Data is essential for effective urban planning and management. This chapter provides a comprehensive overview of data and data collection techniques for pedestrian planning, aiming to provide researchers and practitioners insights into selecting suitable data and data collection techniques based on their specific pedestrian planning needs. This chapter begins by outlining the taxonomy of data for pedestrian planning, identifying the types of pedestrian behaviour, data types, and data features that are important for pedestrian planning considerations. It specifically identifies four types of data that are essential for pedestrian planning, namely environmental and infrastructure data, traffic data, personal characteristics, and physiological data. This chapter provides a comprehensive overview of each type of data used in pedestrian planning and where these data can be sourced. Moreover, this chapter provides an in-depth overview of different data collection techniques used in pedestrian planning, including sensors, crowd sourcing, and eXtended Reality. The advantages and limitations of each technique are also discussed, offering practical insights for employing them for data collection purposes. In summary, this chapter serves as a comprehensive guide to understanding the why, what, where, and how of using data to enhance pedestrian planning. It offers the readers the knowledge to collect and use data effectively, which ultimately supports the designing, planning, and management of pedestrian-friendly urban environments.

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.

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.

As bicycle use increases, so does the need for formal parking spaces to safely store them while performing other activities at a destination. In the Netherlands, several municipalities have created indoor and outdoor formal parking spaces, which remain underutilised. Instead, many cyclists choose to ‘fly park’, i.e. informally lock their bicycle to objects on the street. This can cause dangerous situations or inconvenience, for example by blocking sidewalks. The discrepancy between the use of formal and informal parking spaces may be attributed to a lack of information provided to cyclists about the available formal parking options. This study investigated the effectiveness of different traffic sign designs in encouraging the use of formal parking spaces. The designs were developed within this research with the intention of capturing different communication strategies, namely hazardous, neutral educative and negative educative. A stated preference choice experiment was then performed to allow the comparison of the effectiveness of the different designs, and thus communication strategies. The responses were analysed using discrete choice modelling. According to the results, traffic signs alerting users to the fact that controls are performed (hazardous communication) are the most effective in the fly parking prevention, especially for frequent bicycle users.

To promote urban sustainability, many cities are adopting bicycle-friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map-matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map-matching methods face challenges in accurately selecting the best-mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map-matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi-objective map-matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second-largest city in the Netherlands, is selected as the case study city, and real-world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high-traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.

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.

The bicycle is a very important mode for travel in various countries, particularly in the Netherlands. However, it is in practice often modelled with less detail than other urban modes, such as the car and public transport. Moreover, the increasing use of e-bikes and the differences with conventional bikes show that more research into this transport mode is needed. E-bikes require less physical effort and allow higher speeds, making the e-bike suitable for longer distances. The goals of this research are to (1) create a mode choice model that predicts an accurate modal split for urban areas in the Netherlands and this model is used to (2) find significant factors that influence the modal split, in order to support municipalities of Dutch urban areas to stimulate the use of the (e-)bike. Within both goals, potential differences between conventional bikes and e-bikes are considered. A conceptual model, following from the literature, describes the assumed modal choice including factors relevant to cycling. Data was used mainly from the Dutch National Travel Survey (ODiN). Discrete choice models, a multinomial logit and a nested logit, are estimated to identify significant influencing factors. Results show that a nested logit model is the most explanatory one compared to the other models, with a rho-square-bar of 0.469. The model includes 15 main variables, 3 quadratic components and 4 interaction effects. The nested structure is formed by a correlation between the bike and the e-bike. The factors that show to be generally highly influential for the bike and the e-bike are the travel distance, owning a driver’s license and street density. The model is practically applicable for municipalities to form expectations in the modal shift for changes in their networks or policies. However, modelling these changes has not been validated and thus needs further research.

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.

Sufficient cycle track width is important to prevent single-bicycle crashes and collisions between cyclists. The assumptions on which the minimum width is based in guidelines is founded on only a few studies. The aim of the present study is to investigate the relationship between cycle track width and lateral position of cyclists. Method: We conducted an experiment to evaluate the lateral position of cyclists along cycle tracks with different widths (Study 1). Participants cycled on an instrumented bicycle with a LIDAR to measure their lateral position. Five conditions were defined: cycle track width of 100 cm, 150 cm and 200 cm without interaction, and cycle track width of 150 cm and 200 cm with an oncoming cyclist simulated by a parked bicycle. The cross-sectional Study 2 is based on the collected lateral position measurements at cycle tracks with varying width reported in Dutch studies since 2010. Results: The experimental Study 1 with 24 participants shows that an increase in cycle track width causes cyclists to ride further away from the verge and keep more distance from an oncoming cyclist. The cross-sectional Study 2 was based on lateral position measured at 33 real-life Dutch cycle tracks. Study 2 yielded similar results, indicating that doubling pavement width increases lateral position by some 50%. Study 2 shows that, compared with a solo cyclist without interaction, a right-hand cyclist of a duo and a cyclist meeting an oncoming cyclist ride around 30% closer to the verge. Conclusions: The wider the cycle track, the more distance cyclists maintain from the verge. Cyclists ride closer to the verge due to oncoming cyclists. Practical applications: Given a cyclists’ lateral position while meeting, common variations between cyclists’ steering behavior, and vehicle width and circumstances, a cycle track width of 250 cm is needed for safe meeting maneuvers.

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.

Currently, the research on ship behavior during encounters focuses on evasive behavior during specific situations with existing risks of collision. However, the preliminary selection of encounters to refine the presented ship behavior is biased. To obtain a full understanding of all ship behavior during different encounters in ports and waterways, the encounter is defined from the viewpoint of the spatial-temporal co-existence of ships in the same waterway segments during the same period. Based on this definition, this paper investigates ship behavior through the encounter process with other ships. The proposed approach starts from the moment when the distance in between is minimum as the critical moment to recognize ship behavior change (course alteration and speed change) based on the Sliding Window algorithm. Thus, the encounter process is identified by the key behavior feature point into phases, being before decision-making, before the critical moment, after the critical moment, and after being past and clear. The relative movement factors are calculated according to the behavior status of both ships to describe the conditions, timing, and objective of behavior change during the dynamic process of encounters. The empirical findings based on one-year Automatic Identification System data in the port of Rotterdam are presented. In the overtaking encounters, as the give-way ship, about 14% of the overtaking ships do not take any evasive actions. Among the ships with behavior changes, the preference for course alteration and speed change is equal. As the stand-on ship, about 87% of the overtaken ships take cooperative maneuvers to facilitate the encounter, in which deceleration seems the primary choice. The timing of overtaken ship's behavior change is later than overtaking ship. For overtaking ships, the objective of course alteration is a clear passing distance of about 5 times her beam, 100m for overtaken ships irrespective of her own size. Regarding speed, the overtaking ship aims to reach a relative speed of 0.3 times her own SOG, while the objective for the overtaken ship is fixed at around 2–3 m/s. In the encounters of ships sailing in the opposite direction, most of the ships take maneuvers to change their course or speed. However, within the influence distance of 2 km, over 76% of the ships do not take any evasive behavior, which implies a passing-by situation. Based on the recognized key feature points of behavior change, statistical tests show the objective of clear passing distance has been reached beforehand. The behavior change during head-on situations could be due to the precautionary behavior of officers onboard in case of interaction between ships. The findings enrich the knowledge of ship behavior during different types of encounters in real-life navigation, which can be further applied to simulation models for ship behavior in ports and waterways.

Bicycle networks are made up of different types of infrastructure for cars, bikes and mixed use, which has resulted in various definitions being used to describe them. However, it’s crucial to bring these definitions together to understand the structural differences among them and the impact of choices and investments in bike infrastructure. This study examines different definitions of bicycle networks in 47 cities, analysing scaling effects and various network metrics for four different definitions. Understanding structural characteristics of different bicycle networks definitions contributes to the body of knowledge necessary for design interventions by policymakers.

When making trips in urban environments, cyclists lose time as they stop and idle at signalized intersections. The main objective of this study was to show how augmenting the situational awareness of traffic signal controllers, using observations from moving sensor platforms, can enable prioritization of cyclists and reduce lost time within the control cycle in an effective way. We investigated the potential of using observations from connected autonomous vehicles (CAVs) as a source of new information, using a revised vehicle-actuated controller. This controller exploits CAV-generated observations of cyclists to optimize the control for cyclists. The results from a simulation study indicated that with a low CAV penetration rate, prioritizing cyclists by tracking reduced cyclist delays and stops, even with a small field of view. As the delay of car directions were not taken into account in this study, the average car delay increased considerably with an increasing number of cyclists. Future work is needed to optimize the control that balances the delays and stops of cyclists and cars.

In the study of pedestrian dynamics, the consideration of group behaviours is essential, as group interaction within crowds is a common phenomenon and significantly influences pedestrian behaviours. This special issue highlights the importance of pedestrian group behaviours and examines the subject using a wide range of methods, such as experiments, modelling, and algorithms. The works comprising this special issue can hence provide ideas for future studies on pedestrian dynamics and can also serve as a long-term reference for modelling pedestrian group behaviours.

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.

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.

Cycling contributes to public health because it requires physical effort [1] and offers economic and environmental advantages over motorized transport [2]. However, 41,000 cyclists die every year in traffic crashes, 3% of the total worldwide [3]. Most fatal bicycle crashes are collisions with motor vehicles. Severe injuries among cyclists, however, are mostly due to single bicycle crashes and their numbers are increasing [4, 5]. An international review showed that the share of hospitalised casualties due to single-bicycle crashes varied from 3% to 41 % of the total number of hospitalised casualties [ 6]. ... The aim of the present study is to investigate the relationship between cycle track width and lateral position. We conducted an experiment in which the cycle track width was manipulated to determine its effect on lateral position. The results have been compared with previous findings from literature.

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.

In this paper, we study observed cycling distances within an accessibility framework, using data from the Netherlands, the Copenhagen Metropolitan Area and the Freiburg Region. As a scope, we look at outbound trips in home-based tours which include a single destination. We relate these observed cycling distances to a rich set of explanatory variables using both quantile and ordinary least square regression models. The results provide evidence that cycling distances are similarly distributed in all three regions. Most cycling distances are rather short, with a median of only two and a mean of three kilometers. These values vary depending on the type of activity at the destination, gender and age of the traveler and the type of bicycle that has been used. Moreover, a few remarkable differences have been found between the three regions, such as substantially different effects of age and e-bike use on observed cycling distances. Noteworthy is the missing effect of urban density. The findings of this research provide urban planners with differentiated information about how far people cycle to daily-life destinations. As shown for the example of the “15 minutes city,” the outcomes can also be used to refine existing concepts of bicycle accessibility. Finally, this research offers valuable insights into three of Europe’s most developed bicycle cultures.

Pedestrians are among the travelers most vulnerable to collisions that are associated with high fatality and injury rates. The increasing rate of urbanization and mixed land-use construction make walking (along with other non-motorized travel) a predominant transportation mode with a wide variety of behaviors expected. Because of the inherent safety concerns seen in pedestrian transportation infrastructures, especially those with conflicting multimodal movements expected (crosswalks, transit platforms, etc.), it is important that pedestrian behavior is modeled as a risk-taking stochastic behavior that may lead to errors and thus collision formation. In previous work, the complexity and cost associated with building pedestrian models in a cognitive-based environment weighted down the construction of simulation tools that can capture pedestrian-involved collisions, including those seen in shared space environments. In this paper, a tool that will help evaluate the safety of pedestrian traffic is initiated: an extended modeling framework of pedestrian walking behavior is adopted while incorporating different physiological, physical, and decision-making elements. The focus is on operational decisions (i.e., path choices defined by longitudinal and lateral trajectories) with a pre-specified set of origins and destinations. The model relies on the prospect theory paradigm where pedestrians evaluate their acceleration and directional alternatives while considering the possibility of colliding with other ‘‘particles.’’ Using a genetic algorithm method, the new model is calibrated using detailed trajectory data. This model can be extended to model the interactions between a variety of different modes that are present in different mixed land-use environments.