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.

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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.

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As the title suggests, cyclists are the main topic of this dissertation and more specifically, their behaviour while they are ‘in motion’. The term ‘in motion’ is used in the title to represent microscopic operational cycling behaviour, which is the behaviour of cyclists, treated as individuals (microscopic level), while they are riding their bicycle and making decisions on how to interact with other traffic participants and with the infrastructure (operational level). Within this dissertation, models are developed to capture this behaviour using data collected for this purpose. Further empirical data analyses led to more behavioural insights and design recommendations were provided based on the findings. In this summary, each of these elements is shortly discussed, along with the need for this research.@en

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.@en

This paper presents a novel microscopic modelling framework for bicycle flow operations. The modelling principles are based on similar principles successfully applied in our previous work on pedestrian and vessel flow. The main contributions of the paper are in the extension towards modelling cyclists that has not been proposed in literature before, and in the insights gained by simulation with the model using different scenarios, showing how the model outcomes depend on the modelling choices and parameters. The generalisation entails two major changes compared to our previous pedestrian model. First of all, the model does justice to the kinematics of cyclists. Contrary to pedestrians, cyclist are more restricted in their movement. The model approximates these restrictions by considering speed and movement direction and changes therein. Secondly, the model includes different strategies (cooperative, zero-acceleration, demon opponent) in its underlying game-theoretical framework, and allows including traffic rules. This allows us to model different attitudes towards risk representing different types of cyclists. The (qualitative) insights gained by application of the model pertain to one-on-one interactions between cyclists and the impact of the strategy assumptions and parameter choices on those interactions as well as on the collective phenomena that occur in the cyclist flow and their sensitivity to parameters (reflecting the extent of the prediction horizon, the level of anisotropy, and the relative importance of keeping the desired path). With respect to the collective phenomena, we look at efficiency and self-organised patterns. We conclude that the model acts in a plausible manner. While we do not aim to show empirical validity, we see that the qualitative behaviour of one-on-one interactions is plausible if compared to experimental or field data. We also observe plausible collective patterns, including forms of self-organisation under specific parameter settings. The latter is not trivial given the fundamental differences in bicycle and pedestrian flow.

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Due to the increasing use of the bicycle as access and egress mode to public transport hubs, bicycle parking facilities are being constructed and extended. The provision of appropriate infrastructure requires the understanding of the needs and preferences of the cyclists. In order to gain insights into the parking spot choices of the cyclists, we have collected data at a bicycle parking facility in Delft train station (the Netherlands). This dataset is used to estimate discrete choice models that distinguish between an uncongested (i.e. many empty spots) and a congested (i.e. almost full) state of the facility. The findings reveal that cyclists want to park close to the exit of the parking facility which promotes the design in tiers. At the same time, they want to exert minimal effort, which calls for an easier mechanism to park in the top tier. Last but not least, providing real-time information signs is advisable, as it facilitates the parking spot choice as by reducing searching time.@en

Density is one of the most relevant variables in a traffic flow description. For objects in 2 dimensions, density can be determined by the space that is allocated to each of the objects. This paper introduces a new way of computing the space available for a bicyclist, accounting for speed and accounting for the non-zero size of a bicycle. This changes local densities. The proposed method modifies the Voronoi densities, and assigns space to a bicycle. We assign space to bicycle A if it has a closer proximity to any point of bicycle A than any point of any other bicycle. The proximity is determined by the distance and the angle in relation to velocity of the bicycle. Specific proximity functions need to be formulated and calibrated to match cyclist behavior. This method helps to define a density level for cyclists, which in turn can for instance lead to a better indication of a Level of Service.@en

Cycling research at the operational behavioral level is limited, mainly because of the lack of empirical data. To overcome this data shortage, we performed a controlled, large-scale cycling experiment in the Netherlands. In this paper we describe the methodology for setting up and implementing such an experiment, from the motivation of its design using a conceptual model describing cyclist behavior to adjustments that were required during the experiment. The main contribution of this paper is, therefore, to be used as a guide in future experimental data collections. Moreover, we present the characteristics of the participants and their bicycles, and provide a qualitative description of phenomena observed during the experiment. Finally, we elaborate on the potential that the collected dataset holds for future research into understanding and modeling operational cycling behavior.@en

Operational cycling behaviour is greatly understudied, even lacking a definition of what this behavioural level actually entails in terms of decision making. In this paper, we define the cyclist operational level and argue that it consists of two intertwined processes, a mental and a physical process. The mental process refers to path choices made within a route and the physical process refers to the bicycle control dynamics through pedalling and steering. We propose a novel two-layer framework, where each layer captures the tasks of one of the processes within the operational level. Discrete choice theory is proposed to model each layer. The plausibility of the framework is demonstrated through an application focusing on the queue formation process upstream of a red traffic light, including selecting a queuing position and cycling towards it. Models are estimated for the two layers using cyclist trajectory data collected at a signalised intersection in Amsterdam, the Netherlands. The models reveal the attributes that influence the decisions made in each layer and are face validated using simulation. The proposed framework and the (behavioural) findings of its application are the main scientific contributions of this paper, which pave the way for future research.@en

Real-time holding control strategies are implemented, among other reasons, in order to protect transfers. In the context of high-frequency services, there is a need to reconcile between striving for single-line regularity and synchronizing inter-line arrivals. Their operationalization depends on the predictions regarding passenger flows across the network. We examine the influence of real-time passenger data on the performance of transfer synchronization control. To this end, we develop two real-time transfer synchronization controllers which make use of different passenger data sources. The controllers differ in their assumptions concerning capacity constraints as well as on-board crowding conditions. The results show that each transferring passenger saves on average 2–10 min thanks to the proposed strategy, while on-board passengers experience a delay of 1–2 min each in most cases. The highest time saving per transferring passenger is obtained when the demand level is low and the controller opts for synchronizing more frequently. HighlightsRule-based holding controller selects transfer synchronization or line regularityThe impact of different passenger data on controller performance is investigatedOn-board crowding conditions are considered by the real-time controllerOn-board occupancy is the most valuable real-time passenger data source

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