Research on the relationships between travel-related attitudes and travel behaviour has recently been reinvigorated by new theorizing as well as new empirical models. While traditional theories assume a rather static role of attitudes, i.e. acting as stable predispositions that cause behaviours in a unidirectional manner, recent models assume that attitudes and behaviours mutually influence each other over time. This study aims at better understanding attitude-behaviour dynamics by capitalizing on the circumstances presented by the ongoing COVID-19 pandemic. It assesses how the fear of COVID-19 infection and (the attitude towards) working-from-home influence train use as well as train use attitudes. To explore the (within-person) reciprocal relationships between these variables, random-intercept cross-lagged panel models were estimated using a 4-wave longitudinal dataset collected during the COVID-19 pandemic from a large panel of train travellers in the Netherlands. The results indicate that train use and the attitude towards train use reciprocally influence each other. Those with stronger fears of infection in one wave tend to use the train less in a subsequent wave, but higher use of the train in one wave also reduces the fear of infection in the next. We also found that working from home (WFH) and travelling by train operate as substitutes for one another. Moreover, people who work from home frequently become more fearful of infection. All the findings are consistent with cognitive dissonance theory that people develop attitudes that align with their behaviours. The paper concludes with several policy implications related to changing attitudes and promoting train use.@en

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.@en

This study investigates whether the decline in public transit ridership is a temporary phenomenon or indicative of a structural shift in travel patterns and attitudes. We estimate a latent class trajectory model using data from a comprehensive and large-scale survey administered by the Dutch national train operator conducted at eight different points in time after the onset of the pandemic. Six latent trajectories in train use and stated future intentions to use the train are revealed, showing different ‘recovery’ pathways. Whereas low-educated frequent commuters travel almost as much as before, highly educated frequent commuters and mixed-purpose travellers still travel much less, even in the last wave when all restrictions are lifted. The results indicate that travellers belonging to these classes have structurally changed their behaviour. The shift to working from home is more pronounced than the shift to private car use.@en

First and last mile connectivity of public transport hubs is a key component in promoting multi-modal travel. The Dutch train station operator (NS Stations) promotes the combination of bike and train by offering a train station-based round-trip bikesharing (SBRT) scheme, known as ‘OV-fiets’, located at train stations throughout the country. This scheme allows users to rent a bike to travel between train stations and their destination and vice versa. The round-trip nature of the SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. Little is known about the determinants of demand for round-trip bikesharing, especially when being integrated into an existing PT scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system using multiple linear regression (MLR) and an in-depth analysis for selected stations. The results are compared with the findings of one-way bikesharing schemes. The results show that for hourly rentals in an SBRT-system, the highest explanatory power is attributed to the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Furthermore, the magnitude of the correlation between the determinants and the hourly demand varies considerably across stations, depending on the underlying demand patterns.@en

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.@en

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.@en