# Framework for real-time multimodal container transport risk management

Cheik Ouedraogo, Rodolphe Barlogis, Aurelie Montarnal, Didier Gourc, Cedric Rosemont

► To cite this version:

Cheik Ouedraogo, Rodolphe Barlogis, Aurelie Montarnal, Didier Gourc, Cedric Rosemont. Framework for real-time multimodal container transport risk management. Engineering Applications of Artificial Intelligence, 2026, 165, pp.113108. 10.1016/j.engappai.2025.113108. hal-05417119

HAL Id: hal-05417119

https://imt-mines-albi.hal.science/hal-05417119v1

Submitted on 17 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Framework for real-time multimodal container transport risk management

Cheik Ouedraogo<sup>a,*</sup> <sup>□</sup>, Rodolphe Barlogis<sup>a</sup>, Aurelie Montarnal<sup>a,**</sup> <sup>□</sup>, Didier Gourc<sup>a,□</sup>, Cedric Rosemont<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Natural language processing

Bidirectional encoder representations from
transformers

Event extraction

Risk management

Decision support system

Multimodal transportation

## ABSTRACT

In the current context of globalization, multimodal container transport plays an important role in the efficiency and effectiveness of the supply chain. Indeed, containerization induces high productivity during port handling and a reduction of transport costs thanks to groupage. It also ensures the integrity and security of the goods transported. Nevertheless, the large number of stakeholders involved in the container transport process makes it extremely complex and leads to a loss of visibility and traceability during the transit of goods. In addition, the transit process is very often subject to random events that lead to delivery delays and increased transport costs. Considering all these difficulties, this work proposes a system for real-time detection of random events that may disrupt the multimodal transport of containers. In this paper we show how by relying on container traceability/visibility data, textual data and on natural language processing techniques, our system can detect in real time random events that may disrupt the transport flow. We describe the different modules and models that make up our system and then show its performance through a real use case. This work focuses on decision support for the activities of the stakeholders involved in the multimodal transport chain (exporters, consignees, carriers, shipping companies, etc.). The research is conducted in collaboration with an industrial partner (Traxens), who develops a real-time tracking solution for containers.

## 1. Introduction

Delivering the right supplies, at the right time, to the right place, in the right quantities and to the 'right people' are the critical success factors in the international supply chain (Pettit and Beresford, 2009). Multimodal container transport, which integrates multiple transportation mode and multiple stakeholders (Fig. 1), has become a cornerstone of scale (Ouedraogo et al., 2020). However, the complexity of multimodal supply chains, characterized by numerous stakeholders and fragmented processes, often leads to a lack of visibility and heightened vulnerability to disruptive events. These disruptions ranging from natural disasters (e.g., storms, floods) to human errors (e.g., lack of skilled workers) and regulatory changes can severely impact supply chain performance (Alyami et al., 2014; Ouedraogo et al., 2020; Vilko and Hallikas, 2012). To address these challenges, researchers and practitioners have turned to emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). IoT enables real-time

monitoring of container conditions (e.g., temperature, humidity, shock) through sensor networks, while AI techniques, including machine learning, facilitate the analysis of large datasets to predict and mitigate risks (Nawaz et al., 2019; Ouedraogo et al., 2022). For instance, Bukkapatnam and Komanduri (2007) developed a system using vibration sensors and AI to monitor container handling conditions. Despite these advancements, a significant research gap remains in integrating real-time heterogeneous data streams such as shipment events, social media data, and news to enhance Supply Chain Risk Management (SCRM) in multimodal transport. This study addresses this gap by proposing a novel conceptual framework for real-time SCRM. The framework leverages real-time shipment data, social media (e.g., Twitter) and news sources (e.g., News API, Google News) to detect disruptive risk events using Natural Language Processing (NLP). By integrating these diverse data streams, the framework aims to provide a Decision Support System (DSS) for managing disruptive risks in multimodal container transport. This approach not only enhances visibility and traceability

E-mail addresses: cheik.ouedraogo@mines-albi.fr (C. Ouedraogo), rodolphe.barlogis@mines-albi.fr (R. Barlogis), aurelie.montarnal@mines-albi.fr (A. Montarnal), didier.gourc@mines-albi.fr (D. Gourc), contact@next.io (C. Rosemont).

<footnote><sup>a</sup> Centre du Génie Industriel, IMT Mines Albi, Allé des Sciences, Albi, 81000, France</footnote>

<footnote><sup>b</sup> Next 4, Street 29, 31400, Toulouse, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>
but also enables proactive risk mitigation, offering a contribution to the field of SCRM. The rest of the paper is organized as follows. In section 2, the existing research in SCRM and related fields is discussed. The proposed framework for disruptive risk events detection is presented in section 3. Sections 4 and 5 present the performance evaluation and the prediction quality of the proposed approach. In these same sections, the usefulness of the proposed framework is validated by applying it to a real use case and through simulations.

## 2. Literature review

Based on a literature review, this section aims (1) to define and identify disruption risk events, (2) to find and describe existing frameworks able to manage and optimize multimodal container supply chains and (3) to review works using machine learning techniques used to identify and assess supply chain risks. The following three parts are based on the methodology of the systematic literature review. To find the most interesting studies about multimodal transport SCRM, we requested two major bibliographic databases (Scopus and Web of Science).

### 2.1. Disruption risk events on multimodal supply chain risk management

Which hazardous events can negatively affect the multimodal container supply chain?

Disruption and uncertainties on the supply chain are the results of random events which lead to a performance drop throughout the transport supply chain. They have a serious impact on international trade (Ouedraogo et al., 2020). They delay the arrival of goods, disrupt the organization of ports, with consequences for the entire production and distribution chain. The purpose of this section is to examine risk events that cause disruption in the multimodal supply chain and their impact.

#### 2.1.1. Supply chain risk events and taxonomy

While many authors have attempted to define risk, a common definition remains elusive, for instance, Gourc (2006) defines risk as “the possibility that an event may occur whose occurrence would have an impact (positive or negative) on the progress of activity”. In contract, Moslemi et al. (2016) view risk as purely negative, leading to disruptions in the supply chain. Despite the lack of consensus, two main concepts are widely accepted for characterizing risk: (i) Probability (P) of occurrence and (ii) Impact (I) of the risk event. In this work, P represents the likelihood of random events disrupting container transport flow, and I

denote its consequences on the supply chain, such as delays or additional costs. Thus, supply chain risk is commonly expressed as the product of probability and impact (Equation (1)); this formula widely used in risk analysis on supply chain risk management (Vilko and Hallikas, 2012).

$$R = P \cdot I \qquad (1)$$

Moslemi et al. (2016) define SCRM as “the identification of potential sources of risk and implementation of appropriate strategies through a coordinated approach among supply chain members to reduce supply chain vulnerability”. SCRM involves four key steps:

1. Risk identification: Creating a list of potential risk events, their sources, and their impact areas.

2. Risk evaluation: Quantitatively or qualitatively evaluating the probability and impact of each event.

3. Risk assessment: Selecting and implementing solutions to mitigate risks by reducing their probability or impact.

4. Risk monitoring: Continuously controlling risk levels to ensure they remain acceptable.

These steps enhance the resilience of the supply chain to risk events. Many authors (Guo et al., 2021; Kabadurmus and Erdogan, 2020; Kra-marz et al., 2021; Zhang et al., 2019) have contributed to risk identification by proposing various typologies. For instance, the authors in (Vilko et al., 2019) provide a literature review on risk visibility and control in maritime supply chains, categorizing risk as internal (e.g., bottlenecks, labor strike) or external (e.g., natural disasters, severe weather). Similarly Ho et al. (2015) classify risks in the shipping industry into five categories: environmental risk factor, industry risk factor, organizational risk factor, problem specific risk factor and decision risk factor. Alyami et al. (2014) identify 76 risk sources in container port operations using an advanced FMEA and expert knowledge, while Tang and Musa (2011) classify risks based on supply chain flow: physical, information and financial. A review of these typologies reveals that risks can be grouped into three main categories based on their origin:

* Supply risk: shipment events, port events, accident, route deviation, congestion, maritime traffic incident, strike, etc.;

* Environmental risk: storms, tornadoes, strong winds, etc.;

* Container related risks: temperature variation, humidity variation, pressure variation, etc.

This classification highlights the unique risk faced in multimodal

<figure><img src="image_3.png" /><figcaption>Fig. 1. Multimodal transport process.</figcaption></figure>
container transport, where interdependencies between transport modes (e.g., road, se, rail) amplify the impact of disruption.

### 2.1.2. Risk impact

Contrary to the definition of risk, the authors such as Ho et al. (2015); Mohseni et al. (2019); Vilko et al. (2019) agree its impacts on the supply chain. Indeed, a review of the literature on SCRM shows that there are four types of impacts: (1) time impact; (2) financial impact; (3) quality impact; and (4) environmental impact:

* Time impact: Increased arrival times and lead times for goods;

* Financial impact: Loss of container or goods, additional costs, and penalties;

* Quality impact: Damage to the carrier's reputation and loss of goods quality;

* Environmental impact: Increased carbon emissions due to delays or rerouting.

## 2.2. Framework for supply chain risk management

Do the proposed SCRM frameworks permit to handle disruption risk events during multimodal container transport?

The aim of this part of the literature review is to present the most recent frameworks that propose better management of supply chain risk. The article analysis shows that many authors try to mitigate supply chain risks by proposing frameworks and methodologies to handle them. For example, to reduce risks in multimodal transport of dangerous goods, the authors in (Sun et al., 2019) propose a fuzzy logic framework that allows the selection of the best transport routes for dangerous goods and reduces carbon dioxide emissions. Sun et al. (2019) mixed fuzzy probabilistic chance constraint, linearization technique, and the normalized weighting method to establish objective function to optimize multimodal hazardous goods transport route selection. The authors in (Gumuskaya et al., 2020) develop a framework, based on interviews with transport experts, to develop coordination in hinterland transport. Their framework considers the exchange of information, minimal costs, minimal delay, maximum volume, minimal capacity, waste and commercial negotiation. Recently, to improve transport networks subject to traffic congestion and excessive air pollution, authors in (Du et al., 2020) propose a hybrid multimodal framework for short-term traffic flow forecasting. Their work presents how to process sequential traffic data (traffic speed, flow, travel time and weather conditions, etc.) to find spatio-temporal correlations, and a deep learning model based on attention mechanism that allows to solve traffic flow forecasting problems. Kaewfak et al. (2021) propose a methodology for risk identification and analysis based on the fuzzy analytical hierarchy process. This methodology aims to help practitioners better prioritize risks by generating a score for each risk. To reduce gas emission during multimodal transport, Paraskevadakis et al. (2021) propose a framework based on Importance-Performance Analysis (IPA) and Analytical Hierarchical Process (AHP). Other authors in (He et al., 2019) propose a framework based on cascade failure theory to determine risk propagation through multimodal transport networks. Their works permit to establish that "When the proportion of risk source nodes is more than 60 %, all the nodes in the multimodal transport network are in a state of failure". To reduce the demurrage and detention cost in multimodal container transport, Fazi and Roodbergen (2018) propose a framework based on an optimization model. This framework compares different demurrage and detention policies and provides managerial insights to practitioners. The literature review on supply risk management frameworks shows that there is a great deal of interest in the subject. Table 1 presents the supply chain axes that are the focus of the frameworks proposed in the articles reviewed.

<table><caption>**Table 1**
Focus addressed in framework.</caption><thead><tr><td>Focus</td><td>Authors</td></tr></thead><tbody><tr><td>Risk management</td><td>(Bazaluk et al., 2021; Fattahi and Behnamian, 2022; Gumuskaya et al., 2020; Guo et al., 2021; He et al., 2019; Jain et al., 2020; Kaewfak et al., 2021; Sun et al., 2019; Xu et al., 2021; Yu et al., 2020)</td></tr><tr><td>Traffic optimization</td><td>(Bazaluk et al., 2021; Chislov et al., 2021; Kabadurmus and Erdogan, 2020; Kaewfak et al., 2021; Smarzaro et al., 2021)</td></tr><tr><td>Traffic forecasting</td><td>(Du et al., 2020; Zhang et al., 2019)</td></tr><tr><td>Planning optimization</td><td>(Fazi and Roodbergen, 2018; Gumuskaya et al., 2020; Hadjidemetriou et al., 2022; Jiang et al., 2020; Kaewfak et al., 2021; Larsen et al., 2021; Xu et al., 2021)</td></tr><tr><td>Emission reduction</td><td>(Boehm et al., 2021; Dong et al., 2018; Fattahi and Behnamian, 2022; Paraskevadakis et al., 2021)</td></tr><tr><td>Pricing optimization</td><td>(Tawfik and Limbourg, 2018)</td></tr></tbody></table>

## 2.3. Supply chain risk management and artificial intelligence

Do data-driven frameworks allow to manage risks disrupting Larsen et al., 2021 multimodal container transport supply chain?

The last part of the literature review aims to present recent works done on SCRM and using Artificial Intelligence techniques. The multimodal supply chain suffers from a lack of visibility, which can have a serious impact on the supply chains of those involved (Vilko et al., 2019). To deal with traceability and visibility problems during shipments research have been carried out in the field of multimodal SCRM (Ebinger and Omondi, 2020; Tsang et al., 2018). These researchers propose a framework and system which permit to monitor and track container's inside and outside environment variations. These monitoring systems are designed by using IoT technologies to capture parameters such as location, temperature, humidity, pressure, etc. "IoT can sense real-time information from the surroundings and can share this information with other devices" (Nawaz et al., 2019). For instance, Tsang et al. (2018) propose a risk monitoring system based on IoT to better manage cold supply chains. This system uses a wireless sensor network to collect ambient environmental variations during shipments. Moreover, as information systems and social networks advance, more data is available, some of which reveal insights into supply chains. All this supply chain data enables the use of AI techniques to improve SCRM. For instance, Kara et al. (2020) develop a data mining-based framework for SCRM. To identify, assess and mitigate different types of risk in the supply chain, the proposed framework takes as input unstructured data coming from heterogeneous data sources and enables the identification and quantification of internal and external risks in the supply chain. Other authors in (Zhou et al., 2020) proposed a distributed deep learning model using a convolutional neural network to detect financial fraud in the supply chain. Aboutorab et al. (2022) propose a reinforcement learning approach to help practitioners proactively identify risks. This proposed approach presents four modules: data preparation, data collection, entity recognition, and Reinforcement Learning (RL)-based recommender system. The data preparation module allows practitioners to define manually disruption risk events. The data collection module permits to scrap news articles. The third module allows the contextual description of each collected article to be extended and, finally, the RL-based recommender system allows the selection of the news to be shown to the practitioners. Kumar et al. (2020) propose a data-driven model based on a neural network to improve the accuracy of demand forecasts. Authors use fuzzy historical demand of sales data to train a model and show that their solution is more accurate than other forecasting methods. Also based on a neural network, Dumitrascu et al. (2020) propose a model for the supply chain performance evaluation. The authors use an artificial neural network to identify specific key performance indicators following specific problems in the supply chain. Thanks to this model, the authors have demonstrated the possibility of extracting knowledge or information to feed the key performance
indicators. The authors in Janjua et al. (2023) develop an AI-based methodology to identify supply chain disruptive events using Twitter feeds in real-time. AI techniques used in this work have their strengths and limitations, making them suitable for different SCRM tasks. For instance, data mining and machine learning are ideal for risk classification and pattern recognition, while deep learning excels in handling complex data structures such as images or sequential data. Reinforcement Learning is well-suited for dynamic decision-making, and neural networks are effective for demand forecasting and performance evaluation. However, the choice of method depends on the specific requirements of the SCRM task, such as data availability, computational resources, and the need for real-time analysis.

In conclusion, AI techniques have become indispensable tools for enhancing SCRM. By leveraging data mining, deep learning, reinforcement learning, neural networks and natural language processing, researchers and practitioners can better identify, assess, and mitigate risks in supply chains.

## 2.4. Research gap

The literature review shows that SCRM generated a lot of interest. With the aim of optimizing supply chain resilience, many researchers proposed risk management frameworks based on different methods and techniques. However, few of these studies (Aboutorab et al., 2022; Janjua et al., 2023; Nawaz et al., 2019) are dedicated to the detection of risk events in the supply chain. Moreover, only two works focusing on real-time risk events detection use data mining techniques (Aboutorab et al., 2022; Janjua et al., 2023) despite the availability of real-time information on multimodal container transport and the increasing use of AI algorithms for risk management in several areas of supply chain (fraud detection, performance evaluation, etc.).

None of the previously cited works propose a framework based on heterogeneous data (news articles and social media) that allows supply chain risk managers to detect disruption risk events in multimodal container supply chains in real-time. However, some authors such as Vilko et al. (2019), and Wattanakul et al. (2018) argue that real-time risk management in the supply chain is important to enable stakeholders to be more responsive and proactive. According to the above-mentioned reasons, this study highlights some points in the multimodal transport area which can be useful for both academics and practitioners. The main contribution of this study is a data-driven framework, based on news articles, social media data and multimodal container supply chain visibility/traceability data, that enables near real-time detection of disruption risk events. Our research focuses on decision support for the activities of stakeholders involved in the intermodal transport chain (exporters, consignees, carriers, shipping lines, etc.). More specifically, we are interested in risk management during intermodal transport. In this context, we are interested in the real-time detection of random events likely to disrupt multimodal transport flow. Our aim is to offer to the stakeholders involved in the multimodal transport chain a solution to overcome the limitations of current hazard management tools (traceability and visibility tools).

# 3. The proposed framework for real-time risk detection

This section presents the proposed framework for real-time detection of disruptive risk events. In this study, risk detection is approached as a real-time extraction problem of disruptive risk events in multimodal container transport. Fig. 2 shows the conceptual model of the proposed framework, which consists of two main modules:

1. Data processing, which collects all the useful data needed to detect random events that could disrupt multimodal transport. It consists of a data collection model that takes container traceability and visibility data as input and then collects textual data from queries addressed to specific data sources.

2. Information processing, which is used to qualify the collected data. It is responsible for the detection of random events. It is made up of 4 models, the aim of which is to process the data to identify the random events disrupting the transport of containers.

First of all, before random events can be extracted, press articles and data from social networks are collected in a database by the data processing module. This module will regularly query several data sources and update the database. The data processing module uses the visibility data of the containers to determine the location of the data to be retrieved. The textual data is then used as input for the second module, the information processing module. For each input, the information processing module checks whether it is likely to contain an event, and if so, the event is extracted. The input data is first subjected to linguistic pre-processing to produce a more abstract representation of the texts. The pre-processing phase for each input consists of (i) lemmatising the data, (ii) tokenising the data and (iii) removing stop words. Once the data has been pre-processed, we use an encoder stack to vectorise the tokens (creation of words embedding). The words embedding are then used as input for the two models (symptom detection and event identification) with a view to extracting random events likely to disrupt the flow of goods. These two modules and their component models are detailed in the sections below.

## 3.1. Data processing module

The aim of this module is to collect textual data that can relay information on random events disrupting the container transport chain. To do this, it takes as input location data (visibility data) from an IoT device attached to the container. The following sub-sections describe the textual data sources and operation of this module.

### 3.1.1. Data sources

The value of this system lies in its ability to gather information from different sources and use it to identify risks in multimodal transport. For this purpose, potential data sources that could provide information on random events in multimodal transport systems were identified, such as News-API,<fnref n="1" /> Twitter,<fnref n="2" /> Marine Traffic,<fnref n="3" /> Waze,<fnref n="4" /> and OpenWeather.<fnref n="5" /> Information from these data sources can be numerical, such on the platform Marine Traffic, or qualitative, such on the platform Twitter which contains textual information. Information from these sources is then used to map out the occurrence of different random events identified in the literature review presented in the previous section. Table 2 shows the results of this analysis, including the names of the resources and the specific random events they were able to detect.

### 3.1.2. Data collection

The Risk Detection System data collection module aims to collect real-time information related to random events that may generate risks in the multimodal container transport supply chain. This module uses container visibility data to identify the exact position of the container and then collects the data sources of interest. To set up our risk detection system, we are going to focus on qualitative data (textual data). Once the position of the container is known, the data collection model queries textual data sources such as News-API, Event-registry or Twitter that have been previously identified (Table 2) from keyword-based queries limited to the region of the world where the container is located. The keywords used are the random events disrupting the container supply chain identified in sub-section 2.1.1 and correspond to the types of

<footnote n="1">¹ News-API: https://newsapi.org; API: Application Programming Interface.</footnote>

<footnote n="2">² Twitter: https://developer.twitter.com.</footnote>

<footnote n="3">³ Marine Traffic: https://www.marinetraffic.com/en/ais/home/.</footnote>

<footnote n="4">⁴ Waze: https://www.waze.com/fr/live-map.</footnote>

<footnote n="5">⁵ OpenWeather: https://openweathermap.org/.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Risk detection System: a conceptual framework.</figcaption></figure>

events identified in Table 2 (e.g. storm, tornado, strike, congestion). The collected data that is likely to relay information about random events disrupting the multimodal transport flow is then stored as a triplet ($N_i$, $T_i$, $L_i$) in a database. $N_i$ refers to the content of the data, $T_i$ refers to the publication date of the data, and $L_i$ refers to the location of the container. These data details will be used in the information processing module. Fig. 3 presents a schematic diagram of data collection module and Algorithm 1 shows the process used to collect data.

**Algorithm 1.** Data collection algorithm

who can take what actions with what information, and when, under what circumstances, using what methods". The data processing module of our risk detection system exploits and stores container visibility and traceability data and textual data from the data sources we present in subsection 3.2.1. To meet the necessary conditions for proper data governance as defined by Al-Ruithe et al. (2019), we have defined rules for the collection of data stored in the database of the data processing module:

• For traceability data, the framework only collects the anonymized

**Algorithm 1** Data collection algorithm

**Data:** list $Le_i$ of random events $e_i$ (e.g. Storm, Tornado); list $Ln_i$ of data
$N_i$; Localization $L_i$ of the container.

**Result:** data triplet ($N_i$, $T_i$, $L_i$)
initialization **for** $e_i$ **in** $Le_i$ **do**

**for** $N_i$ **in** $set_n$ **do**
    if $e_i \subseteq N_i$ **then**
      **insert** $N_i$ **in** the Textual database
    **else**
      **if** $i \leftarrow i + 1$
      **end**
    **end**
  **end**
**end**

### 3.1.3. Data governance

Al-Ruithe et al. (2019); Zeljko (2010) define data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe

identifier of the container transmitted by our industrial partner (Traxens) as well as container location data: longitude and latitude;

• For the textual data, the framework only collects the text of the data likely to describe a random event that could disrupt the transport of the containers and the date of publication.
<table><caption>**Table 2**
Focus addressed in framework.</caption><thead><tr><td>Data source name</td><td>Type of events</td></tr></thead><tbody><tr><td>OpenWeather</td><td>Storm, tornado, strong winds, etc</td></tr><tr><td>Marine Traffic</td><td>Route deviation, congestion, maritime traffic incident</td></tr><tr><td>Waze, GoogleMaps</td><td>Road accident, route deviation</td></tr><tr><td>Smart container</td><td>Temperature, humidity, pressure variation, etc.</td></tr><tr><td>Twitter</td><td>Storm, strike, congestion, etc</td></tr><tr><td>NewsApi, Eventregistry<sup>a</sup></td><td>Strike, congestion, etc</td></tr><tr><td>Avurnav</td><td>Regulation, traffic incident</td></tr></tbody></table>

Metadata is not collected or stored in our data processing module database. The data processing module does not collect personal data. Data that directly or indirectly identifies a human being is not collected. This limit concerns confidentiality and data security.

## 3.2. Information processing module

In this section the focus is made on the processing of textual data to extract random events. The section is composed of four sub-sections which are elaborated below: (1) data pre-processing; (2) creation of word embeddings; (3) symptom detection; and (4) event identification.

### 3.2.1. Data pre-processing

Data pre-processing is an important step in data mining process; it permits to improve machine learning techniques performance. Indeed, textual data often contains fuzziness and is very often unstructured. According to Vijayarani et al. (2015), “pre-processing method plays a very important role in text mining techniques and applications. It’s the first step in the text mining process”. This data pre-processing has three major roles: (1) data lemmatization; (2) data tokenization; and (3) stopwords removal:

* Data lemmatization: this method allows to group words of the same family together: it is a lemma grouping. For example, the words “is”, “are”, “be” will be transformed into “be” after the lemmatization. This permits to standardize the input data;

* Data tokenization: it allows to transform a text into a series of individual tokens based on delimiters such as the space between two words in most languages (french, english, etc.);

* stopwords removal: stopwords regroup all articles, prepositions, and pro-nouns, etc that do not give real sense to the textual data in NLP (Natural Language Processing) tasks. Indeed, stop word removal is the most frequently pre-processing task used in NLP. For instance,

“the”, “a”, “an”, “so”, “what” must be removed to the textual data before using it for analysis or prediction in NLP.

More details on textual data pre-processing methods can be found in (Vijayarani et al., 2015) which gives an overview on pre-processing techniques for text mining.

### 3.2.2. Creation of word embeddings

A word embedding is a vector representation of a word or phrase that has been obtained from a model that has been trained on a large corpus of textual data. The vector representation of the word obtained considers the context in which it is used in the text (Wiedemann et al., 2019). One of the most used tools for creating word embeddings is “Bidirectional Encoder Representations from Transformers” (BERT). It was proposed by Google in 2018 and is based on the transformers self-attention mechanism (Vaswani et al., 2017). Transformers are designed to process sequential input data, such as natural language, based on the core idea of the attention mechanism that mimics human attention and provides context for any position in the input sequence. The transformation has two key components: (i) the encoder and (ii) the decoder as shown in Fig. 4:

* Encoder: the encoder takes words simultaneously and generates a word embedding for each of the words. These word embeddings are vectors that encapsulate the meaning of the words. Similar words have closer numbers in their vectors.

* Decoder: As its name suggests, the decoder performs a task opposite to that of the encoder. It takes the vectors of embedding from the encoder and generates the corresponding words.

BERT is a type of deep learning model that consists of multiple encoders stacked on top of each other. The most common version of BERT has 12 encoders, but there is also a larger version called “BERT Large” that consists of 24 encoders and is more powerful but requires more computational resources. Each encoder in BERT has 512 entries, and each entry corresponds to a token. Authors such as Wadden et al. (2019); Wiedemann et al. (2019) have tested several words embedding solutions to classify textual data and conclude that BERT performs best among them. In the information processing module, after the data pre-processing phase, the pre-trained model BERT is used for the vector representation of our data. These vectors are later used by symptom detection and event identification models. Fig. 5 shows the process of creating the word embeddings. It takes as input texts and provides as output vector representations of each input token. The process of creating word embeddings with BERT consists of three phases: (i) the

<figure><img src="image_5.png" /><figcaption>Fig. 3. Schematic diagram of data collection module.</figcaption></figure>

<footnote>aEvent registry: https://eventregistry.org.</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 4. Transformers architecture (Vaswani et al., 2017).</figcaption></figure>

first phase is to transform the input data into a sequence of words (tokens). The first and last tokens correspond to special tokens. The first token, "[CLS]" allows the use of BERT for text classification tasks and the last token "[SEP]" on the other hand specifies the end of the input text. (ii) The sequence of words is then embedded into vectors (input embedding). The Embedding of inputs are finally processed by (iii) the encoding part of the transformer (Vaswani et al., 2017). The output is a set of extracted features embedding (output Embedding), each of which represents the local context for the corresponding words in the text (Chiu and Baker, 2020).

More notions about the creation of input embedding and attention mechanism are available in (Chiu and Baker, 2020; Usuga-Cadavid et al., 2022; Vaswani et al., 2017) works.

### 3.2.3. Symptom detection

A symptom can be defined as any manifestation that reveals the occurrence of a random event. The objective of the symptom detection model is to determine whether the input data is a manifestation of a random event that may disrupt one of the stages of the multimodal transport chain. From a functional point of view the symptom detection model is an essential filter that allows the event identification model to better target random events.

#### 3.2.3.1. Description of symptom detection model.
We approach symptom detection in the risk detection system as a binary classification problem that takes as input the words embeddings and provides as output a score indicating if this input is a symptom. To do this we use the fine-tuning technique that allows BERT to solve specific tasks. The authors in Devlin et al. (2019) state that the fine-tuning process is straightforward since the transformer's self-attention mechanism allows BERT to model many downstream tasks. The fine-tuning process consists of adding an additional layer of neural network after the final layer of BERT and training the resulting network.

The fine-tuning process is performed using a classifier of a neural network to specialize BERT for detecting textual data that contains information related to random events disrupting the multimodal transport flow. A supervised learning approach is used to train the classifier to recognize the symptoms. The latter provides a score $\alpha$ in [0, 1] as output.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Words embedding creation process.</figcaption></figure>
This $\alpha$ score is the certainty of the model that the input data is a symptom. Data is considered as a manifestation of a random event if $\alpha$ tends to 1 and conversely if $\alpha$ tends to 0, the data does not present any symptom. The corpus of data used for training the classifier is presented in the next subsection.

**Fig. 6** presents the architecture of the symptom detection model. An artificial neural network layer has been added to the output of BERT to perform the data classification.

#### 3.2.3.2. Implementation of the symptom detection model.
In the implementation of the symptom detection and event identification models, two categories of events were considered based on the nature, availability, and quantity of annotated data available for a supervised learning approach. These categories are severe weather events and traffic restrictions, as there are many annotated data sets available for these types of events. An event category is a group of events that are classified together based on their source.

In this section, a training data set and an implementation of the symptom detection model is presented.

#### 3.2.3.3. Available data set.
In the literature authors such as Dabiri (2018); Mazoyer et al. (2020); McMinn et al. (2013); Mele and Crestani (2019) have built up corpus of labeled textual data that can be used to train event extraction models. This data is available on demand for research purposes. For instance, Mele and Crestani (2019) have built a corpus of English textual data composed of news articles, RSS (Really Simple Syndication) feeds and tweets. The authors harvested four months (from March to June 2016) from nine different sources the textual data and labeled them according to the types of events they relay. In total the corpus contains 147,000 documents so 4000 were tagged according to 57 media events (tornado, flood, storm, etc.). McMinn et al. (2013) proposes a large corpus of tweets in English for event identification. This corpus offers 120 million tweets collected over a month. To our knowledge, this is the largest corpus of Twitter data currently available, and it contains more than 150,000 tweets tagged according to at least 9 categories of events (sports, accidents and natural disasters, economy, etc.). The authors in (Mazoyer et al., 2020) propose a labeled corpus of tweets in French (Event 2018) for event identification. These data collected over a period of one month (July to August 2018) and were manually tagged according to 17 event categories (art, disaster and accident, climate, sports, etc.). Dabiri (2018) proposes a corpus of 20,439 tweets tagged according to three categories of events. This corpus was created from the Twitter search API and each of the tweets was classified into one of the following three categories: (i) no traffic for all tweets that do not fit into the other two categories, (ii) traffic incident to label all tweets reporting non-recurring events that generate an abnormal increase in traffic or reduce the capacity of the transport infrastructure such as traffic accidents, broken down vehicles, highway maintenance, construction zones, road closures, vehicle fires, etc. Then (iii) the last category traffic conditions and information which reports any tweets reporting the release or reopening of roads that had already been affected by traffic events.

#### 3.2.3.4. Training data set.
While there is labeled data available to train event identification models, there is a lack of open access data corpora specifically related to random events that disrupt multimodal transport flow. Out of the four references cited, only a small portion of the data from (Dabiri, 2018) and (Mele and Crestani, 2019) could be used in this work because the word embedding model BERT only considers the first 510 words in the input text, and many news articles are longer than this limit. This limitation caused many news articles to be discarded. In addition to the existing data corpus, a new corpus of 47,500 tweets was created by querying the Twitter API over the course of one month (April 2021) using a data collection module. Twitter social network is recognized as a major source of information on public events. Twitter offers a

platform for retrieving textual data that can help us understand public opinion, measure interest in certain types of subjects or detect unpredictable events in real time. Indeed, this social network has been used to extract events (Coche, 2018) or to predict stock markets (Pagolu et al., 2016), among other applications (Mazoyer et al., 2020). In addition to being used in several event extraction projects, the Twitter API can be used to target and query events. Twitter allows data from a specific part of the world to be targeted and queried. It is therefore suitable as a source for the implementation of the data collection module. To create our corpus of data we therefore requested the Twitter API with keywords that correspond to severe weather and traffic incidents events (storm, tornado, congestion, strike, etc.) and the geographical coordinates (latitude and longitude) of the locations in the world that interest us. The API response is in JSON (JavaScript Object Notation) format, and we retrieve the following attributes:

* Id: this is the unique identifier of the tweet;

* Created_at: corresponds to the date on which the tweet was published;

* Full_text: this is the content of the tweet;

* Timestamp_run: this is the date on which the tweet was entered into the database;

* Longitude and latitude: the geographical coordinates where the tweet was published.

**Fig. 7** shows the structure of the data stored in the database.

To ensure that the model we want to implement accurately captures the entire semantic meaning of the text data, we trained the symptom detection model using text data with a length of less than 510 tokens. To do this, we manually annotated 2300 texts form corpus of 47,500 tweets to determine whether they contained symptoms related to freight flow. We also included 320 RSS feed articles and 97 tweets from the corpus annotated for extreme weather events by Mele and Crestani (2019) in our sample data. Additionally, we included only 1000 tweets from the data corpus of Dabiri (2018) to avoid the classifier learning to recognize only the symptoms specific to traffic incidents. We made sure to balance the two categories of symptoms (extreme weather and traffic restrictions) in the data. The distribution of the data by source is presented in Table 3.

#### 3.2.3.5. Training.
According to Vabalas et al. (2019), a reliable approach to evaluating the performance of a machine learning model is to train the model with available data and evaluate its classification performance using newly collected data or a separate data set. Another approach, also known to be reliable, is to separate the data corpus into two sets (a training set and a validation set) before developing a model and to use only the validation set to evaluate the performance of the model. To ensure the performance of the symptom detection model, the data set was decomposed into two parts: (i) the training data which corresponds to 80 % of the data set and (ii) the validation data (20 % of the data set).

#### 3.2.3.6. Metrics.
According to Haouari et al. (2020), the confusion matrix is often used for the evaluation of classifiers in the field of machine learning. The confusion matrix is a matrix of size $n*n$ where n represents the number of classes and allows us to observe how the model classifies the test set with respect to its true class. In the case of the classifier of the symptom detection model, it is a two-class problem (True: symptom; False: no symptom) as presented by Table 4.

There are several metrics that can be derived from the confusion matrix to evaluate the performance of the classifier. Some examples include:

* Accuracy = $\frac{TP+TN}{TP+TN+FP+FN}$, which is the percentage of correct prediction of the model on the total number of entries.
<figure><img src="image_8.png" /><figcaption>Fig. 6. Symptom detection process.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Illustration of data stored in the database.</figcaption></figure>

<table><caption>**Table 3**
Distribution of the data set.</caption><thead><tr><th>Sources</th><th>Symptom</th><th>No symptom</th><th>Total</th></tr></thead><tbody><tr><td>Tweets</td><td>2030</td><td>1367</td><td>3397</td></tr><tr><td>Flux RSS</td><td>330</td><td>20</td><td>350</td></tr><tr><td>Distribution (%)</td><td>63 %</td><td>37 %</td><td>100 %</td></tr></tbody></table>

* Precision = $\frac{TP}{TP+FP}$, which is the proportion of correct predictions with the model validation data set. It represents the ratio between the number of correct predictions and the total number of input samples.

* Recall = $\frac{TP}{TP+FN}$ represents the percentage of true positives that have been identified by the model.

<table><caption>**Table 4**
Illustration of confusion matrix.</caption><thead><tr><th></th><th>False</th><th>True</th></tr></thead><tbody><tr><th>False</th><td>TN<sup>a</sup></td><td>FP<sup>b</sup></td></tr><tr><th>True</th><td>FN<sup>c</sup></td><td>TP<sup>d</sup></td></tr></tbody></table>

* F1_score = $\frac{2 * Recall * Precision}{Recall + Precision}$ which is the harmonic means of precision and recall, it is useful when we try to find a balance between precision and recall.

<footnote><sup>a</sup>TN : True Negative.</footnote>

<footnote><sup>b</sup>FP : False Positive.</footnote>

<footnote><sup>c</sup>FN : False Negative.</footnote>

<footnote><sup>d</sup>TP : True Positive.</footnote>
<table><caption>**Table 5**
Metrics of the symptom detection model.</caption><thead><tr><th>Métriques</th><th>α = 0.50</th><th>α = 0.75</th><th>α = 0.9</th></tr></thead><tbody><tr><td>Accuracy</td><td>0.80</td><td>0.80</td><td>0.77</td></tr><tr><td>Précision</td><td>0.85</td><td>0.91</td><td>0.95</td></tr><tr><td>Recall</td><td>0.84</td><td>0.76</td><td>0.76</td></tr><tr><td>F<sub>1</sub>-score</td><td>0.86</td><td>0.82</td><td>0.84</td></tr></tbody></table>

Table 5 presents the results obtained on the validation data set according to different values of the probability threshold and Fig. 8 presents the confusion matrix for the considered thresholds. Since the objective of the symptom detection model is to characterize the data as likely to refer to a disruptive event or not, it must have a low false positive rate. According to the confusion matrix 8c, a value of α = 0.9 is best suited to our context.

### 3.2.4. Event identification

The event identification model is designed to identify random events that may disrupt the flow of containers. It takes the filtered word embeddings of text data as input and produces a list of identified events as output.

#### 3.2.4.1. Description of event identification model.
The event identification model is based on the work of (Coche, 2018), which presents a method for identifying named entities in text data related to crisis situations. This method involves using a “vector space” and a “label propagation” algorithm. First, textual data is vectorized using the BERT embedding method to create a vector space with a semantic similarity distance. The vector space consists of clusters of vectors representing semantically similar entities. The unlabeled data is then transformed into vectors and projected into the vector space. The label propagation algorithm is used to assign labels to the data based on their membership

in one of the clusters in the vector space. In our work, the identification of random events disrupting multimodal transport flow is treated as a multi-classification problem based on event extraction in textual data. Event extraction in our work involves calculating the semantic similarity between two entities. Semantic similarity is a measure of the similarity in meaning between two entities, as described in (Resnik, 1995). It is widely used for the classification of textual data (Koutroumbas and Theodoridis, 2008). We use semantic similarity in our model to extract random events that may disrupt the multimodal transport flow. The event identification model consists of two steps: (1) creating a reference word embedding; and (2) calculating the semantic proximity between the reference embedding and any other suspected word embedding that may represent an event of interest (a random event disrupting the flow of goods). The process of creating word embeddings (illustrated in Fig. 5) is used to create vector representations of entities that are known to represent random events disrupting the flow of goods and entities that are likely to be random events. Then, a semantic similarity metric is used to determine the semantic proximity between the reference embedding and the embedding of other entities.

1. Creating the reference “Word embedding”: it involves two steps. First, explicit textual data (reference texts) containing examples of disrupting risk events (such as storm, strike, congestion, etc.) are identified to create reference embeddings. This process creates a vector representation for each token in the text, considering the context of the text. The second step involves extracting the word embedding for the event of interest. For example, using the text “Congestion at Shanghai port threatens to hamper global trade” to create a reference embedding for the event category traffic restriction would involve applying the word embedding creation process on this sentence and retrieving the vector corresponding to the word “congestion”.

<figure><img src="image_10.png" /><figcaption>(a) Confusion matrix for α = 0.5</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(b) Confusion matrix for α = 0.75</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(c) Confusion matrix for α = 0.9</figcaption></figure>

<caption>**Fig. 8.** Confusion matrices for various α values.</caption>
2. Semantic similarity computation: the process involves calculating the semantic similarity score between the reference word embeddings and the embeddings of textual data that has been identified as positive by the symptom detection model. To do this, a specific semantic similarity metric must be chosen. Many metrics have been proposed to evaluate the semantic similarity between two vector representations of words such as the Spearman correlation, or the cosine distance (Hiebel et al., 2022). More notions on semantic similarity metrics are available in the work of (Patwardhan et al., 2003). The authors in this article have done a comparative study on some metrics.

In our event identification model, cosine similarity which is the most used vector metric for computing semantic similarity between two texts (Hiebel et al., 2022) is used. Cosine similarity is computed by measuring the cosine of the angle between two vectors. Equation (2) presents the formula used to calculate the cosine similarity between two vectors X and Y (Koutroumbas and Theodoridis, 2008). Fig. 9 presents the process of calculating semantic similarity. It produces a score $\beta$ in [0, 1] indicating the degree of similarity between the vectors. Therefore, a threshold must be chosen to confirm the similarity between the two vectors.

$$S_{\text{cosinus}}(X, Y) = \frac{X^T Y}{\|X\| \|Y\|} \qquad (2)$$

$$\text{where } \|X\| = \sqrt{\sum_{i=1}^{n} x_i^2} \text{ and } \|Y\| = \sqrt{\sum_{i=1}^{n} y_i^2}.$$

Table 6 presents the semantic similarity score between the word embedding reference “congestion” and the tokens of the following text: “Recurrent congestion in the port of Le Havre is a source of delay for the container ship” Ref_emb denotes the reference word embedding which corresponds to the vector representation of the word congestion and Emb_tokeni denotes the word embedding of an input token. Cos(Ref_emb, Emb_tokeni) is the similarity score obtained by applying the cosine distance.

### 3.2.4.2. Implementation of event identification model.
This part presents the implementation phases of the event identification model.

### 3.2.4.3. Creation of reference word embedding and semantic similarity computing.
To create the reference Word embedding, we used a pretrained BERT template that we downloaded from HuggingFace,<fnref n="6" /> as well as reference sentences. The technical configuration of the BERT template is available below:

* vocab_size = 30,522;

* hidden_size = 768;

* num_hidden_layers = 12;

* numحيثية_هدهد = 12;

* intermediate_size = 3072;

* max_position_embeddings = 512.

For the creation of the reference word embedding for the extreme weather we used the following reference text: “A violent storm is approaching the coast” and for that of the traffic restriction category the text “Recurrent congestion in the port of Le Havre is a source of delay for the container ship” was considered. The choice of using these two sentences as references was motivated by the fact that they explicitly mention random events that disrupt the flow of goods during multimodal transport. The embedding of the word “storm” and that of “congestion” is then used for the calculation of semantic similarity with other word embedding.

### 3.2.4.4. Choice of the threshold for the semantic similarity metric.
The threshold of the semantic similarity metric $\beta$ determines whether a word embedding is considered like reference embedding. This threshold represents the minimum score required for a word, as represented by its embedding, to be considered an event of interest.

We are trying to find a threshold that will help us accurately detect events by measuring the scores of certain word embeddings and eliminating false positives. As an example, we can look on Fig. 10, the distribution of the semantic similarity of the words “storm,” “tornado,” “high wind,” and other words with the word embedding of “storm” in the sentence “A violent storm is approaching the coast.” From this data, we can see that the words “tornado” and “strong wind” are semantically like “storm.” Based on this information, we can conclude that a threshold of 0.5 would be suitable for detecting extreme weather events because it allows us to identify events that are semantically like the word “storm” and classify them as extreme weather.

### 3.2.4.5. Evaluation of event identification model.
To evaluate the performance of the event identification model, the data that are manifestations of random events from the data corpus presented in part 3.2.3 are used. As a reminder, it is annotated according to two categories of events: (i) extreme weather; (ii) traffic restriction. In this part of the evaluation of the risk identification model, we are using data characterized only as symptoms by the symptom detection model. Table 7 presents the events and their distribution in the data corpus.

The events identification is like a multi-classification problem according to three classes, the first two classes correspond to the categories of events, and the third one is the “no event” category. This last class stipulates that the model does not detect any random event that could disrupt the multimodal transport chain. To evaluate the event identification model, we use a confusion matrix, like how we evaluated the symptom detection model. The confusion matrix for the event identification model is shown in Fig. 11 after applying the model to the annotated data set and Table 8 present metrics obtained on the data set present in Table 7.

Although the confusion matrix and metrics are not perfect, the results obtained with the event identification model on the two categories of events are satisfactory to support the decision support. Indeed, out of 1322 real events signaling an extreme climate likely to disrupt the flow of containers, the event identification model manages to detect 1006 (i. e., a recall of 76 %) and out of 1038 data relaying events on the restriction of traffic it detects 702 (recall of 67 %) (see metrics in Table 8). In our work, we are in the presence of strong and rather redundant signals, the data in the same region of the world very often relay the same information. Thus, with these detection performances we are almost sure to detect the occurrence of random events during multimodal transport.

## 4. Framework validation – A proof of concept

This section aims to demonstrate that the proposed framework can permit to detect and identify in real-time disruption event risk during multimodal container transport. For this purpose, a real use case of the risk detection system is presented.

### 4.1. Use case description

Extreme weather refers to the occurrence of any event such as storms, floods, tornadoes, etc., which may disrupt multimodal transport flow. On April 14, 2022, South Africa, and more precisely the city of Durban, was hit by severe weather conditions that seriously damaged the infrastructure with devastated roads and collapsed bridges. Durban

<footnote n="6">⁶ HuggingFace : https://huggingface.co/.</footnote>
<figure><img src="image_13.png" /><figcaption>Fig. 9. Process of computing semantic similarity.</figcaption></figure>

<table><caption>**Table 6**
Example of application of the similarity measure.</caption><thead><tr><td>No.</td><td>Input tokens</td><td>Cos(Ref<sub>emb</sub>, Emb<sub>token</sub>)</td></tr></thead><tbody><tr><td>1</td><td>Recurrent</td><td>0.658</td></tr><tr><td>2</td><td>Congestion</td><td>0.960</td></tr><tr><td>3</td><td>Port</td><td>0.423</td></tr><tr><td>4</td><td>Le Havre</td><td>0.203</td></tr><tr><td>5</td><td>source</td><td>0.413</td></tr><tr><td>6</td><td>delay</td><td>0.629</td></tr><tr><td>7</td><td>container</td><td>0.381</td></tr></tbody></table>

<figure><img src="image_14.png" /><figcaption>Fig. 10. Illustration of the choice of threshold for event identification.</figcaption></figure>

<table><caption>**Table 7**
Events distribution in the data set.</caption><thead><tr><td>No</td><td>Event category</td><td>Number</td><td>Event</td></tr></thead><tbody><tr><td>1</td><td>Severe weather</td><td>1322</td><td>Storm, flood, tornado</td></tr><tr><td>2</td><td>Traffic restriction</td><td>1038</td><td>Congestion, strike, accident</td></tr></tbody></table>

<figure><img src="image_15.png" /><figcaption>Fig. 11. Confusion matrix for the event identification model.</figcaption></figure>

<table><caption>**Table 8**
Metrics of the event identification model.</caption><thead><tr><td>Metrics</td><td>Severe weather</td><td>Traffic restriction</td></tr></thead><tbody><tr><td>Accuracy</td><td>0.72</td><td>0.72</td></tr><tr><td>Precision</td><td>0.74</td><td>0.70</td></tr><tr><td>Recall</td><td>0.76</td><td>0.67</td></tr><tr><td>F1-score</td><td>0.75</td><td>0.68</td></tr></tbody></table>

is open to the Indian Ocean and its port is one of the main maritime terminals of the African continent. This flooding has been relied on social networks and by press articles as shown in Fig. 12.<fnref n="7" />

<footnote n="7">⁷ Traxens: https://next4.io/next4-becomes-a-traxens-company/.</footnote>
<figure><img src="image_16.png" /></figure>

Well, the flooding did some serious damage! Roads closed. Water disruption, communication disruption etc etc. Please everyone, we implore you: stay strong, stay safe! Roads are blocked and washed away. Help where you can.

<figure><img src="image_17.png" /></figure>

5:47 AM · Apr 16, 2022 · Twitter for iPhone

<caption>Fig. 12. Example of a tweet about extreme weather (flooding).</caption>

During this period, a container belonging to a customer of the company Traxens passed through the port of Durban. As the container moved through the port, it triggered the data collection module, which collected tweets about flooding that occurred during the container handling phase. Fig. 13 illustrates the route that the container took during its transit.

## 4.2. Data-set description

This subsection presents the structure of the data used for the real-time risk detection system application case.

### 4.2.1. Traceability and visibility data

The company Traxens offers a traceability and real-time visibility solution to their customers. This solution consists of a tracker attached to the container and a web platform that allows customers to track the progress of their containers. The solution provides Traxens's customers with data on (1) changes in the conditions of container transport; and (2) transport events that occur during the transit of the container:

1. The data on the conditions of transport:

* Temperature;

* Humidity;

* Pressure;

* Shocks.

2. Transport events:

* The starting point;

* The arrival events;

* The intermediate points (transit area, storage area, intermediate port, etc.);

* The position of the container (longitude and latitude);

* Craning: Handling of the container with a crane.

The risk detection system focuses on the transport events, specifically on the position of the container. The position of the container is used by the real-time risk detection system to gather textual data.

### 4.2.2. Textual data

The textual data used by the detection system was collected from the Twitter API using the container position and the keywords "storms", "floods", "high wind", and "tornado". A 20-km radius around the container position and a one-week horizon were defined for collecting tweets. This means that any tweet that was published in the last 7 days within a 20 km area around the container's position and satisfying the conditions defined in the query was collected. The position of the container at the port of Durban was retrieved by visibility data on April 19, 2022. On that day 21 tweets were collected, the oldest of which was from April 12 ("The rains are back. With strong gusts of wind.") and the most recent from April 19 (tweet 1). The structure of the textual data is presented in Fig. 7.

<figure><img src="image_18.png" /><figcaption>Fig. 13. Itinerary of the container obtained via the Traxens platform.</figcaption></figure>
## 4.3. Event detection and identification

After removing the retweets (10), the remaining tweets (11) are processed by various information processing models. The data is first pre-processed by a data pre-processing module to create a more concise representation of the texts. Table 9 presents the collected data content. The symptom identification model is then used to determine if any of the data represents random events that could disrupt the multimodal transport flow. This involves converting the input text into word embeddings. Table 10 presents the results of applying the symptom identification model to the data, using an α threshold of 0.9 for symptom detection. All data that has been identified as a manifestation of a random event is used as input for the event detection model. This process involves using the word embeddings of each input data token to calculate semantic similarity with a reference embedding (storm). A threshold of 0.5 is used when applying cosine semantic similarity metrics. Table 11 presents the results of applying the event detection model to the input data that was identified as a manifestation of a random event based on the results of the symptom identification model.

## 4.4. Result and framework validation

In this case study, the data collection module of the real-time risk detection system collected 21 tweets related to the city of Durban using visibility data from our industrial partner. The risk detection system used the symptom detection model to classify the collected data and detected three tweets as representing random events that could potentially disrupt the multimodal transport chain, while the rest of the data was excluded. The event identification model then confirmed the presence of extreme weather at the port of Durban based on these three texts. The port of Durban is a regular POI (Point Of Interest) for our industrial partner's customers. A post-transport analysis of this container with traceability data shows that the handling operations took 6 days against an average of one day in normal transport conditions. The real-time risk detection system can identify random events that disrupt the multimodal transport flow using its data collection and information processing modules.

<table><caption>**Table 9**
Use case data.</caption><thead><tr><th>No.</th><th>Textual content</th></tr></thead><tbody><tr><td>Txt.1</td><td>tweet 1<sup>a</sup></td></tr><tr><td>Txt.2</td><td>Thank you God for saving us from Storm and sever floods' May those who are victims of floods stay strong in all ways.</td></tr><tr><td>Txt.3</td><td>Living in KZN makes you strong. Survived the lockdown, the looting, the vicious storm and tonight another sabotage of the substation. Half the town is again without power. We bang our heads against the ANC-brick wall of the lawlessness and keep smiling.</td></tr><tr><td>Txt.4</td><td>No wind ? It was pretty strong yesterday afternoon and followed by light rain.</td></tr><tr><td>Txt.5</td><td>Well, the flooding did some serious damage! Roads closed. Water disruption, communication disruption etc. Please everyone, we implore you: stay strong, stay safe! Roads are blocked and washed away. Help where you can.</td></tr><tr><td>Txt.6</td><td>The rains are back. With strong gusts of wind.</td></tr><tr><td>Txt.7</td><td>IN PICS- The ongoing showers has caused a road collapsed at Nhlungwane, north of Durban, as the heavy rains continues to wreak havoc across the province of KwaZulu-Natal.</td></tr><tr><td>Txt.8</td><td>I doubt bro the current is really strong and the wind was not even helping matters but let's just pray for a miracle coz we all need 1 here in Durban.</td></tr><tr><td>Txt.9</td><td>Durban is flooding guys check on your friends and family members it really bad and it getting worse now, wind is getting strong with non stop heavy rain.</td></tr><tr><td>Txt.10</td><td>Fortunately it's not my garden. Used to have one though, slightly bigger. Not sure if the dirt road can be travelled on yet with my little car. Will find out tomorrow. But jeez, it's not stopping & the wind is strong as well. Add the Corrugated roofing, I can't think.</td></tr></tbody></table>

<table><caption>**Table 10**
Output of the symptom identification model.</caption><thead><tr><th>No.</th><th>Text</th><th>α Value</th><th>Symptom</th></tr></thead><tbody><tr><td>Txt.1</td><td></td><td>0.47</td><td>No</td></tr><tr><td>Txt.2</td><td></td><td>0.61</td><td>No</td></tr><tr><td>Txt.3</td><td></td><td>0.47</td><td>No</td></tr><tr><td>Txt.4</td><td></td><td>0.55</td><td>No</td></tr><tr><td>Txt.5</td><td></td><td>0.82</td><td>No</td></tr><tr><td>Txt.6</td><td></td><td>0.90</td><td>Yes</td></tr><tr><td>Txt.7</td><td></td><td>0.37</td><td>No</td></tr><tr><td>Txt.9</td><td></td><td>0.96</td><td>Yes</td></tr><tr><td>Txt.10</td><td></td><td>0.60</td><td>No</td></tr><tr><td>Txt.11</td><td></td><td>0.95</td><td>Yes</td></tr></tbody></table>

<table><caption>**Table 11**
Output of the event detection model.</caption><thead><tr><th>No.</th><th>Text</th><th>β Value</th><th>Event category</th></tr></thead><tbody><tr><td>Txt.6</td><td></td><td>0.62</td><td>Severe weather</td></tr><tr><td>Txt.9</td><td></td><td>0.60</td><td>Severe weather</td></tr><tr><td>Txt.11</td><td></td><td>0.68</td><td>Severe weather</td></tr></tbody></table>

# 5. Generalization of the real-time risk detection system

This section generalizes the proposed real-time risk detection system and provides a robust validation of its performance through extensive simulations. The goal is to demonstrate the system's practicality, scalability, and effectiveness in identifying and mitigating risks in multimodal container transport.

## 5.1. General operation of the real-time risk detection system

The real-time risk detection system operates through a series of interconnected modules, each designed to perform specific tasks that collectively enable the identification and mitigation of risks during multimodal transport. The system's operation can be summarized in the following steps:

1. Data Collection Module. The system continuously collects two types of data:

* Traceability and Visibility Data: This includes real-time information about the container's location, environmental conditions (temperature, humidity, pressure, shocks), and transport events (e.g., craning, transit points).

* Textual Data: The system gathers relevant textual data from social media platforms (e.g., Twitter) using predefined keywords (e.g., "storms," "floods") and geolocation parameters (e.g., a 20-km radius around the container's position).

2. Data Preprocessing Module: The collected textual data is pre-processed to remove noise (e.g., retweets, irrelevant content) and prepare it for analysis. This step ensures that only meaningful data is passed to the subsequent modules.

3. Symptom Identification Module: The preprocessed data is analyzed using a symptom identification model to detect potential risks. This model uses word embeddings and a predefined threshold (e.g., α = 0.9) to classify whether the data represents a random event that could disrupt the transport flow.

4. Event Detection Module: Data identified as symptomatic of a random event is further analyzed using an event identification model. This model calculates the semantic similarity between the input data and reference embeddings (e.g., "storm") to categorize the event (e.g., severe weather). A threshold (e.g., β = 0.5) is used to confirm the event's relevance.

5. Risk Mitigation Module: Once a risk is confirmed, the system triggers alerts and provides actionable insights to stakeholders (e.g., logistics

<footnote><sup>a</sup>We prefer not to display this tweet because it has a racial connotation.</footnote>
managers, transport operators). This enables timely decision-making to mitigate the impact of the detected risk on the transport chain.

## 5.2. Robust validation of the framework

For the robust validation of the framework, we conducted simulations to evaluate the system's performance. In this subsection we also present a comparison of the performance of the proposed framework with the framework of (Janjua et al., 2023). These authors also propose a framework to identify supply chain disruption events using twitter feeds in real time. The simulations included:

1. Scenario 1: Extreme weather events (e.g., storms, floods) in different geographical regions.

2. Scenario 2: Traffic restriction:

* Infrastructure disruptions (e.g., collapsed bridges, road closures).

* Operational delays (e.g., port congestion, craning delays).

To simulate the operation of our system, we used tweets collected on the twitter API without considering the presence of a container for data collection. The aim of this simulation is to check the detectability of a risk using the symptom detection and event identification model when called upon in real time. To do this, we used a dataset consisting of 11,301 text data items labeled according to 3 events categories: (1) No event, (2) severe weather and (3) traffic restriction. The distribution of the dataset by event category is shown in Fig. 14.

The results of the simulations demonstrated that the system consistently identified risks with an average accuracy of 85 % across all scenarios. The system's ability to adapt to different transport conditions and geographies further highlights its practicality and scalability. The system's performance was evaluated using the following metrics:

* Accuracy: The percentage of correctly identified risks out of the total risks detected.

* Precision: The percentage of non-risks incorrectly classified as risks.

* Response Time: The time taken by the system to detect and alert stakeholders about a risk.

The results, summarized in Table 12, demonstrate the system's effectiveness in real-time risk detection and mitigation.

Fig. 15 shows the result of the comparison of metrics for the detection of random events between the approach proposed (Janjua et al., 2023) and the real-time event detection system proposed in this work. The result of this comparison shows that our framework achieves higher performance (F1-score) for real-time event detection.

<figure><img src="image_19.png" /><figcaption>Fig. 14. Event category distribution dataset.</figcaption></figure>

<table><caption>**Table 12**
Performance metrics of real-time risk detection system.</caption><thead><tr><th>Metrics</th><th>Simulation results for severe weather</th><th>Simulation results for Traffic restriction</th></tr></thead><tbody><tr><td>Accuracy</td><td>0.86</td><td>0.85</td></tr><tr><td>Precision</td><td>0.87</td><td>0.91</td></tr><tr><td>Recall</td><td>0.88</td><td>0.87</td></tr><tr><td>F1-score</td><td>0.86</td><td>0.88</td></tr><tr><td>Response time</td><td>276 s.</td><td>192 s.</td></tr></tbody></table>

<figure><img src="image_20.png" /><figcaption>Fig. 15. Performance comparison (Recall, precision and F1-score).</figcaption></figure>

## 5.3. Practical implications

The robust validation of the framework demonstrates its practicality and effectiveness in real-world scenarios. The system's ability to detect risks in real-time provides significant value to stakeholders in the multimodal transport industry, including:

* Logistics Managers: Enables proactive decision-making to minimize delays and disruptions.

* Transport Operators: Reduces the risk of damage to goods and infrastructure.

* Customers: Ensures timely delivery of goods, enhancing customer satisfaction.

In conclusion, the proposed real-time risk detection system has been rigorously validated through real-world implementation and extensive simulations. The results confirm its practicality, scalability, and effectiveness in identifying and mitigating risks during multimodal container transport.

# 6. Discussion

In this paper we propose a system for detecting risks in real-time during the transport of containers via multiple modes of transport to aid those involved in the transport process with risk management. While the topic of risk management in supply chains has been extensively studied, there has been limited focus on detecting risks in real-time during multimodal container transport. The literature review shows the need for such a system to increase the resilience of the multimodal transport chain by increasing the reactivity and proactivity of the actors (Wattanakul et al., 2018). Container transport risk management tools make container traceability and visibility data available (Bukkapatnam and Komanduri, 2007). On the other hand, there are more sources of textual data that can inform about the occurrence of random events disrupting the container transport chain (Janjua et al., 2023). In addition, it is important to note the increasing use of artificial intelligence and natural language processing techniques to manage risk in the supply chain (Baryannis et al., 2019). Thanks to these techniques, authors
propose solutions to reduce the carbon footprint of transport, optimize or predict traffic in the taming chain and manage risks. However, these solutions are not applied at the container scale. In this present study, we propose a framework to detect in real time random events disrupting the multimodal transport flow of containers. This framework relies on traceability/visibility data and heterogeneous textual data. A data collection model that takes in the location of the container is used to collect the textual data in real time. We then use NLP techniques to process the data to standardize it before using symptom detection and event identification models to extract the events disrupting the transport chain. The symptom detection model is a binary classification model, and the event identification model is based on semantic similarity calculation.

The proposed risk management framework allows to collect and process in real time heterogeneous data to extract events that could disrupt the multimodal container transport chain.

# 7. Conclusion, limitations, and future work

## 7.1. Limitations and future work

While the topic of risk management in supply chains has been extensively studied, there has been limited focus on detecting risks in real-time during multimodal container transport. To address this gap in literature, this work proposes a system for detecting risks in real-time during the transport of containers via multiple modes of transportation to aid those involved in the transportation process with risk management. Although we have demonstrated the effectiveness of this system through a real-case application, it does have some limitations.

### 7.1.1. Limitation on symptom detection and event identification models

Symptom detection and event identification models have a fairly high number of false negatives. To improve the performance of the system, it will be necessary to propose methods of calibration of these models in future work. To do this, we will draw inspiration from the work of Gasmi (2022), who proposes a text classification model based on BERT while using a hyperparameter optimization algorithm. In this vein, the authors in (Qin et al., 2024) propose an optimization method to improve the gradient method used in neural networks with a view to improving their performance for classification prediction tasks. In our future work, we propose to improve the classification performance of our symptom detection and event identification models using the Conic Multivariate Adaptive Regression Spline (CMARS) proposed by (Özmen et al., 2011) which are methods derived from Multivariate Adaptive Regression Spline (MARS) presented in Friedman (1991)'s work. MARS is a non-parametric regression method used to model the non-linear relationships that may exist between different variables. The MARS algorithm consists of two main parts: the forward and backward stepwise algorithms. CMARS is an alternative to the backward algorithm. In the CMARS method, a penalised residual sum of squares (PRSS) is used as a Tikhonov regularisation problem, and this two-objective optimization problem has been addressed using conic quadratic programming (Nesterov and Nemirovskii, 1994; Priyanto et al., 2020), which is a continuous optimization technique. In our future work, we will use the CMARS regression method to improve the traditional gradient method used in neural networks. We can draw on the work of Çevik and Weber (2017), for example, who uses a variant of the MARS algorithm, Voxel-MARS, for diagnosing neurodegenerative diseases. Based on brain image analysis, the authors in (Çevik et al., 2017) used MARS method as classifier in order to detect Alzheimer's disease in early stages of progression. The results presented in this work are promising and provide a good basis for improving our symptom and event detection models. Our ambition is to improve the performance of our symptom and event identification models through better parameter estimation for classification tasks. In addition, the model we use for creation of word embedding in the real-time risk event detection system only considers

the first 510 words of an input text. This forced us to implement our symptom identification and event detection models with texts of 510 words or less. In practice, some news articles are much longer than the limit imposed by BERT. In the current state, our risk detection system would not be able to process this type of text in its entirety. It is necessary to get rid of this limit so that the real-time risk events detection system can consider all the data sources.

### 7.1.2. Limits concerning availability of container tracking and visibility data

The real-time risk events detection system was implemented based on the availability of container location data from container tracking and visibility. This simplifying assumption is not always true for all transport. For example, in the following cases: (i) container in the bottom of the hold; (ii) tracker failure; and (iii) no relay antenna nearby, visibility and tracking data are unavailable. To overcome this assumption, it is important to extend the data sources to be integrated in this system such as the AIS data of the vessels.

## 7.2. Conclusion

The globalization and complexity of the container supply chain makes it very susceptible to random events during the multimodal goods transport. For example, the blocking of the Suez Canal by a container ship has disrupted the global supply chain by causing congestion in major ports and a worldwide decrease in shipping capacity. A system that provides visibility into the occurrence of random events during multimodal container transport could improve the responsiveness and risk proactivity of the stakeholders involved in the supply chain. In this study, we design a real-time risk events detection system for identifying potential disruptions during the multimodal transport of containers is made up of two main modules. The first module is responsible for collecting all necessary data for detecting random events, while the second module uses a symptom detection model to filter the collected data through pre-processing and transformation into a more abstract form. The filtered data is then analyzed using an event identification model to extract any random events that could disturb the transport process. The system uses event extraction techniques and data from various sources to detect these potential disruptions. A demonstration of the real-time risk events detection system's ability to automatically process data is presented using text data from the Twitter API and container location data from our industrial partner's container tracking system. This proof of concept shows how the system can be used to identify risks in real-time. Validation of the real-time risk detection system is based on a single real-case application case for a basic multimodal transport case. Given the complexity of the multimodal transport chain and the availability of more and more textual data sources that can be used as input to the framework, it is important to address the scalability of the real-time risk detection system before its use. Future work considering the frequency with which we will collect textual data based on container visibility data is required to control the volume of data to be processed in real time by the system and facilitate its industrialization.

# CRediT authorship contribution statement

**Cheik Ouedraogo:** Writing – review & editing, Writing – original draft. **Rodolphe Barlogis:** Visualization, Formal analysis, Conceptualization. **Aurelie Montarnal:** Writing – review & editing. **Didier Gourc:** Validation, Supervision. **Cedric Rosemont:** Project administration, Methodology.

# Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Cheik OUEDRAOGO reports equipment, drugs, or supplies was provided
by Traxens. If there are other authors, they declare that they have no
known competing financial interests or personal relationships that could
have appeared to influence the work reported in this paper.

Data availability

The authors do not have permission to share data.

References

Aboutorab, H., Hussain, O.K., Saberi, M., Hussain, F.K., 2022. A reinforcement learning-based framework for disruption risk identification in supply chains. Future Gener. Comput. Syst. 126, 110-122.

Al-Ruithe, M., Benkhelifa, E., Hameed, K., 2019. A systematic literature review of data governance and cloud data governance. Personal Ubiquitous Comput. 23, 839-859.

Alyami, H., Lee, P.T.W., Yang, Z., Riahi, R., Bonsall, S., Wang, J., 2014. An advanced risk analysis approach for container port safety evaluation. Marit. Pol. Manag. 41, 634-650.

Baryannis, G., Dani, S., Antoniou, G., 2019. Predicting supply chain risks using machine learning: the trade-off between performance and interpretability. Future Gener. Comput. Syst. 101, 993-1004.

Bazaluk, O., Kotenko, S., Nitsenko, V., 2021. Entropy as an objective function of optimization multimodal transportations. Entropy 23, 946.

Boehm, M., Arnz, M., Winter, J., 2021. The potential of high-speed rail freight in Europe: how is a modal shift from road to rail possible for low-density high value cargo? European Transport Research Review 13, 4.

Bukkapatnam, S.T., Komanduri, R., 2007. Container integrity and condition monitoring using rf vibration sensor tags. In: 2007 IEEE International Conference on Automation Science and Engineering. IEEE, pp. 585-590.

Çevik, A., Weber, G.W.,Eyüboğlu, B.M., Oğuz, K.K., The Alzheimer's Disease Neuroimaging Initiative, 2017. Voxel-MARS: a Method for Early Detection of Alzheimer's Disease by Classification of Structural Brain MRI, 258, pp. 31-57.

Chislov, O., Bogachev, V., Zadorozhniy, V., Kravets, A., Bakalov, M., Bogachev, T., 2021. Mathematical modeling of cargo flow distribution in a regional multimodal transportation system. Transport problems 16.

Chiu, B., Baker, S., 2020. Word embeddings for biomedical natural language processing: a survey. Language and Linguistics Compass 14, e12402.

Coche, J., 2018. Extraction Automatisée De Modele De Situation De Crisea Partir De Données Issues De Réseaux Sociaux.

Dabiri, S., 2018. Tweets with traffic-related Labels for Developing a Twitter-based Traffic Information System.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K., 2019. Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding, pp. 4171-4186.

Dong, C., Boute, R., McKinnon, A., Verelst, M., 2018. Investigating synchronodality from a supply chain perspective. Transport. Res. Transport Environ. 61, 42-57.

Du, S., Li, T., Gong, X., Horng, S.J., 2020. A hybrid method for traffic flow forecasting using multimodal deep learning. Int. J. Comput. Intell. Syst. 13, 85-97.

Dumitrascu, O., Dumitrascu, M., Dobrotă, D., 2020. Performance evaluation for a sustainable supply chain management system in the automotive industry using artificial intelligence. Processes 8, 1384.

Ebinger, F., Omondi, B., 2020. Leveraging digital approaches for transparency in sustainable supply chains: a conceptual paper. Sustainability 12, 6129.

Fattahi, Z., Behnamian, J., 2022. Location and transportation of intermodal hazmat considering equipment capacity and congestion impact: elastic method and sub-population genetic algorithm. Ann. Oper. Res. 316, 303-341.

Fazi, S., Roodbergen, K.J., 2018. Effects of demurage and detention regimes on dry-port-based inland container transport. Transport. Res. C Emerg. Technol. 89, 1-18.

Friedman, J.H., 1991. Multivariate adaptive regression splines. Ann. Stat. 19, 1-67.

Gasmi, K., 2022. Improving bert-based model for medical text classification with an optimization algorithm. In: International Conference on Computational Collective Intelligence. Springer, pp. 101-111.

Gourc, D., 2006. Vers Un Modèle Général Du Risque Pour Le Pilotage Et La Conduite Des Activités De Biens Et De Services: Propositions Pour Une Conduite Des Projets Et Une Gestion Des Risques Intégrées.

Gumuskaya, V., van Jaarsveld, W., Dijkman, R., Grefen, P., Veenstra, A., 2020. A framework for modelling and analysing coordination challenges in hinterland transport systems. Marit. Econ. Logist. 22, 124-145.

Guo, J., Xu, J., Du, Q., He, Z., 2021. Risk assessment on multimodal transport network based on quality function deployment. Int. J. Intell. Syst. 36, 1408-1440.

Hadjidemetriou, G.M., Teal, J., Kapetas, L., Parlikad, A.K., 2022. Flexible planning for intercity multimodal transport infrastructure. J. Infrastruct. Syst. 28, 05021010.

Haouari, A.T., Souici-Meslati, L., Atil, F., Meslati, D., 2020. Empirical comparison and evaluation of artificial immune systems in inter-release software fault prediction. Appl. Soft Comput. 96, 106686.

He, Z., Guo, J.N., Xu, J.X., 2019. Cascade failure model in multimodal transport network risk propagation. Math. Probl Eng. 2019, 3615903.

Hiebel, N., Fort, K., Néveol, A., Ferret, O., 2022. Clister: Un corpus pour la similarité semantique textuelle dans des cas cliniques en français (clister: a corpus for semantic textual similarity in french clinical narratives). In: Actes De La 29e Conférence Sur Le Traitement Automatique Des Langues Naturelles, 1. conférence principale, pp. 287-296.

Ho, W., Zheng, T., Yildiz, H., Talluri, S., 2015. Supply chain risk management: a literature review. Int. J. Prod. Res. 53, 5031-5069.

Jain, A., van der Heijden, R., Marchau, V., Bruckmann, D., 2020. Towards rail-road online exchange platforms in eu-freight transportation markets: an analysis of matching supply and demand in multimodal services. Sustainability 12, 10321.

Janjua, N.K., Nawaz, F., Prior, D.D., 2023. A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Enterp. Inf. Syst. 17, 1959652.

Jiang, X., Fan, H., Luo, M., Xu, Z., 2020. Strategic port competition in multimodal network development considering shippers' choice. Transp. Policy 90, 68-89.

Kabadurmus, O., Erdogan, M.S., 2020. Sustainable, multimodal and reliable supply chain design. Ann. Oper. Res. 292, 47-70.

Kaewfak, K., Ammarapala, V., Huynh, V.N., 2021. Multi-objective optimization of freight route choices in multimodal transportation. Int. J. Comput. Intell. Syst. 14, 794-807.

Kara, M.E., Firat, S.Ü.O., Ghadge, A., 2020. A data mining-based framework for supply chain risk management. Comput. Ind. Eng. 139, 105570.

Koutroumbas, K., Theodoridis, S., 2008. Pattern Recognition. Academic Press.

Kramarz, M., Knop, L., Przybylska, E., Dohn, K., 2021. Stakeholders of the multimodal freight transport ecosystem in Polish-Czech-Slovak cross-border area. Energies 14, 2242.

Kumar, A., Shankar, R., Aljohani, N.R., 2020. A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Ind. Mark. Manag. 90, 493-507.

Larsen, R.B., Atasoy, B., Negenborn, R.R., 2021. Model predictive control for simultaneous planning of container and vehicle routes. Eur. J. Control 57, 273-283.

Mazoyer, B., Cagé, J., Hervé, N., Hueldot, C., 2020. A French corpus for event detection on Twitter. In: Twelfth Language Resources and Evaluation Conference, European Language Resources Association (ELRA), pp. 6220-6227.

McMinn, A.J., Moshfeghi, Y., Jose, J.M., 2013. Building a large-scale corpus for evaluating event detection on Twitter. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 409-418.

Mele, I., Crestani, F., 2019. A multi-source collection of event-labeled news documents. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 205-208.

Mohseni, S.A., Van Hassel, E., Sys, C., Vanelslander, T., 2019. Economic evaluation of alternative technologies to mitigate sulphur emissions in maritime container transport from both the vessel owner and shipper perspective. Journal of Shipping and Trade 4, 15.

Moslemi, A., Hilmola, O.P., Vilko, J., 2016. Risks in emerging markets: logistics services in the mediterranean region. Maritime Business Review 1, 253-272.

Nawaz, F., Janjua, N.K., Hussain, O.K., 2019. Perceptus: predictive complex event processing and reasoning for iot-enabled supply chain. Knowl. Base Syst. 180, 133-146.

Nesterov, Y., Nemirovskii, A., 1994. Interior-Point Polynomial Algorithms in Convex Programming. SIAM.

Ouedraogo, C.A., Rosemont, C., Namakiaraghi, S., Montarnal, A., Gourc, D., 2020. Maritime risks taxonomy: a structured literature review of maritime risks classification. In: MOSIM'20-13ème Conférence Internationale De Modélisation, Optimisation Et Simulation, p. 17.

Ouedraogo, C.A., Montarnal, A., Gourc, D., 2022. Multimodal container t ransportation traceability and supply chain risk management: a review of methods and solutions. Int. J. Supply Oper. Manag. 9, 212-234.

Özmen, A., Weber, G.W., Batmaz, I., Kropat, E., 2011. Rcmars: robustification of emars with different scenarios under polyhedral uncertainty set. Commun. Nonlinear Sci. Numer. Simulat. 16, 4780-4787.

Pagolu, V.S., Reddy, K.N., Panda, G., Majhi, B., 2016. Sentiment Analysis of Twitter Data for Predicting Stock Market Movements.

Paraskevadakis, D., Bury, A., Ren, J., Wang, J., 2021. A services operations performance measurement framework for multimodal logistics gateways in emerging megaregions. Transport. Plann. Technol. 44, 63-92.

Patwardhan, S., Banerjee, S., Pedersen, T., 2003. Using measures of semantic relatedness for word sense disambiguation. In: International Conference on Intelligent Text Processing and Computational Linguistics. Springer, pp. 241-257.

Pettit, S.J., Beresford, A.K.C., 2009. Port development: from gateways to logistics hubs. Marit. Pol. Manag. 36, 253-267.

Priyanto, D., Zarlis, M., Mawengkang, H., Efendi, S., 2020. Approach of Analysis of Data Mining Prediction in Earthquake Case Using Non Parametric Adaptive Regression Method.

Qin, H., Zheng, H., Wang, B., Wu, Z., Liu, B., Yang, Y., 2024. Reducing Bias in Deep Learning Optimization: the RSGDM Approach.

Resnik, P., 1995. Using Information Content to Evaluate Semantic Similarity in a Taxonomy.

Smarzaro, R., Davis Jr., C.A., Quintanilha, J.A., 2021. Creation of a multimodal urban transportation network through spatial data integration from authoritative and crowdsourced data. ISPRS Int. J. Geolnf. 10, 470.

Sun, Y., Liang, X., Li, X., Zhang, C., 2019. A fuzzy programming method for modeling demand uncertainty in the capacitated road-rail multimodal routing problem with time windows. Symmetry 11, 91.

Tang, O., Musa, S.N., 2011. Identifying risk issues and research advancements in supply chain risk management. Int. J. Prod. Econ. 133, 25-34.

Tawfik, C., Limbourg, S., 2018. Pricing problems in intermodal freight transport: research overview and prospects. Sustainability 10, 3341.

Tsang, Y.P., Choy, K.L., Wu, C.H., Ho, G.T., Lam, C.H., Koo, P., 2018. An internet of things (iot)-based risk monitoring system for managing cold supply chain risks. Ind. Manag. Data Syst. 118, 1432-1462.

Usuga-Cadavid, J.P., Lamouri, S., Grabot, B., Fortin, A., 2022. Using deep learning to value free-form text data for predictive maintenance. Int. J. Prod. Res. 60, 4548-4575.
Vabalas, A., Gowen, E., Poliakoff, E., Casson, A.J., 2019. Machine learning algorithm validation with a limited sample size. PLoS One 14, e0224365.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017. Attention Is all You Need.

Vijayarani, S., Ilamathi, M.J., Nithya, M., et al., 2015. Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication Networks 5, 7-16.

Vilko, J.P., Hallikas, J.M., 2012. Risk assessment in multimodal supply chains. Int. J. Prod. Econ. 140, 586-595.

Vilko, J., Ritala, P., Hallikas, J., 2019. Risk management abilities in multimodal maritime supply chains: visibility and control perspectives. Accid. Anal. Prev. 123, 469-481.

Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H., 2019. Entity, Relation, and Event Extraction with Contextualized Span Representations.

Wattanakul, S., Henry, S., Bentaha, M.L., Reeveerakul, N., Ouzrout, Y., 2018. Improving Risk Management by Using Smart Containers for real-time Traceability arXiv preprint arXiv:1810.13332.

Wiedemann, G., Remus, S., Chawla, A., Biemann, C., 2019. Does Bert Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings.

Xu, B., Li, J., Liu, X., Yang, Y., 2021. System dynamics analysis for the governance measures against container port congestion. IEEE Access 9, 13612-13623.

Yu, H., Sun, X., Solvang, W.D., Laporte, G., Lee, C.K.M., 2020. A stochastic network design problem for hazardous waste management. J. Clean. Prod. 277, 123566.

Zeljko, P., 2010. Some Practical Experiences in Data Governance, pp. 939-946.

Zhang, W., Wang, X., Yang, K., 2019. Incentive contract design for the water-rail-road intermodal transportation with travel time uncertainty: a stackelberg game approach. Entropy 21, 161.

Zhou, H., Sun, G., Fu, S., Fan, X., Jiang, W., Hu, S., Li, L., 2020. A distributed approach of big data mining for financial fraud detection in a supply chain. Comput. Mater. Continua (CMC) 64.