessible” stations, particularly through the distinction that emerges between classes C2 and C3.

## Towards a Temporal Classification

Through a more dynamic analysis based on the temporality of flows, we observe a slight evolution in the classification. We examined the distribution of stations within the classes defined according to the RT₂ reference. It is interesting to note that, while the proportion of C1 remains relatively stable around 6% for the pedestrian scale (PI), it fluctuates between 6% and 13% at the cycling scale (CI). However, the class showing the greatest variation is undoubtedly C2, particularly during RT₂ and RT₄ periods, corresponding to morning peak hours. Indeed, the share of C2 is higher during peak periods and for CI, indicating increased attractiveness of stations in these spatio-temporal contexts.

In an effort to gain a more systemic view of the changes in station class affiliation, we adopted the approach developed by Caset, Vale, et al. (2018, p. 518), which they called the “DNA sequence.” This approach allows the representation of a station’s affiliation to its clusters based on the variation in the size of its surrounding area. The “DNA sequence,” denoted DNA, consists in an ordered succession of classes to which each station is associated, depending on the considered temporal scales (RT₁ to RT₆) and geographical scales (PI and CI) (see equation 4, page 24).

$$DNA_k = \sum_{k=1}^{6} (C_{PI}^{RT_k} + C_{CI}^{RT_k}). \quad (4)$$

where:

DNA_k is the DNA sequence of station k.

Finally, nearly as many combinations as stations are observed, with no less than 263 unique sequences across the railway network for the 12 components analyzed. This result shows that only 1.89% of stations consistently retain the same class affiliation. However, several recurrent chains can be cited:

* “C2C2C2C2C2 – C2C2C2C2C2C2”, representing stations entirely within C2;

* “C2C2C2C2C2C2 – C1C1C1C1C1C1”, corresponding to stations that transition from C2 in the PI context to C1 for CI across all periods;

* “C3C3C3C3C3 – C2C2C2C2C2C3”, denoting stations that move from C3 to C2 across all time intervals except weekends.

Continuing the exploration of the typology thus obtained, and after clarifying the distribution of stations within each category, we now undertake a detailed analysis of these classes. The following section provides a definition of the identified categories from an urban planning perspective, supported by a statistical analysis of the determining factors. This interpretative framework not only helps to grasp the specificities of each class but also offers a conceptual reading of the observed distinctions, shedding light on the spatial and functional logics underlying this regional classification.

## From Definition to Interpretation of the Categories

In order to define the categories as faithfully as possible to the statistical data, while offering a simplified framework for operationalization and communication, we chose to draw inspiration from the “butterfly model” as the visual framework for analysis. This conceptual representation was adopted by Caset (2019, p. 52), who applied it to the Belgian stations of Waterloo, Watermael, and Buggenhout, relying on the initial models developed by the Hollande-Septentrionale and Association Deltametropolis (2024).

Based on Fig. 13, (p. 26), divided into four axes constituting the NPART model, we have the opportunity to compare not only the normalized and weighted values of each dimension and variable but also the two types of station areas. It appears that the dimensions related to station ridership during morning peak hours (RT₂) and the concentration and diversification of urban functions (P) generally present the lowest values. This statistical observation suggests that some stations compensate for low urban intensity with higher accessibility. In this specific case, the contribution of urban intensity to ridership is low in many cases, indicating potential for development through urban intensification. However, the weakest indicators are almost all found in the dimension related to local connectivity (A), particularly regarding metro and tram service coverage (A₇), bike-sharing systems (A₆), bicycle parking availability (A₅), and the length of the cycling network (A₄), for both pedestrian (PI) and cycling (CI) catchment areas. This suggests that bike-friendly connections remain largely marginalized.

In light of these data, we are able to give a more precise meaning to this classification within the regional context of our empirical study. At this stage, it is important to consider that class C1 encompasses stations and their surrounding urban areas, accessible by foot or by bike, that adhere to or approach the principles of TOD and M-TOD planning. The first class, C1, displays remarkably high scores across the four dimensions that constitute the NPART, positioning these stations as the closest candidates to the TOD model and its variant, compared to what is present in the studied network. This class is primarily distinguished by better cycling connections within public spaces, a strategic position within the technical network, and strong industrial and commercial attractiveness.

Class C2, on the other hand, holds TOD development potential, under certain conditions that we will elaborate on. If C1 displays weighted average values significantly higher than those of C2, with differences of 103% for P (PI) and 92% (CI), 103% for A (PI) and 50% (CI), and 815% for RT₂ (PI) and 343% (CI), the gap narrows for N, reaching only 36% (PI) and 16% (CI). These disparities thus raise a relevant question about the identity of C2. The main differences are observed in the treatment of public spaces. This allows us to infer that C2 encompasses stations with potential for rail-oriented urban development, as the infrastructure and rail service are of good quality, although there are gaps in terms of human activity concentration and territorial hospitality. Furthermore, these are TOD station areas with a residential dominance.

Finally, class C3 records very low scores across all the NPART indicators. The quality of rail service (N) is generally not ensured, with low frequency of trains, reduced commercial speed, and a poor strategic position within the network, offering few directional choices and limited access to other stations within a given distance-time. The surrounding areas are weakly developed or poorly designed (P), with very low employment density, relatively few green spaces, a very
<figure><img src="image_14.png" /></figure>

Sample: 318 stations

<caption>**Fig. 12.** Regional Classification of Station Areas Accessible by Walking and Cycling, Based on Three Classes.</caption>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

limited number of POIs, and low land attractiveness for industrial and commercial sectors. These areas also tend to host more affluent populations. In addition to the need for

urban intensification, these station areas do little to promote the use of the walking, cycling, and public transport triptych (A). Cycling infrastructure is virtually non-existent for both
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 13. Radar Chart of the NPART Model Indicators by Station Area Class.**</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

PI and CI, and bus service remains very limited. Moreover, the space allocated to cars is generous, with moderate restrictions on motorized traffic and a high rate of car ownership among residents. Finally, this lack of accessibility and development is reflected in very low ridership at the stations (RT₂). In a way, class C3 does not designate "stations to close," but rather stations with potential, where it would be appropriate to explore development avenues combining N, P, and A in relevant and realistic combinations, in order to increase ridership and trigger the TOD virtuous circle (Jamme et al., 2019, p. 416). This leads us to remind that the concept of urban planning is primarily a metropolitan and regional "pearl necklace," and that each element of the system, from the largest to the smallest, has a role to play. Thus, the smallest stations should not be overlooked.

The detailed analysis of each station leads us to summarize them concisely, in line with the requirements of the classification, as follows: C1 includes nodes and areas truly oriented towards rail or those that tend towards this urban planning model, C2 pertains to nodes and areas with TOD potential—some of which could also fall under Transit-Adjacent Development (Renne, 2009, p. 3)—and C3 corresponds to car-centric nodes and areas, which tend to turn their backs on railway transport infrastructure. This distinction between TOD, TAD, and car-dominated systems follows the clustering produced within the NPM framework by Z. Li et al. (2019, p. 271). Specifically questioning the actual presence of TAD within these groupings, it became clear that it was necessary to go beyond the simple typology opposing TOD, TAD, and non-TOD. Based on a commonly accepted

definition of TAD, characterized by a high level of public transport service, a high population density, and, conversely, relatively low ridership and density of pedestrian and cycling areas, we identified 86 pedestrian station areas (PI) and 33 cycling station areas (CI), distributed across the entire set of classes. Among the PI perimeters, 63% belong to class C3 (54 stations), while 34% are in C2 (29 stations). For the CI perimeters, the distribution is more balanced, with 21% in C3 and 55% in C2. The results suggest that the concept of TAD transcends the established typological classes, calling for a reconsideration of their meaning. To this end, we drew inspiration from the four clusters identified by L. Yang and Song (2021, p. 249), who distinguish between "already TODs" (ATOD); "potential TODs" (PTOD); "improvable TODs" (ITOD); and "no TODs" (NTOD). By transposing this analytical framework to our own results, we propose a reading grid to requalify our three classes. Class C1 could be assimilated to an ATOD, that is, a fully structured area according to the principles of TOD and M-TOD. Class C2 would correspond to a PTOD, i.e., an area that meets the fundamental conditions to evolve into a mature TOD and M-TOD model. Finally, class C3 would resemble more of an ITOD, marked by an incomplete spatial organization and requiring adjustments to maximize its rail-oriented urban development potential.

The proposed classification of station areas was validated using a triangulation approach that combined quantitative analysis, planning references, and expert input. First, the results were benchmarked against regional planning documents and technical reports, which confirmed the

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relevance of our regional typology. The proposed station area classification is fully aligned with the objectives set out in national and regional planning documents. It operationalizes several ambitions defined in the SRADDET<fnref n="31" />, including the increase the modal share of public transport between 10% and 12% (Région Hauts-de-France, 2024b, p. 169). Furthermore, our approach complements the bike-and-ride priorities to increase the modal share of cycling from 3% to 12%, expressed in the planification régionale de l'intermodalité (PRI), planification régionale des infrastructures de transports (PRIT), and schéma régional des véloroutes et voies vertes (SR3V) by offering a spatialized tool to identify and prioritize stations where walking and cycling can serve as viable feeder modes to regional rail (Région Hauts-de-France, 2024a, p. 46). Moreover, the segmentation provided by the national Document de Référence des Gares (DRG) (SNCF Gares & Connexions, 2024), as well as the earlier classification developed for the former Picardie region prior to its integration into Hauts-de-France (CETE Nord Picardie and DREAL Picardie, 2011), served as external validation references. These classifications distinguish stations based on annual ridership and territorial significance. While most of the stations in our study align with these existing classifications, our approach incorporates a broader set of indicators, enabling a more dynamic and fine-grained typology. In particular, it highlights stations with untapped development potential at walking and cycling scales, which are not always captured by conventional segmentation methods. Lastly, we recently had the opportunity to present the results of our NPART model to urban and transport planning practitioners involved in our case study area, during a dedicated workshop organized with planners working in the Région Hauts-de-France, the Métropole Européenne de Lille (MEL), and the Agence de développement et d'urbanisme de Lille Métropole (ADULM). The discussions confirmed the strategic relevance of the proposed classification, which was recognized as a useful framework for supporting integrated planning approaches and identifying priority areas for intervention. The shared and spatialized nature of the typology was particularly appreciated for its potential to foster dialogue between institutions and to inform operational decision-making.

## -5 Conclusion

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Developing station neighborhoods often requires prioritizing one aspect or criterion over others, thereby raising international debates around TOD regarding the feasibility of simultaneously optimizing a large number of parameters (Veloso e Zarate and Triggianese, 2024, p. 181). In this perspective, it is essential to identify the levers of action and their effects in order to promote TODs supported by active mobility, especially in a context where specialized forms of TOD are emerging (Cervero et al., 2017, p. 111). Within this framework, we developed the NPART model, designed to identify and better understand the mechanisms supporting an Micromobility-friendly TOD (Moinse, 2025b, p. 621).

The classification of stations and their surroundings revealed three distinct classes, thereby refining the overall picture of

the rail network, which is predominantly composed of so-called "dependent" stations—both at the pedestrian and cycling scales. Only 6% of stations fall within the class corresponding to the TOD model, while nearly half of the network belongs to the category of stations with TOD potential. Notably, the share of stations in the first class—considered to reflect the highest level of urban development—doubles when the boundaries of station neighborhoods are expanded. This finding highlights the importance of promoting solutions adapted to the first and last mile issue, such as bike-and-ride. Furthermore, improvements in design—significantly supported by cycling infrastructure—are positively correlated with other dimensions of the model. These interdependencies underscore the strategic role of the connection between rail service quality and urban development intensity, thus confirming the value of an integrated approach to enhance both the attractiveness and functionality of the areas served.

Through this modelling process, we were able to better define and situate the impact of the main criteria contributing to the definition of an M-TOD approach. On the side of infrastructure and public transport service—both at the pedestrian and cycling scales—factors such as rail service frequency, betweenness centrality, centrality degree, number of directions served, and the number of stations accessible within one hour prove to be decisive for increasing station usage. Regarding activity intensity, key components include the land value of industrial, commercial, and office functions, the presence of "superior" and "intermediate" POIs, as well as the availability of green spaces. However, at the cycling scale, job density emerges as an even more decisive factor, ranking highest among all key indicators. Finally, in terms of local connectivity, metro and tram service, along with elements fostering a comprehensive territorial hospitality toward cycling—such as the presence of bikeshare systems, bicycle parking, and dedicated cycling infrastructure—are the most influential factors at both spatial scales.

By structuring these contributions around a reflection on infrastructures, mobility services, and the territorial configurations of station areas, an Micromobility-friendly TOD (M-TOD) formalized by Moinse (2025b, p. 621) helps renew our interpretation of rail-based urbanism and the planning strategies for territories served by rail. This study thus represents the translation of theoretical and empirical findings into a structured and operational proposal for intermodal rail-oriented urbanism. By fully integrating cycling into the design of station areas and intermodal hubs, the M-TOD opens up a new perspective on urban mobility planning. It transcends a segmented view of transport modes to promote a high-performing intermodal system articulated with the urban fabric. These reflections provide concrete tools for urban stakeholders seeking to integrate cycling into their land use policies and public transport strategies.

For the purpose of showcasing the model as a practical and interactive visual tool, we developed a prototype dynamic map using the Python programming language. This map could later be enhanced through the integration of JavaScript for increased interactivity. Concretely, this mapping interface allows users to navigate among the various stations in the region and to explore the different criteria of the model, both through numerical data and radar charts for PI, PB, CI, and CB (see Fig. 14, p. 28). The associated radar chart puts this information into perspective relative to other stations, enhancing the readability of results and facilitating their interpretation in a decision-making or operational context (see Data Availability section, p. 30).

While the dissemination of our model represents a key step in ensuring a robust scientific outcome, we remain aware of

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<footnote n="31">³¹The schéma régional d'aménagement, de développement durable et d'égalité des territoires (SRADDET) of the Hauts-de-France region, adopted in 2020 and revised in 2024, is the strategic instrument that sets the objectives and general rules applicable to intercommunal urban planning documents. Regarding mobility, this plan precisely defines the region's medium- and long-term ambitions in terms of intermodality, development of passenger transport, logistics, planning of regional infrastructure and routes of regional interest, as well as its airport strategy for certain airfields (Cerema, 2024, p. 1). This strategic document is structured around three major ambitions for 2050: (i) a connected region, (ii) enhanced multipolarity for balanced development, and (iii) a rethought daily life, focused on new proximities and improved quality of life (Région Hauts-de-France, 2024b, p. 7).</footnote>
<figure><img src="image_17.png" /><figcaption>Fig. 14. Visual Examples from the Interactive Map Showing Bicycle Parking Locations.</figcaption></figure>

Authors: Moinse, L'Hostis, Amini Pishro, Cissé, Lehmann, Zhang, Theureaux, Adjeroud, Yuetong, and Xiao (2025)

certain limitations. By focusing on the direct applications of these recommendations, the relevance of an M-TOD strategy could be further consolidated through an in-depth study regarding shared mobility, more specifically about dockless services, which has been only marginally addressed in this work due to its limited deployment in the studied area. Likewise, the analysis should be extended to other transport networks, particularly bus systems, while data collection should be enhanced by novel techniques like street view data (J. Liu et al., 2025, p. 7). A complementary reflection also raises questions about the economic and financial models needed to support the development of an M-TOD. Finally,

although NPMs are grounded in empirical observations that reflect local transport and land-use dynamics, this very specificity raises a potential paradox regarding their transferability. This underscores the importance of their structured analytical framework and adaptable set of indicators, which together enable the model to be recalibrated for diverse geographical contexts. Such a balance makes NPMs both robust and sensitive to local planning conditions.

Broadening these perspectives, a study on the implementation conditions of an M-TOD through the lens of

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governance and stakeholder dynamics also appears essential. An evaluation of institutional levers and regulatory mechanisms could provide concrete tools for action in support of this renewed urban planning model. Finally, a relevant extension of this research would be to compare several geographical contexts to assess the transferability of the conclusions to diverse territories. The study of M-TOD would thus benefit from being applied to areas with varied urban configurations and transport systems, as well as being updated in our case study with the upcoming joint implementation of a rail project-the *Services express régionaux métropolitains* (SERM)-and a cycling infrastructure project-the *Vélo Plus* network-around the Lille metropolitan area.
CRediT Author Statement

**Dylan Moinse:** Conceptualization; Methodology; Software; Investigation; Writing - Original Draft; Visualization. **Alain L'Hostis:** Conceptualization; Methodology; Validation; Writing - Review & Editing; Supervision; Project administration; Funding acquisition. **Ahad Amini Pishro:** Conceptualization; Methodology; Software; Validation; Investigation; Writing - Review & Editing; Visualization; Supervision. **Ndèye Aïta Cissé:** Conceptualization; Methodology; Validation; Investigation; Writing - Review & Editing. **Xavier Lehmann:** Conceptualization; Methodology; Validation; Investigation; Writing - Review & Editing. **Shiquan Zhang:** Conceptualization; Methodology; Validation; Supervision; Writing - Review & Editing. **Olivier Theureaux:** Software; Validation; Writing - Review & Editing. **Heythem Adjeroud:** Validation; Writing - Review & Editing. **Yuetong Liu:** Formal analysis. **Qixiao Hu:** Formal analysis.

Acknowledgements

This research project is the result of a scientific collaboration between researchers from the Laboratoire Ville Mobilité Transport (LVMT) at Université Gustave Eiffel, the School of Civil Engineering at Sichuan University of Science and Engineering (SUSE), and Sichuan University (SU). It was supported by a one-year research engineer position, funded through the I-SITE *Initiative of Excellence* at Université Gustave Eiffel.

The authors acknowledge the organizers of the 35th International Geographical Congress (IGC 2024), held in Dublin, particularly the session Transport and Geography: Sustainable Transit-Oriented Development (STOD): New Perspectives and Advances on August 28, 2024, chaired by Chia-Lin Chen and Richard Knowles, where they had the opportunity to present this research project. Furthermore, the authors appreciate the organizers of the monthly seminar of the Laboratoire Ville Mobilité Transport (LVMT) for hosting the session Variations in the Urban Integration of French Railway Stations and Their Classification on June 21, 2024, organized by Anne Aguiléra and Rayane Al Amir Dache, which provided a valuable platform to discuss the project's progress and further refine its contributions. The authors extend their gratitude to the reviewers of this article for their valuable suggestions and constructive feedback.

Data Availability

The authors have made this model both reproducible and adaptable by sharing the programming code developed for extracting and processing spatial databases and station areas (Moinse, L'Hostis, et al., 2025). The code is publicly accessible for review and reuse in the following GitHub repository: https://github.com/dylan-moinse/Node-Place-Accessibility-Ridership-Time-Model.

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