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.

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.

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.

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.

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