Posts tagged demand responisve transit

Performance assessment of fixed and flexible public transport in a multi agent simulation framework

The emergence of innovative mobility solutions that offer flexible transport services, is changing the way urban public transport systems will be designed. Such mobility solutions offer on demand transport services and hence can solve the problems inherent with traditional line based and schedule based public transport systems. It is essential to understand the dynamics of this new demand-supply market with co-existing and competing fixed and flexible public transport. However, the performance of the system comprising of users and transit services and the factors influencing them, have received limited attention in literature. In this paper a model is developed to analyse the system performance when the modes of fixed public transport and flexible public transport operate in competition. The model is implemented in the multi-agent simulation framework MATSim with dynamic assignment in which the users optimize their travel plan through iterative learning from the service experienced and altering their travel plan. The scenarios in which the flexible public transport offer private and shared services are considered. The system performance is analysed for varying fleet size of flexible public transport and ratio of cost of flexible to fixed public transport.

Find the paper HERE

Urban Demand Responsive Transport in the Mobility as a Service ecosystem: its role and potential market share

Mobility as a Service (MaaS) is entering the transportation market. MaaS aims at the full
integration of the existing transportation services and it offers tailored mobility packages to
the user. In MaaS ecosystems, on-demand services play an important role as complement to
public transport due to their flexibility. However, to date, most attention has been placed on
individual on-demand services. This study focuses on Demand Responsive Transport (DRT):
collective on-demand services. Using an on-line survey, we analysed the characteristics of
the respondents who chose different modes of transport among their selected modes.
Results find a distinctive pattern in the willingness of users to use different modes, with
different levels in what could be considered as a multimodality ladder. The different rungs of
it would be: 1st car (if available), 2nd public transport, 3rd DRT and 4th taxi-like services.
This way, a person standing on the third rung would include car, public transport and DRT in
their consideration set, but not taxi. This finding suggests that, if implemented in the right
way, DRT services can attract a larger number of users than taxi-like services, especially in a
MaaS ecosystem where initial barriers to try this service can be lessened.

Find the paper presented by Maria Alonso Gonzalez at the Thredbo conference in Stockholm HERE

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