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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

In the pursuit of promoting cycling and providing better cycling infrastructure, its design is of great importance. One of the critical locations in a network are intersections, and specifically T-junctions, due to the inverted perception of priority. In this paper, we investigate the behaviour at T-junctions dedicated to cyclists and the effect on bicycle flow efficiency resulting from the introduction of lane markings that advise through cyclists to shift to the left so that merging cyclists can occupy the space on the right hand side of the cycle path. A comprehensive framework is proposed for the assessment of the T-junction efficiency. Empirical trajectory data from a large-scale cycling experiment are used for the analyses. The findings suggest that cyclist heterogeneity can be even more influential on the efficiency than the infrastructure design. Moreover, a form of self-organisation is observed, as through cyclists are willing to move to the left and allow merging cyclists to fit in the cycle path without the provision of any instructions. This means that the lane marking is both redundant and not improving the efficiency of the T-junction.

High-deck coaches form an essential component of mass transportation systems in China. Safe evacuation from high-deck coaches is facing dire challenges. However, evacuation behaviour from high-deck coaches has not been deeply understood yet. In this study, a novel conceptual framework is firstly proposed to capture the evacuation behaviour of coach passengers, and next based on which 22 full-scale experiments have been conducted to examine the effect of three selected factors: available exits, lighting conditions and age groups on the evacuation behaviour of Chinese passengers in a high-deck coach, in a systematic and quantitative way. Four performance indicators of evacuation behaviour, i.e., evacuation time, pre-evacuation time, flow rate and crowdedness, were collected and analysed. The results indicate that limited available exits and the dim lighting condition (less than 1 lux) significantly reduce the evacuation efficiency and increase the crowdedness within the aisle area regardless of the age groups. Compared to young students, the evacuation of middle-aged people is observed to have a significantly longer pre-evacuation time, lower evacuation efficiency, and higher level of crowdedness. In addition, young students’ pre-evacuation times are found to conform to the Weibull distribution, whereas middle-aged people’ pre-evacuation times could be modelled with the Loglogistic distribution. Empirical results of this study could be helpful for the improvement of the safety design of high-deck coaches, and provide valuable benchmarks for the development of coach evacuation behaviour and simulation models.

In dit artikel worden de effecten van COVID-19 op de (relevante onderdelen van de) aanbodkant van het mobiliteitssysteem beschreven. Gezien de verwachte impact ligt de focus op voetgangersstromen, fietsstromen, het gebruik van deeldiensten en openbaar vervoer voertuigen. We kijken naar het effect op de capaciteit, via gemeten of theoretisch verwachte effecten op het gedrag. We maken hierbij onderscheid tussen eenvoudige infrastructuur elementen (een voetpad, een fietspad) en netwerken of knooppunten, waarbij meer complexe interacties optreden. Dit is relevant om te kunnen inschatten welke vraag door het mobiliteitssysteem kan worden verwerkt, voordat overbelasting (congestie, vertragingen, verdrukking) ontstaat. Het artikel start met een theoretisch raamwerk waarin we de verschillende aspecten die invloed hebben op de aanbodkant van het mobiliteitssysteem in kaart brengen. Vervolgens gaan we in op de verschillende (exit) scenario’s die internationaal zijn uitgerold en waarop deze impact hebben. De effecten zijn zo goed mogelijk gekwantificeerd op grond van a) bevindingen uit de literatuur, b) theoretische analyses, en - waar mogelijk - c) analyse van beschikbare data.De belangrijkste bevinding van dit onderzoek is dat de doorstroomcapaciteit van enkelvoudige infrastructuur elementen met 60 tot 70 procent kan afnemen, mits mensen zich strikt aan de 1,5 meter afstand houden. Indien we naar netwerken of (multimodale) knooppunten kijken, dan zien we dat deze afname groter wordt, afhankelijk van hoe effectief de ruimte in het knooppunt wordt - of kan worden - benut. We laten zien hoe lokale kenmerken zich doorvertalen naar het gehele knooppunt en we laten zien hoe de spreiding in ruimte leidt tot extra reductie van de doorstroomcapaciteit. De effecten met betrekking tot de opslagcapaciteit – hoeveel verkeersdeelnemers kunnen zich maximaal in een netwerk of knooppunt bevinden - zijn nog groter dan voor de doorstroomcapaciteit, wederom gegeven de opvolging van de 1,5 meter maatregel. Deze bevindingen laten zien dat de 1,5 meter maatregelen bij vergelijkbare vraag zou leiden tot enorme afwikkelingsproblemen. Uit eerste data analyses blijkt echter dat de 1,5 meter afstand vaak met voeten wordt getreden en dat veel interacties plaatsvinden op minder dan 1,5 meter afstand. Wel is onze bevinding dat in de verschillende fasen van de COVID-19 crisis de opvolging van de 1,5 meter regel verandert.

Bike-sharing systems have rapidly expanded around the world. Previous studies found that docked and dockless bike-sharing systems are different in terms of user demand and travel characteristics. However, their usage regularity and its determinants have not been fully understood. This research aims to fill this gap by exploring smart card data of a docked bike-sharing scheme and GPS trajectory data of a dockless bike-sharing scheme in Nanjing, China, over the same period. Both docked and dockless bike-sharing users can be classified into regular users and occasional users according to their usage frequency. Two systems are cross-compared regarding their travel characteristics. Then, binary logistic models are applied to reveal the impacts of travel characteristics and built environment factors on the regularity of bike-sharing usage. Results show that for both bike-sharing systems, regular users and occasional users share similar riding time and distance, while significant differences in the spatio-temporal distribution between docked and dockless bike-sharing systems are observed. The regression model results show that the “Trips during morning and afternoon peak hours” are positively associated with the regularity of both docked and dockless bike-sharing usage. However, the “Riding distance” variable is negatively associated with the usage regularity of both systems. Built environment factors including working point of interest (POI), residential POI, and transit POI promote the usage regularity of both bike-sharing systems. Finally, policy implications are proposed, such as increasing the density of docking stations in suburban areas and developing high-quality parking area for dockless bike-sharing around public transport stations. This study can help operators or governments to launch or improve the service of bike-sharing systems.

Macroscopic pedestrian models are theoretically simpler than microscopic models, and they can potentially be solved faster while producing reasonable predictions of crowd dynamics. Therefore, they can be very useful for applications such as large-scale simulation, real-time state estimation and crowd management. However, the numerical methods presently used to solve macroscopic pedestrian models, which are mostly grid-based, have some shortcomings that limit their applicability. More specifically, they usually include complex procedures for grid generation and remeshing, and they produce simulation results that may not be sufficiently accurate (for example, because of unclear boundaries between flow states). Smoothed Particle Hydrodynamics (SPH) constitutes an alternative numerical method that could potentially overcome these limitations. SPH is a meshfree method where a crowd is represented by a set of particles that possess material properties and move according to macroscopic laws. Relevant state variables at each particle are approximated using information about the material properties of the neighboring particles and a smoothing function. This paper puts forward for the first time a generic SPH framework for solving macroscopic pedestrian models; in addition, it demonstrates that an SPH-based simulation model can produce meaningful and accurate results by means of three case studies. The first case study shows that the proposed numerical method can approximate well the analytical solution of a simple macroscopic model applied to a queue-discharge scenario. The second case study demonstrates that the proposed numerical method can potentially reproduce density dispersion (a phenomenon observed in real crowds) more accurately than grid-based methods, due to its meshfree, Lagrangian, and particle-based nature. The third case study highlights the need to reformulate the acceleration equation of the basic macroscopic model in order to reproduce lane formation in bi-directional flows (also an observed phenomenon) using the proposed SPH framework, and this paper presents a solution to do so.

Bike-sharing systems have witnessed rapid growth in the last decades. Bike-sharing has been found to influence modal shift from car, public transit, and active transportation modes. However, the impacts on modal shift by considering different kinds of bike-sharing systems are rarely discussed. This study examines the modal shift dynamics and the influential factors on modal shift in response to various bike-sharing systems. Data are obtained by an online survey targeting both non-bike-sharing users and bike-sharing users in a Dutch context. Binary logit models are developed to investigate the relationship between modal shift to bike-sharing with socio-demographic, commuting trip and motivation factors. The survey results show that dockless bike-sharing (Mobike) users are more likely to be non-Dutch and often have no driving license, whereas the situation is opposite for docked bike-sharing (OV-fiets), bicycle-lease (Swapfiets) and non-bike-sharing users. Except for train use, bike-sharing users reduced walking, the use of private bicycle, bus/tram and car. Swapfiets showed a most significant influence on modal shift for both single and multimodal trips. The regression model results indicate that “No stolen/damage problem” and “Cheaper than other modes” are significant factors promoting dockless bike-sharing and bicycle-lease. “Good quality of bicycles” is a significant factor considered by docked bike-sharing and bicycle-lease users. “Public transport subsidy by employer” encourages commuters to shift to docked bike-sharing, whereas individuals with a government student discount are less likely to shift to Swapfiets. Male and multimodal commuters are more likely to use dockless bike-sharing. Commuters are less likely to shift to docked bike-sharing if the trips are “Short” or suitable for “Private bicycle”. The findings provide a clear understanding of the modal shift and its determinants that can help municipal planning and policy decision-making in terms of bike-sharing systems.

This paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both the mathematical formulation and the graphical representation are provided. A logit choice model is applied to describe drivers' lane choice probability. The lane changing rate is estimated by employing the Incremental Transfer (IT) principle and the lane choice probability model. The numerical implementation of the model in the case of two lanes is discussed. The simulation results reveal that the proposed Lagrangian model is able to describe lane changing dynamics of vehicle platoons; while the lane changing equilibrium curve at the macroscopic level is consistent with that from the multilane Eulerian model as well as the observed data on the highway.