Bicycle delay is an important variable to assess the performance of the cycling transportation system, especially as an indicator of intersection efficiency. This article estimates a machine learning (ML)-based model for estimating average bicycle delays at signalized intersections. This study evaluates various ML models with regressor features, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks. Sparse GPS cycling data (as reference data) from the Talking Bikes program in the Netherlands and the local control signal and flow detection information from the VLOG data provided by a Dutch city are adopted to train the ML models. The findings illustrate the viability of estimating bicycle delays by considering the interplay among weather conditions, temporal factors, junction topology, and local traffic conditions. The estimation model fit using the best-performing model - random forest - has doubled compared to the case without such additional traffic information, indicating its improved performance. Insights gained from the estimation model emphasize the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

With growing disruptions, uncertainties, and complex risks like pandemics, natural disasters, and geopolitical tensions, firms must ensure supply chain continuity, quick recovery, and agility in responding to market needs. As a result, designing resilient supply chain networks (SCNs) has become both essential and highly important. To address this problem, based on the complex adaptive system (CAS) theory and by modifying the Barabási and Albert (BA) model, a supply chain network evolving (SCNE) model with adaptive strategies is designed, which considers firms’ edges growth and rewiring strategies. Utilizing mean-field theory, the SCNE model is analyzed and subjected to simulation studies to verify its scale-free properties. It also examines the structural characteristics of SCN evolution under different adaptive strategies. Subsequently, a case study of Acura automobile SCN is conducted for topological robustness analysis. Finally, the results of the simulation are validated using an ordinary least squares (OLS) regression model, demonstrating the effectiveness of adaptive strategies in enhancing the topological robustness of SCNs. We find that the enhancement of SCN topological robustness can be achieved through firms’ edges growth and rewiring strategies in response to node removal disruptions. Quantitatively, firms’ edges growth strategies improve SCN topological robustness approximately 2.1 times more than rewiring strategies, as indicated by the coefficients of 0.81 and 0.75 for largest connected component size and network efficiency, respectively, compared to 0.38 and 0.37 for rewiring strategies. These findings underscore the critical role of adaptive strategies in enhancing the resilience of SCNs.

This paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both continuous formulation and discretization of the model are derived explicitly considering lane-changing characteristics. For model discretization, a lane-changing number estimation model is developed to calculate the net lane-changing number for each vehicle group considering the relative position between vehicle groups in the current and adjacent lanes. With model discretization, the spacing of vehicle groups for each lane can be dynamically calculated. In addition, the boundary conditions for both the continuous Lagrangian model and its discretization are also derived. A numerical implementation of the model in the case of a three-lane highway section with a lane-drop is discussed, and results indicate that the proposed Lagrangian model can well simulate traffic dynamics, including the generation and propagation of congestion, and the perturbation caused by lane-changing behaviors. The lane-changing characteristics in terms of a cumulative net number of lane-changing vehicles for each lane, and the spacing dynamics of vehicle groups can be estimated as well. We further validate the proposed model using real-world data observed from a two-lane freeway section in Japan. The results show that the proposed multilane Lagrangian model can well capture traffic dynamic properties and could provide a relatively accurate estimation in terms of lane volume dynamics, vehicle spacing dynamics, and the cumulative net number of lane-changing vehicles. Comparisons with the Eulerian multilane model indicate that the Lagrangian model offers superior performance in predicting vehicle counts and spacing, especially under congested conditions. This improved performance can be attributed to the Lagrangian model's capability to track individual vehicle groups, resulting in a more precise representation of traffic dynamics.

High-speed trains play an increasingly crucial role in mass transportation systems in China. Due to the confined space and high-density crowds in the train seat area, evacuation from trains is a critically risky event during emergencies but has not yet been fully understood. To fill this gap, well-controlled passenger evacuation experiments in the confined seat area were conducted, based on which microscopic trajectories, evacuation times, flow rates, and typical evacuation behaviours were collected and systematically analyzed. The results suggest that the narrow aisle is a significant bottleneck that impedes passenger evacuation. The evacuation time of passengers increases with the number of seat rows. Furthermore, the instantaneous flow rate can be well captured by the Loglogistic distribution. Some typical phenomena, including overtaking and pushing behaviours, are observed and analyzed. The empirical findings of this study are conducive to deepening the understanding of train passengers’ evacuation behaviour in the seat area.

Evacuation from transportation tools is receiving increasing attention due to its high risk and complexity. However, as a crucial travel mode, high-deck coaches, have been overlooked, lacking a dedicated evacuation model, let alone exploratory simulation analyses. This work proposes an innovative high-deck coach evacuation model framework, where three intertwined modules are developed to separately delineate the strategic, tactical and operational passenger evacuation behaviours. In the strategic behaviour module, the Cox-Weibull hazard duration model is introduced to capture the pre-evacuation times of passengers so that both the distribution characteristics and the dependence on the proximity to the target exit are encapsulated. In the tactical and operational behaviour modules, elaborate behavioural rules are designed and coupled with Cumulative Prospect Theory to comprehensively incorporate the typical behavioural characteristics and decision-making factors of passengers. The framework is validated with empirical data from various scenarios and proven to significantly outperform the state-of-the-art passenger evacuation model. It is found that the CWM substantially improves the prediction accuracy of the framework compared with the Weibull probabilistic distribution. Overtaking behaviour significantly affects passenger evacuations, but does not induce any benefit for the overall system. This study offers valuable tools and insights for high-deck coach evacuation simulation and management.

The operation of intelligent connected vehicles (ICVs) is fundamentally data-driven, continuously generating massive amounts of data. Given the significant value of ICV data to enterprises, industries, and nations, promoting data openness and sharing has become essential. However, such data often contain sensitive information, and its misuse can threaten individual privacy, corporate security, and even national interests. To address this dilemma, this paper develops the misuse risk score (MR-score), a novel quantification model and associated evaluation method for assessing the risk of ICV data misuse. The MR-score is constructed based on three core properties of ICV data: sensitivity; scale; and identifiability. The sensitivity score, information quantity, and identifiability factor are designated as the corresponding evaluation indicators, and systematic approaches for their quantification are proposed. The analytic hierarchy process is employed to measure the sensitivity score. Information entropy is adopted to evaluate the information quantity. A combination of k-anonymity-based and damage source determination-based methods is utilized to estimate the identifiability factor, considering data incompleteness, imprecision, and invalidity. Two empirical ICV data sets are utilized, and comparative analyses are conducted to demonstrate the effectiveness of the MR-score in capturing misuse risks. Higher MR-scores correspond to greater risk. The model captures the joint influence of all three data properties and reveals the marginal diminishing effect of data scale on misuse risk. This work offers valuable tools for data owners and regulatory agencies to prioritize critical data sets, implement targeted data protection measures, and enable secure data circulation while maximizing the value of ICV data.

This paper investigates the development of self-aware mechanisms for automated vehicles, introducing the notion of an automation state estimation system. This system is capable to understand its capabilities in a given context, and can leverage that knowledge to estimate the current and near-future automation performance based on internal metrics, as well as external, static (e.g. lane geometry) and dynamic environmental elements (e.g. traffic and weather information). From an application perspective, we consider automation state estimation in the scope of automation mediation, as part of a broader and holistic mediation system, with the goal to tackle challenging aspects related to transitions of control, mode confusion, and driver engagement. We used real-world data for system design, and implemented the proposed automation estimation system in a prototype vehicle. Based on 70 hours of real-world driving, we also validated the performance of the automation state estimation for automation mediation purposes.

This article presents an integrated educational module for undergraduate traffic engineering at the microscale level. When dealing with a complex transportation system, the education program should cultivate students’ acquisition of both in-depth traffic specialization and breadth of engineering knowledge. For the educational instruction of the signal phase design, this article presents an integrated design that includes several engineering subjects: signal phase design, circuits and electronics, and object-oriented programming (OOP). Due to the lengthy evolution of signal phase design and the high-end industrial standards for this issue, this well-structured problem requires an instructional design to adopt a worked example that integrates varied engineering domains. The integrated design session is intended to deliver education through designing a miniature signal controller while creating an immersive situation and encouraging social teamwork. Feedback from participating students has been positive, indicating the achievement of the planned learning objectives and better mastery of engineering practices.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics—Virus Spread model (PeDViS): an individual-based model that combines pedestrian behaviour models with virus spread models that incorporate direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark ‘at risk’ interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to build-up of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent.

The emergence of electric vehicles (EV) presents new opportunities for transportation decarburization and sustainable transportation in cities worldwide. However, previous research has primarily focused on EV charging infrastructures within urban areas, with limited attention to those launched in expressway service areas. Addressing this gap is crucial in alleviating EV users’ range anxiety on expressway journeys. This study investigates the BEV users’ reuse behavior toward charging infrastructures on expressways. Focusing on the BEV users who had used the charging infrastructures on the expressway, the structural equation modeling and the multi-group analysis are employed to reveal the effect of psychological factors on BEV users’ reuse intention and explore the heterogeneity across different socio-demographic groups. Results reveal that Attitude and Subjective Norm drive the reuse intention. Perceived Risk has an indirect negative effect on reuse intention. Attitude has a more significant effect on reuse intention among elder users, high-frequency users, and low remaining State of Charge (SOC) users. This paper offers new insights for charging infrastructures’ planning and operation in expressway service areas.

The Covid-19 pandemic has had a large impact on the world. The virus spreads especially easily among people in indoor spaces such as restaurants. Hence, tools that can assess how different restaurant settings can impact the potential spread of an airborne virus and that can assess the effectiveness of mitigation policies are of high value. Microscopic pedestrian models provide the tools necessary to assess the detailed movements of people in a restaurant and with that the risk of virus transmission. This paper presents the application of a microscopic pedestrian model, including a novel activity choice and scheduling model, to assess virus transmission risks in restaurants. Simulation experiments identify that different factors impact virus transmission risks in a restaurant. Contacts between restaurant staff and customers are the driving factor for virus transmission in a restaurant whereby especially staff presents a big risk. Hence, mitigation policies focussing on these interactions and on preventing staff from transmitting the virus can be highly effective. The results also show that different restaurant layouts and setups lead to distinctly different transmission risks. Therefore, insights obtained from simulating one restaurant cannot be just transferred to any other restaurant. Together, these results show the added value of including pedestrian models in disease transmission risk modelling exercises to mitigate the impact of a pandemic caused by an airborne virus. However, the research also shows that, to better utilize the potential of pedestrian models for disease transmission risk modelling, future research of pedestrian activity scheduling behaviour in indoor spaces is necessary.

In dit artikel worden de effecten van Covid-19 op de verandering van de verkeersvraag in Nederland onderzocht. In het bijzonder hebben we gekeken naar de veranderingen in de vraag naar autoverkeer, fietsverkeer en openbaar vervoer (OV) in 7 regio's in Nederland gedurende de verschillende Covid-19 periodes. De onderliggende data zijn afkomstig uit detectielussen (voor auto- en fietsverkeer) en check in/check out gegevens (voor het OV).

Zoals verwacht blijkt dat tijdens de lockdowns de vraag het sterkst afneemt (30% - 40% voor auto- en fietsverkeer, meer dan 80% voor openbaar vervoer tijdens de eerste lockdown), terwijl de vraag zich iets herstelt tijdens de periodes met versoepelingen. Vanaf het moment dat de samenleving weer open gaat (in maart 2022) keert de vraag naar autoverkeer terug naar het niveau van vóór de pandemie. Op dat moment is er wel nog steeds sprake van een sterk gereduceerde vraag naar openbaar vervoer (hoewel dat verschilt tussen regio’s). Het herstel van de vraag naar fietsverkeer varieert tussen regio's, waarbij de vraag in sommige regio’s is gereduceerd en in andere regio’s is toegenomen vergeleken met de periode voor de pandemie. Dat het OV moeite zal hebben om terug te komen op het niveau van voor de pandemie blijkt uit het feit dat het aantal OV abonnementen sterk is gedaald. Voor zowel de auto als de trein wordt een korter verblijf op de bestemming waargenomen, hetgeen kan worden veroorzaakt door het feit dat mensen gewend zijn thuis te werken, en op die manier de spitsperiodes kunnen vermijden.

Due to the fact that there is a lack of comprehensive understanding of how the dynamic nature of supply chain networks (SCNs) interrelates with network structures, particularly network topologies under disruptions. This research employs a novel evolving model of a supply chain network (SCNE model) by modifying the Barabási and Albert (BA) model to capture the phenomenon of regional economy and the factor of firms’ attractiveness, considering the degree, the locality preference, and the heterogeneity of SCN members simultaneously. We then analyze the SCNE model via the mean-field theory and conduct simulation study to identify the scale-free characteristic of the proposed supply chain network model. Additionally, we leverage node and edge removal to emulate random and targeted disruptions. We measure and compare the robustness of four network models, i.e., the SCNE model, the Erdos and Rényi (ER) model, the BA model, and the Watts and Strogatz (WS) model using two essential metrics, i.e., the size of the largest connected component and the network efficiency. We find that the robustness of the SCNE model is better than the BA model and the WS model on the whole in the presence of disruptions. Also, from the node level, the SCNE model maintains resilience, behaving similarly to the ER model against random disruptions while it shows vulnerability under targeted disruptions, responding in line with the BA model and the WS model. From the edge level, the network efficiency of the SCNE model changes slowly, and the topological structure of the SCNE model slightly changes initially but decreases rapidly at some value, as well as the BA model, the WS model, and the ER model. Based on the results, we summarize key points of the implications for research and practice in supply chain management.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

Pedestrian tactical choices and operational movement in evacuations essentially pertain to decision-making under risk and uncertainty. However, in microscopic evacuation models, this attribute has been greatly overlooked, even lacking a methodology to delineate the related decision characteristics (bounded rationality and risk attitudes), let alone their effects on evacuation processes. This work presents an innovative two-layer floor field cellular automaton model framework, where three intertwined sub-modules respectively dedicated to modelling the exit choice, the locomotion movement and the exit-choice changing behaviours are proposed and integrated as an entity. By introducing various decision-making elements computed by the proposed algorithm, Cumulative Prospect Theory (CPT) is proposed for the first time to model the exit choice and locomotion decision-making under risk and uncertainty. In the exit-choice changing module, attractive and repulsive forces are invented to jointly describe the tendency to revisit the routing decision. Each sub-module and the whole framework are validated in manifold indoor environments. The simulation results of the modules with CPT accord with the empirics from the evacuation experiments and are superior over those from the state-of-the-art models. The degree of rationality and risk attitudes are proven to have significant impacts on tactical and operational decisions. Furthermore, irrational behaviour in decision-making is not variably detrimental to locomotion efficiency of pedestrians. The proposed framework can serve as an elegant tool to predict pedestrian dynamics. The behavioural findings shed new light on understanding and modelling the tactical and operational decisions in evacuations.

In recent years, the interest in riding in cities using the two-wheeler (e.g., bicycles, electric bicycles, electric mopeds, etc.) increases. Mixed-traffic road segments are one of the most common traffic scenes where the mixed two-wheeler flows exist. Because the movements are often not restricted by lanes, the two-wheeler uses lateral road space more freely and shows obvious multilateral interactions (i.e. multi-interaction) with others, bringing issues that endanger traffic safety. A precise estimation of its impacts on traffic operation and safety is necessary, while the microscopic simulation model can satisfy the need as a helpful tool. However, most existing simulation models of these three types of two-wheelers are essentially focusing on handling the one-on-one interaction. The capability to deal with the two-wheeler multi-interaction in mixed traffic is still rare, and the description of what endogenous tasks are contained by the multi-interaction has also not given by literature. To this end, this paper first defines what the multi-interaction entails on the operational behaviour level, claiming that it contains three intertwined processes, namely a (mental) perception, a (mental) decision, and a physical process. The (mental) perception and decision processes represent the recognition of interactions and the response to traffic conditions, while the physical process refers to the execution of these mental activities. A three-layer simulation framework has then been developed, where each layer sequentially corresponds to one of the operational behaviour tasks. Integrated component models are also proposed in each layer to cover these operational tasks. A Comfort Zone model is hence put forward to dynamically perceive the multiple interactive road users, while a Bayesian network model is developed to deal with the decision-making process under multi-interaction situations. Meanwhile, a behaviour force model is also proposed to capture the non-lane based movements following the selected behaviour and current interaction states. Finally, we face validate the proposed models by the comparison between simulation results and observations obtained from trajectory dataset. Results indicate the model performance matches the observed interaction and motion well.

Similarly to Maslow’s pyramid of human needs, we theorize that cities have a pyramid of bicycle network needs that depends on their level of bicycle culture. As an increasing number of data sources emerge for bicycle data collection, transport authorities face the challenge of understanding how to use the data and which data sources are fit for their network needs. This article defines a framework that relates the bicycle network needs of cities with data collection systems. We showcase the need-driven framework through a case study of Melbourne, Australia, a bicycle ignorant city, and surveying 15 municipalities (and their consultancies) of the Netherlands. By using the proposed need-driven framework cities can understand how to fully exploit bicycle data collection systems and make a systematic plan.

In this chapter, we focus on the modeling of the behavior of cyclists. This behavior encompasses different types of interconnected decisions: from the split-second decisions that cyclists make when they are riding their bike and are interacting with the road and other traffic participants to choices pertaining to the activities they want to perform and the locations where they can perform these activities. These different decisions are often related to different temporal (and spatial) scales. The detail in which these decisions need to be accurately modeled is often dependent on what the model is applied for, as will be explained in the ensuing of this chapter. Therefore, different (types of) models have been developed, as introduced in the last part of this chapter.

Numerous evacuation performance data for the utilization in evacuation modelling and simulations have been established for the conventional/widely studied scenarios, such as building evacuation scenarios. However, such data are typically scarce for a new scenario in literature — evacuation from high-deck coaches. This paper fulfills this gap by presenting empirical high-deck coach evacuation data-sets that can be used for model configuration and validation. To this end, firstly, five essential and commonly used performance metrics, i.e., evacuation time, flow rate, alighting time gap, velocity on stairways and exit choice, were collected and derived from two series of controlled experiments with 7 and 22 runs that involved 44 and 96 participants respectively. Then, all these datasets were structured in the distribution form, based on which three critical behavioural insights are revealed regardless of the evacuation conditions (the types of high-deck coaches, lighting conditions, and age groups). First, the evacuation behaviour in normal (experimental) conditions conforms to a multi-stage pattern (a modified four-stage pattern, i.e., reaction, acceleration, fluctuation and saturation stages). Second, the instantaneous flow rate can be well captured by the Burr, Loglogistic and Lognormal distributions, and the alighting time gap can be represented by the Burr distribution. Third, more than 50% of passengers evacuate through the rear door in the front-and-rear-door evacuations. The frequency of choosing the front door is found to shift towards the direction of the rear door compared to the ideal results (based on the shortest distance calculation) with a magnitude of approximately 1.95 seat rows. The presented data-sets are valuable resources for the development of high-deck coach evacuation models. The empirical findings promote the understanding upon the evacuation behaviour of high-deck coach passengers.

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics.