Multimodal Traffic Management (MMTL)
The project aims to develop new methods for estimating travel demand as well as mode and route choice for multimodal traffic management. Potential effects of multimodal traffic management will also be analyzed in the project.
New systems for combining modes of transport, such as Mobility as a Service (MaaS), provide new opportunities for road users to switch between different modes of transport. At the same time, large amounts of data from both the public transport and the road traffic network as well as multimodal data from mobile networks in combination with new processing methods provide opportunities for a completely new understanding of multimodal travel patterns in a city. Understanding how multimodal travel patterns develop over time provides new possibilities to develop effective tools for multimodal traffic management.
The purpose of the project is to extend the dynamic estimation of OD matrix and route choice for the road network to a dynamic estimate of a multimodal OD matrix including mode and route choice to enable multimodal traffic management. This can provide a wider range of congestion countermeasures and a better basis for choosing measures for traffic management centers such as Trafik Stockholm.
A dynamic multimodal OD matrix in combination with knowledge of mode and route choice enables analysis of correlation between events in different modes of transport. The OD matrix can also be used together with information about route choice to, in the event of incidents in one of the transport modes, identify groups of users that 1) can provide high relief on heavily loaded links and 2) have good alternatives to switch to other transport modes. This can be done both before departure, but also during travel depending on the possibility of switching between the different modes.
The project includes a PhD project focusing on analyzing, developing, and evaluating mode and route choice models for multimodal traffic management using large-scale passive data.
The project is funded by Trafikverket via Centre for Traffic Research (CTR) and is carried out as a doctoral project with collaboration between LiU, KTH and Trafik Stockholm.