Two years ago, a new virus named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) emerged. In the ensuing race to contain the virus, many non-pharmaceutical interventions (NPIs) have been introduced. Yet questions like “What is the risk of SARS-CoV-2 infection in a particular scenario?” and “Which NPIs limit virus transmission most effectively?” remain. Crowd and epidemiological simulation models can help formulate an answer to these questions. This paper studies virus spread and infection risk using a newly developed hybrid virus spread model PeDViS (Pedestrian Dynamics–Virus Spread model), which links an existing validated crowd movement dynamics model (NOMAD) with a new virus spread model (QVEmod). In particular, five common public transit scenarios are simulated: walking through a corridor, buying a ticket, moving through the ticket gates, waiting at a platform, and traveling by train. The relative impact of four variables (i.e., demand, waiting time, facial masks, and ventilation) was studied. This study illustrates that PeDViS can provide comprehensive insights into virus spread and the relative differences in infection risk. Furthermore, it corroborates the impacts featured in literature for all public transit scenarios. That is, ventilation and facial masks limit the probability of infecting other individuals. Moreover, waiting time and higher demand levels increase the probability of infecting other travelers. Second, especially large impacts of the NPIs facial masks and ventilation are found for the more “dangerous” scenarios; that is, long queues, delays, or waiting times coincide with high demands and crowd densities.

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.

Crowd management attempts to guide pedestrian crowds effectively and efficiently. Static crowd-management measures, such as fences, are often used to guide the crowd. Another way to steer pedestrian walking behavior is through “nudging,” that is, gently coaxing people into the “preferred” direction, for instance, by lighting conditions. This paper examines the impact of light intensity (brightness) and color on pedestrian route-choice behavior using data from a VR experiment. The study develops two types of discrete-choice models—a panel mixed logit model and a latent class choice model—featuring the route-choice behavior of pedestrians under varying lighting conditions in a virtual maze in a controlled virtual reality experiment. We found that pedestrians avoided red and dark corridors and chose green and blue corridors. On average, the green light most effectively “pulled” people toward a specific route. In addition, this study uncovered three segments in the population: (1) light-sensitive individuals, (2) darkness-avoiding individuals, and (3) individuals with a severe right-handed bias. We found that the impact of color and brightness levels on route-choice behavior differed greatly across segments.

At the end of 2019, SARS-CoV-2 rapidly spread across the globe within a few months. Since then, tackling the virus has been high on national agendas for over three years. As with other respiratory viruses, physical distancing (i.e., requiring sufficient space between individuals) became a key measure to prevent airborne virus transmission between individuals. However, this measure significantly reduces the capacity of pedestrian infrastructure, as more space is needed between people. This paper develops a capacity framework designed to calculate the capacity of pedestrian infrastructure, evaluate its state, and propose tailored interventions when physical distancing regulations are enforced. The framework is founded on the current state-of-the-art in pedestrian operational movement dynamics and determines capacity using three independent key performance indicators: flow rate, density, and interactions. Through two case studies from the COVID-19 pandemic, this paper demonstrates how the framework identifies when and why pedestrian infrastructures become unsafe and enables targeted interventions. The framework's relevance extends beyond the COVID-19 pandemic, offering insights into crowd management and resilient pedestrian infrastructure design for future airborne disease outbreaks.

Crowd management can provide an intermediate solution for the short-term to pressing crowd safety concerns at limited costs. It entails the systematic process of planning, organizing, and monitoring large gatherings of people to establish and maintain a safe and secure environment. This chapter provides a toolbox that planners and crowd managers can use to identify crowd incident risks, design crowd management strategies, and develop crowd management systems. In addition, some practical pointers concerning the operationalization of crowd management strategies are provided and several future challenges are highlighted.

Crowd management plays a vital role in urban planning and emergency response. Accurate crowd prediction is important for venue operators to respond effectively to adverse crowd dynamics during large gatherings. Although many studies have tried to predict crowd densities or movement dynamics with data-driven predictive models, their validation is often limited to data within the same scenario. As a result, the predictability of the data-driven model in unseen scenarios, such as evacuation scenarios, remains unknown due to the challenges of collecting out-of-distribution data regarding emergency conditions. To address this problem, we present an evaluation pipeline to evaluate different kinds of data-driven models. A method is proposed to generate realistic scenarios by simulation and collect synthetic data from these scenarios to acquire a comprehensive dataset. With these synthetic data, we evaluated different predictive models, from traditional machine learning methods to deep learning time-series prediction models, to explore their generalizability. Furthermore, we propose a weighted average metric, which is better suited to determine the performance of forecasting algorithms under adverse conditions. Through extensive experimentation, we showcase the heterogeneity and diversity of the simulation dataset. The evaluation results also revealed that all the data-driven models performed poorly in unseen scenarios, highlighting the urgent need to develop a robust and generalizable model for predicting crowd flow in indoor spaces.

Integrating bike-sharing programs with public transport enhances accessibility and car-independent mobility, yet a comprehensive societal cost-benefit analysis of this integration remains scarce. This study addresses this gap by conducting an ex-durante analysis of the OV-fiets program in the Netherlands, a station-based round-trip bike-sharing system designed to improve last-mile connectivity for train commuters. The analysis reveals that in the average (balanced) scenario, the net present value (NPV) of the OV-fiets scheme is positive, with a benefit-cost ratio (BCR) of 1.5. This indicates that the scheme has benefited the Dutch society over the 20-year period (2004–2023). In the pessimistic scenario, the NPV remains slightly positive with a BCR of 1.1. This implies that even under the least favourable conditions, where high costs and low benefits are assumed, the scheme still slightly exceeds the break-even point. Conversely, in the optimistic scenario, the BCR rises significantly to 2.4. Primary benefits include enhanced accessibility, reduced road congestion, and improved health outcomes. This research underscores the considerable societal value of the OV-fiets program in the Netherlands, warranting continued investment in the program and emphasising the need for ongoing bicycle safety measures and infrastructure improvements. However, OV-fiets might be successful in the Netherlands; our analysis also shows that copying it into other contexts is not straightforward. The seamless integration of bikes with trains is crucial, and the operators should be able and willing to accept operational losses.

This chapter aims to provide crowd operators with an overview, including the effects of crowd management strategies on the pedestrian choice behavior and movement dynamics. Crowd management strategies are commonly considered as the deployment of steering mechanism. The overview also includes how the profile of a crowd and environment influences pedestrian choice behavior and movement dynamics. Specifically, the study focuses on the impact of different steering mechanisms and profiling factors on pedestrian walking speed, flow rate, pedestrian route choice behavior, and pedestrian wayfinding performance. This overview is based on a review of the state-of-the-art literature, which is also presented within this chapter. It demonstrates the opportunity to employ particular steering mechanisms to manage crowds within a given environment. However, the overview also highlights some limitations in the state-of-the-art regarding the effects of the steering mechanisms, or even in the broader context of crowd management. Specific challenges for future crowd management research are discussed, which could provide crowd operators with more insights into the quantitative effect of crowd management strategies in a given environment.

To develop policies to increase the active mode share, understanding the factors influencing active-mode travel choice behavior, both positively and negatively, is essential. Travel choice behavior research attempts to unravel these behaviors and factors. This chapter provides an overview of pedestrian travel choice behavior research trends. In particular, this chapter discusses (A) which travel choices pedestrians make, (B) which (discrete) choice models can be used to study pedestrians’ travel choice behavior, and (C) which factors are known to influence pedestrians’ travel choice behavior according to the pedestrian literature.

During epidemics, decision-making regarding intervention measures faces complex trade-offs. Interventions targeting indoor venues can mitigate disease spread, since they are associated with higher infection risk for respiratory pathogens. However, as experienced during the COVID-19 pandemic, these measures can lead to economic losses, especially in the hospitality sector. In this study, we propose a hybrid modeling and simulation framework to provide decision support for reducing the infection risk in indoor venues while maintaining viable economic activity. Our framework integrates (i) a microscopic pedestrian model for human movement, (ii) a hybrid simulation model for virus spread and transmission, and (iii) a multi-criteria decision-making approach to identify the best service options. The framework is demonstrated for the SARS-CoV-2 infection risk. The restaurant case study results illustrate that maximizing the distance between seating groups can have a limited effect on the infection risk. Service duration and service capacity are key determinants of expected economic activity, but they constitute significant trade-offs: the former has a substantial impact on the infection risk, and the latter drives the probability of infectious introductions. Our analysis demonstrates the need for multi-criteria approaches during an outbreak and consideration of the epidemiological context for operational decision-making, even at an individual venue.

This chapter explains the processes of verification, calibration, and validation in pedestrian modelling. These are essential processes in the design and use of pedestrian models that together ensure accurate simulations of pedestrian behavior. Verification confirms that the model's implementation aligns with its conceptual design, calibration adjusts model parameters to improve accuracy, and validation assesses how well the model represents real-world pedestrian movements. Verification involves a structured process of testing whether the implemented model accurately reflects the conceptual model. This is done through a series of verification test cases, which compare the simulated outcomes to what is expected from the conceptual model. Calibration and validation are interrelated but serve different purposes. Calibration is an iterative process that fine-tunes model parameters to minimize errors between simulation results and reference data. Validation, on the other hand, assesses how accurate a pedestrian model replicates pedestrian behavior and dynamics. The state-of-the-art approach involves multi-objective calibration and validation, where multiple scenarios and metrics (i.e. objectives) are used to calibrate and validate the model. The choice of objectives has a major impact on the calibration and validation results. Key is that the scenarios and metrics are chosen such that they cover and capture all the relevant behaviors and dynamics. Which behaviors and dynamics are relevant depends on the intended use of the model and the type of modelled behavior. As most pedestrian models are stochastic or use stochastic parameters it is essential that during calibration and validation replications, repeating the simulation multiple time using the same inputs, are run to deal with this. Lastly, a sensitivity analysis of the model is also important to determine which parameters the model is most sensitive to. This guides the calibration process and can ensure that the calibration is as efficient as possible. All these processes are explained in detail in this chapter. This includes descriptions of how to apply them in the context of pedestrian behavior modelling and what are important factors to consider. This chapter therefore provides guidance for both model developers in creating valid models and model users is assessing the quality of their model for the intended application.

More and more people will be living in urban areas. This requires responsive and inclusive urban planning, to keep the urban areas resilient, inclusive and sustainable. This also affects mobility in cities. Governments promote walking as a healthy and sustainable mode of transportation. However, the pressure on pedestrian infrastructure is rapidly increasing, while large crowds gather more and more frequently. We therefore need to get more insights in what pedestrian planning entails. This chapter covers the conclusions of all contributions to this book, ranging from insights into pedestrian traffic flow through data and insights in behavior to different types of models and crowd management. The chapter ends with an overview of innovations in pedestrian planning and management, and what is needed to keep urban regions sustainable and attractive for pedestrians and crowds.

Access/egress travel to train stations poses a significant barrier to increasing the number of train travellers. The last mile is challenging for travellers, given the lack of private modes to reach the destination, strongly limiting the egress range from the station. An often-cited solution is shared micromobility (SMM): bicycles, e-bikes, e-scooters and e-mopeds. Through a stated preference survey, we analyse activity-end mode-choice preferences for SMM, walking and public transport (PT) among the Dutch population. Using a latent class choice model, we uncover three user groups: Multimodal SMM enthusiast (58%), who choose based on the trade-offs between various travel characteristics, while not having strong modal preferences. They are the most open, ready and able to use SMM. SMM hesitant cyclists (16%) have a strong preference for cycling and while they are open to using SMM, they may not feel themselves ready, stating that use of SMM can be difficult and dangerous. SMM-averse PT users (27%) are most likely to use PT and avoid SMM as they find it too difficult and dangerous to use. For policymakers, the high preference to walking over short egress distances reaffirms the need for continued focus on transit-oriented development. For longer distances, policymakers should focus on improving PT service in high-density high-demand areas, as high frequencies and dense PT networks can be justified, while stations in low-demand areas are better served by SMM. Policymakers should also prioritise SMM modes that are cheaper and that travellers are familiar and comfortable with, such as bicycles.

The movement patterns of pedestrian crowds at train stations, large-scale events, and cities are intricate. Due to the complex interplay between pedestrians, the physical environment, the physiological environment, and the available information, predicting how a pedestrian crowd will move is challenging. Microscopic pedestrian simulation models can help identify which movement patterns will arise, when and where, and to what extent the predicted movement patterns might potentially be problematic. Various pedestrian simulation models have been introduced to model pedestrians’ operational movement dynamics. This chapter explores only one of these simulation model categories: microscopic pedestrian simulation models. We introduce a wide variety of microscopic model types currently used to model pedestrian movement dynamics at a microscopic level, including the Cellular Automata model (CA), the Social Force model (SF), the predictive collision avoidance model (PCA), the Optimal Step Model (OSM), the Discrete Choice model (DC), and the data-driven artificial neural network model (ANN). The overall movement dynamics of each agent result from the interactions between agents, their physical and physiological environment, and available information. At the same time, each model type simulates the movement dynamics of agents in a particular way, resulting in slightly different operational and global movement dynamics. This chapter presents the most naive version of each model type, as this best describes the agents’ general choice behavior and movement dynamics. In addition, some interesting extensions, benefits, and challenges of each model type are discussed.

Traffic flow variables are essential for understanding, analysing, and optimising how pedestrians move in urban areas. In this chapter, we introduce the variables that can be used to describe a pedestrian flow. These variables can be microscopic (individual pedestrians), macroscopic (aggregate level), or mesoscopic (distributions of microscopic quantities). We start with the most detailed level of description, a trajectory. The trajectory describes the dynamics of an individual pedestrian as a function of time by its (two-dimensional) position in space. Based on the trajectory, we derive the most relevant microscopic variables, velocity and acceleration. Then, we give different definitions of density, one of the key macroscopic flow variables, by taking a snapshot of a pedestrian traffic situation at a time instant. Density is used to express crowdedness and level-of-service. Next, we look at the space-mean velocity of a pedestrian flow, followed by the third key macroscopic variable, the flow rate. The flow rate is the number of pedestrians passing a cross-section during a certain time period. We end this chapter with the generalised definitions of flow, density, and velocity for a time-space region, and show how they are related.

In public spaces such as city centers, train stations, airports, shopping malls, and multi-modal hubs, accurately predicting pedestrian flow is crucial for effective crowd management e.g. congestion prevention and evacuation planning. Traditional microscopic simulation models offer fine-grained insights by simulating each pedestrian individually, but they are computationally intensive and typically used at the planning and design stage, making them unsuitable for real-time interventions in high-demand scenarios. Macroscopic models, on the other hand, reduce computational cost by aggregating pedestrian behavior and solving partial differential equations, but they typically require estimates of traffic states such as density and speed — quantities that are difficult to measure accurately in practice. Additionally, as the complexity of these physics-based models increases, their computational feasibility for real-time use becomes even more limited. Data-driven (machine learning) models provide a computationally efficient alternative, enhancing real-time prediction capabilities. However, they often require large historical datasets to generalize well, and their performance can degrade under out-of-distribution (OOD) conditions. Moreover, most black-box learning models lack interpretability and domain-specific insights, limiting their practical adoption. In this paper, we propose a novel pedestrian flow prediction model based on the theory of crowd diffusion. Our method estimates flow rates directly from sensor-observed data and infers both Origin–Destination (OD) demand and route choice probabilities to support real-time operations. To address the OOD challenge, we incorporate an online learning mechanism that continuously calibrates model parameters based on incoming observations.

Shared micromobility (SMM), including bicycles, e-bikes, scooters, etc., is often cited as a solution to the first and especially the last mile problem of public transport (PT), yet when implemented, they often do not get adopted by a broader travelling public. As behavioural adaption is largely related to peoples’ attitudes and perceptions, we develop a behavioural framework based on the UTAUT2 framework to gain better understanding why individuals may (not) be willing to use SMM. Through an exploratory factor analysis (EFA) and a latent class cluster analysis (LCCA), we study the adoption potential of SMM and assess drivers and barriers as perceived by different user groups. Our findings uncover six user groups; Shared mobility positives, Car-oriented sharing neutrals, Older apprehensive sharers, Young eager adopters, (Shared) Mobility avoiders and Skilled sharing sceptics. The Young eager adopters and Shared mobility positives tend to be the most open to adopting SMM and able to do so. Older apprehensive sharers would like to, but find it difficult or dangerous to use, while Skilled sharing sceptics are capable and confident, but have limited intention of using it. Car-oriented sharing neutrals and (Shared) Mobility avoiders are most negative about SMM, finding it difficult to use and dangerous. Factors relating to technological savviness, ease-of-use, physical safety and societal perception seem to be the strongest adoption predictors. Younger, high-educated males are the group most likely and open to using SMM, while older individuals with lower incomes and a lower level of education tend to be the least likely.

In traffic flow theory, there is a relationship between the number of vehicles in a certain area and their average speed, the so-called macroscopic fundamental diagram (MFD). Related control concepts involve limiting the inflow into an area, so-called perimeter control. This may increase both the flow inside the controlled area and the outflow rate, reducing overall delays. This control concept is taught in classes on traffic flow theory around the globe. This paper presents an experiment that can be run in class to create the MFD with a group of students. The basic idea is that we have a confined area where participating students have to walk a predefined route. We repeat this experiment several times (runs of 2–4 minutes each), each time with a different, pre-defined number of students being simultaneously in the area (the accumulation). The number of students is controlled by metering the inflow of participants. Exit flow rates are determined for each run by dividing the number of exiting participants by the run time. One run will hence yield one observed combination of number of participants and run time. From this, an accumulation and exit flow rate can be computed. The Macroscopic Fundamental Diagram (MFD) is then created by plotting the exit flow rates and accumulation of all runs. Students can this way derive this MFD from data. Moreover, they also experience during the walking how delays change with accumulation, and thereby the experiment intuitively teaches them the concepts of the MFD and perimeter control. The paper describes the experiment, and provides tools (software, routes) for repeating the experiment with other groups.

Efficient crowd management is essential for optimizing the performance of pedestrian infrastructures, either in terms of crowd flow or pedestrian levels of safety and comfort. This study investigates the impact of one type of crowd management measure, namely lighting, on pedestrian behavior. Using Virtual Reality experiments, the impact of lighting, both the brightness level and the light color, on pedestrian route choice is studied. A virtual maze was designed, featuring 10 T-intersections, where the light conditions are varied at each T-intersection to study its impact on pedestrian route choice. Our study shows that pedestrian route choice is strongly influenced by the light color in a virtual environment. Pedestrians prefer to follow paths with green-colored lights and avoid paths with red-colored lights, irrespective of the light color on the other path. Moreover, pedestrians slightly prefer to use the path with a higher brightness level. Lastly, the results indicate that pedestrians do have a slight right-handed tendency on average, however, this effect cancels out almost completely when other guidance information is present in the scenario. Altogether, the findings suggest that lighting can impact pedestrian route choice behavior.

Mobility patterns and transport systems have been heavily impacted due to the COVID-19 pandemic. Public transport is impacted heavily, as governments worldwide advised against using it. This paper presents the data collection effort initiated by NS (Dutch Railways) and Delft University of Technology to capture changes in travel behavior, attitudes and intentions related to the COVID-19 pandemic among Dutch train travelers. The survey set-up, data collection process, data validation and potential of the dataset are discussed. The data collection effort proves to be a valuable longitudinal data set that is ground for many research opportunities and policy insights.