Increasing travel demand and urbanization lead to road congestion. This results in lost productivity, reduced accessibility, and negative effects on the environment. An already congested transportation system is sensitive to disturbances, and even minor incidents can have major consequences in terms of queues and societal costs. Solutions to reduce congestion in the transport network include urban traffic management. Traffic management measures include regulating signal control, variable speed limits, and ramp metering, as well as distributing traveller information about travel times, congestion, and incidents through radio broadcasting, variable message signs, or navigation apps. A multimodal traffic management system utilizes several transportation modes within an integrated system to improve network performance and robustness. Large-scale mobility data from the public transport network and private vehicles enable a better understanding of travel patterns in multimodal networks. Traffic data can also be used to estimate traffic models that support the evaluation and prioritization of traffic management measures. For example, a route choice model can be used to identify travellers that are most impacted by an incident, and therefore need information about alternative routes or transportation modes.

The aim of the thesis is to utilize mobility data to improve the understanding and support of multimodal traffic management. The aim includes analysing incident impact on multimodal transportation networks, and developing and evaluating route choice models to use as a component in multimodal traffic management decision support systems. In the thesis, recent trends are explored in the transition towards more efficient road transport, emphasizing the role of monitoring and modelling traffic. Second, related literature is surveyed to identify the potential synergies and challenges of multimodal traffic management. Requirements of data and models in a decision support systems for multimodal traffic management are also identified. Based on these requirements, route choice behaviour in the road network is analysed using GPS trajectory data. Last, travel patterns during incidents are analysed to identify the impact of incidents on delay, demand, route choice and mode share. The thesis contributes to a better understanding of multimodal traffic management by integrating largescale passive mobility data into incident impact analysis and route choice modelling.