Over the past decade, the development of ICT and online platforms has provided the infrastructure for new ways of sharing on a scale never seen before which are causing a shift from ownership to access-based- consumption. This trend offers promising prospects for the case of mobility but the true magnitude of impact that the increasing popularity of shared mobility services will have on the total transportation system remains uncertain. For NS, as largest railway operator in the Netherlands, it is therefore relevant to investigate how these new services can contribute to better first and last mile transportation within the multimodal train trip, as most of these types of shared mobility operate on an urban scale. Accordingly, this study aims to explore and measure the factors that affect people’s willingness to use shared mobility services as access or egress transport in multimodal train trips. A series of stated choice experiments was developed in which respondents were asked to choose their preferred mode from a set of alternatives for a given access- or egress trip. Next to conventional modes, included shared modes were bike, (standing) e-scooter, and car. By applying discrete choice modelling, separate mixed logit models were estimated for the home-based side trip (origin to railway station) and the activity based side trip (railway station to final destination) in order to assess the impact of choice factors related to characteristics of the available modes, trip, and traveler. Results show that the willingness to use shared modes is in the first place strongly affected by familiarity with these modes. As the overall observed familiarity and in particular experience with shared modes was low, intrinsic (negative) mode preferences were found to be the dominating choice factors. This was especially the cases for shared e-scooter and to a lesser extent also for the shared car. Traveler characteristics were found affect the magnitude of the fixed mode preference in a sense that young and higher educated travelers significantly appeared to be more open to try shared modes. Contrary to the e-scooter and car, the shared bike exemplifies a more familiar option which was found to results in a different hierarchy of mode related factors: the general fixed mode preference becomes less dominant and usage costs gains more importance.

The aim of this study is to capture heterogeneity in travel behaviour during unplanned train disruptions focusing on the rise of teleworking due to the COVID-19 pandemic to improve train services and help predict passenger flows during disruptions. To perform the analyses, a dataset is elicited from 815 Dutch train commuters by distributing an online questionnaire. A labelled stated choice experiment was designed and a latent class choice model was estimated. The biggest indicators of travel behaviour are the moment of discovering the disruption, the disruption length and job characteristics. Four latent classes were uncovered: the ’Trade-off teleworkers’, ’Sceptic returners’, ’Trusting workplace travellers’ and ’Endless waiters’. Each class has a different initial preference for a travel option and different sensitivities to the travel attributes as well. Individual characteristics such as age, necessity to arrive at the workplace, ability to telework, telework attitude and the amount of trust in the provided travel information play a role in predicting choice behaviour during unplanned train disruptions. Based on the results advice is given on how the level of service during disruptions can be improved.