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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Crowding is often analyzed using crowd dynamics variables. Yet, it is questionable whether quantitative variables fully describe the perception of crowdedness. This paper presents four case studies into the Pedestrian Level-of-Service (PLoS), featuring a 1) mass event, 2) shopping environment, 3) festival, and 4) touristic hotspot. The relation between the PLoS and the crowds' movement dynamics is studied using a combination of survey and monitoring data. This study establishes that the perception of LoS is partly related to the crowds' dynamics, and that the combination of in-situ surveys and monitoring data provides more comprehensive insights w.r.t. pedestrians' perceptions of space.

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.

Physical distancing has been an important asset in limiting the SARS-CoV-2 virus spread during the COVID-19 pandemic. This study aims to assess compliance with physical distancing and to evaluate the combination of observed and self-reported data used. This research shows that it is difficult to operationalize new rules, that context affects compliance, that there needs to be a need for compliance, and that rules require upkeep. From a methodological point of view, this study found that the combined methods provide a comprehensive picture of compliance behaviour, that it is challenging but essential to mitigate response fatigue in long-term monitoring studies, and that it would be interesting in future research to learn how actual behaviour is influenced by personal narratives.

Although numerous studies used Virtual Reality (VR) to study pedestrian behavior, there is an ongoing debate about the validity of using VR for studying pedestrian behavior. This study aims to contribute toward the validation of immersive VR systems for pedestrian wayfinding behavior studies by conducting a direct comparison of a field experiment and a matched virtual experiment. Both experiments feature three identical wayfinding assignments across multiple floors in a building. To evaluate the ecological validity of VR, three metrics of three different levels of wayfinding behavior are adopted, namely travel time (level: wayfinding performance), wayfinding strategy (level: decision-making), and angular speed of the head (level: observational behavior). Our findings show that VR can be used to study pedestrian wayfinding strategy in buildings with a single floor. However, there are significant differences in pedestrian wayfinding strategy between the field experiment and the VR experiment. Additionally, we found significant differences in the angular speed of the head between the two experiments. It suggests that researchers should take caution when using VR as a research tool to study the wayfinding strategy and the observational behavior of pedestrians in multi-story buildings.

Making the mobility sector sustainable is a key part in the transition to a climate- and carbon-neutral society (European Parliament, 2023). For trips too long to be performed by active modes (walking, cycling), public transport is the most sustainable alternative available to travellers. With a high capacity and efficient use of resources, it is ideal for transporting larger numbers of people over longer distances. (Brons et al., 2009).
In the Netherlands, 39% of all train passengers arrive to the station by bicycle, with over half a million bicycle parking spaces offered around the country (Dutch Railways, 2023). And although some travellers have their own bicycle on both the home and activity side of the train trip, most only have a bicycle on their home-end, meaning they have to rely on other forms of transportation on the activity-end, such as walking or local public transport (buses, trams, metros). A bicycle-sharing scheme is also available at almost 300 stations, with over 21 thousand bicycles, resulting in 5.4 million total trips in 2022 (Dutch Railways, 2023).

In recent years, the rise of digitalisation and increased use of smartphones have brought along with them many new shared (electric) (micro)mobility alternatives to the mobility ecosystem, such as car-sharing, (electric) bicycle-sharing, e-scooters, e-steps to name but a few. Currently mainly present in larger cities and used predominantly for shorter trips within urban areas, they do hold the potential to improve the accessibility of public transport stations, especially for more distant access/egress trips due to the assistance of electric motors.

To get a crucial understanding of train travel, micromobility and how they can interact and complement each other, we investigated the perceptions and preferences of individuals; how they perceive these emerging modes, how likely they are to use them and what they find important when making their travel decisions. We looked into the joint access-mode and train station choice, analysing how the quality of access modes and train service at a specific station affect each other; in other words, how do people trade-off attributes from different legs of the same trip. Secondly, we investigated the potential to use various shared mobility services, included a comparison when pitted against the car and bicycle.
Lastly, we evaluated the impact of introducing shared e-mopeds on public transport, considering it as both an egress mode at the activity-end of the trip, as well as a potential competing mode for the main leg of the journey.

We show that more positive perceptions of micromobility and a higher intention to use such services is often linked with past experience using such services, digital savviness (knowing how to use a smartphone), a more multimodal travel behaviour portfolio (particularly frequent use of public transport) and a higher achieved level of education. We analysed latent market segments and uncovered similar patterns, with a somewhat large share of those ready to use (shared) micromobility, with around a quarter of the population being more sceptical, but then also residing in rural areas, having a lower level of education and being less skilled with digital technology.
These results provide valuable insights for policymakers on how to proceed with introducing such services. Selecting the correct policy is vital to achieve a desired modal shift, as introducing new modes can also result in shifts from modes which are already at a satisfactory level (e-mopeds attracting cyclists for example). We show that shared e-mopeds can be both a competitor and ally to public transport, meaning that the service implementation strategy is key to secure the desired outcomes and mitigate the negative side-effects.
Future research should also compare how the various new micromobility services compete with each other for new and existing travellers. Additionally, checking for potential induced demand of such services, both as a the main mode or simply as an access/egress mode, would be valuable for policymakers.

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.

Understanding people’s travel behavior is key to creating spaces that discourage car use, especially for short, walkable distances. The scope of this study is to understand better people’s propensity to use a car rather than walk for short-distance trips by focusing on the concept of perceived exertion (PE). A comparison is performed of two case study locations: Malta, a Euro-Mediterranean island with a high car dependency, and the Netherlands, a European country with a high active mode share of walking and cycling. Surveys were distributed to two university populations in each of the case study locations to analyze the parallels and variations in travel behavior and perceptions. Applying a mediation model analysis, the results show a partial mediation (Malta) and a full mediation (Netherlands) of PE in the relationship between car use frequency (CF) and distance threshold (DT), that is, the distance people are willing to walk rather than use a car. The mean DT for walking varied significantly between the two samples, resulting in 15.18 min (1.2 km or 0.7 mi) in the Netherlands and 17.99 min (1.4 km or 0.9 mi) in Malta, despite the comparatively larger active mode share in the Netherlands. Complementing this, the ordinal logistic models for the two countries indicate that those that perceive walking for short trips to be more effortful and those with a high CF are less inclined to walk long distances. Findings are compared with previous research, and policy-relevant suggestions based on these findings are provided.

Different leader-follower behaviors may be observed in models, such as group gathering, backtracking, and changing between groups. However, a comparison of these behaviors resulting in possible substantially different estimates of optimal evacuation procedures is lacking. Hence, we developed an agent-based model in combination with exploratory modeling to compare backtracking, group gathering, and followers changing leaders and investigate their influence on the evacuation and response time. The simulation results showed that backtracking and changing of groups increased the evacuation time. Whereby group gathering increase the response time. In addition, the combination of behaviors increases the influence on evacuation and response time. Further research needs to test these results with empirical studies and investigate the impact of other leader-follower behavior. The found insights may be utilized in evacuation research for modeling this behavior and they provide a valuable basis for designing policies in buildings with a high distribution of leader-follower groups.