Impacts of a multimodal mobility service on travel behavior and preferences: user insights from Munich’s first Mobility Station

The City of Munich, in cooperation with the local public transport provider MVG, is testing a pilot project of a “Mobility Station”, which is a multimodal mobility hub connecting public transport (PT) and new shared mobility services. The project’s goal is to provide sustainable mobility options that allow citizens to be mobile without owning a car. To evaluate the acceptance of the Mobility Station, as well as short and long term effects on mobility behavior, we developed an online user survey in close cooperation with the stakeholders and experts in the field of shared mobility. The results provide insights on the awareness and perception of the Mobility Station among users, their mobility patterns, current degree of multimodality, as well as actual and potential changes on mobility behavior and travel preferences due to the multimodal mobility service. Most users are young, male, and highly educated individuals with access to multiple mobility options. PT plays a central role for daily mobility together with the services they were identified to be customers of. The high share of users that use different mobility services at least once a month indicates some degree of multimodality. Actual and potential changes in mobility behavior towards multimodality were revealed. Some users declared to use other mobility services more often. They appreciate the availability of different mobility options and show interest in other services and intermodal connections indicating that there is still potential to increase multimodal behavior.     

Modelling public transport on‐board congestion: comparing schedule‐based and agent‐based assignment approaches and their implications

Transit systems are subject to congestion that influences system performance and level of service. The evaluation of measures to relieve congestion requires models that can capture their network effects and passengers' adaptation. In particular, on‐board congestion leads to an increase of crowding discomfort and denied boarding and a decrease in service reliability. This study performs a systematic comparison of alternative approaches to modelling on‐board congestion in transit networks. In particular, the congestion‐related functionalities of a schedule‐based model and an agent‐based transit assignment model are investigated, by comparing VISUM and BusMezzo, respectively. The theoretical background, modelling principles and implementation details of the alternative models are examined and demonstrated by testing various operational scenarios for an example network. The results suggest that differences in modelling passenger arrival process, choice‐set generation and route choice model yield systematically different passenger loads. The schedule‐based model is insensitive to a uniform increase in demand or decrease in capacity when caused by either vehicle capacity or service frequency reduction. In contrast, nominal travel times increase in the agent‐based model as demand increases or capacity decreases. The marginal increase in travel time increases as the network becomes more saturated. Whilst none of the existing models capture the full range of congestion effects and related behavioural responses, existing models can support different planning decisions. Copyright © 2016 John Wiley & Sons, Ltd.     

A modeling approach for matching ridesharing trips within macroscopic travel demand models

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.