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  • 1. Order onlineBuy this publication >>
    Danielsson, Anna
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Towards efficient urban road transport using multimodal traffic management2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    As travel demand and urbanization increase, they cause road con-gestion. This results in lost productivity, reduced accessibility, and negative effects on the environment. Solutions to reduce congestion in the transport network include urban traffic management. It could for example be regulating signal control, variable speed limit, and ramp metering, or distributing traveler information about traveltimes and congestion through radio broadcasts, 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 both the public transport network and private vehicles enable a better understanding of multimodal travel patterns. Traffic data can also be used to estimate reliable traffic models that can support evaluation and prioritization of traffic management measures. 

    The aim of the thesis is to identify synergies and challenges of multimodal traffic management. The aim includes analyzing, devel-oping, and evaluating dynamic route choice models that can support multimodal traffic management decisions, using large-scale passive mobility data. First, recent trends are explored in the transition to more efficient road transport, emphasizing the role of monitoring and modeling 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 system that can help to prioritize between multimodal traffic management measures are also identified. Based on these requirements, route choice in the road network is analyzed using GPS trajectory data. This provides insights into how data-driven route choice models can be a component in multimodal traffic management. 

    The thesis contributes to the understanding of how a decision support system for multimodal traffic management can be developed, how route choice modeling can be used in such a tool, and how multimodal traffic management is needed in the transition towards more efficient road transport. 

    List of papers
    1. Transition towards more efficient road transports: insights from mobility analytics
    Open this publication in new window or tab >>Transition towards more efficient road transports: insights from mobility analytics
    2023 (English)In: Handbook on Climate Change and Technology / [ed] Frauke Urban, Johan Nordensvärd, Northampton: Edward Elgar Publishing, 2023, p. 180-195Chapter in book (Refereed)
    Place, publisher, year, edition, pages
    Northampton: Edward Elgar Publishing, 2023
    Series
    Elgar handbooks in energy, the environment and climate change
    National Category
    Transport Systems and Logistics
    Identifiers
    urn:nbn:se:liu:diva-203031 (URN)9781800882102 (ISBN)9781800882119 (ISBN)
    Available from: 2024-04-24 Created: 2024-04-24 Last updated: 2024-05-07Bibliographically approved
    2. Analysis of Route Sets and Attributes in Route Choice Estimation for Urban Traffic Management Using GPS Data
    Open this publication in new window or tab >>Analysis of Route Sets and Attributes in Route Choice Estimation for Urban Traffic Management Using GPS Data
    2024 (English)In: Proceedings of the 103rd Transportation Research Board Annual Meeting, 2024Conference paper, Published paper (Refereed)
    Abstract [en]

    Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context.

    Series
    TRB Annual Meeting Online
    Keywords
    Probe Vehicle Data, Systems Management, Choice Models
    National Category
    Transport Systems and Logistics
    Identifiers
    urn:nbn:se:liu:diva-203155 (URN)
    Conference
    Transportation Research Board Annual Meeting. Washington D.C., USA, January 7th-11th 2024
    Note

    Funding: This work was supported by the Swedish Transport Administration (Trafikverket) via the Centre for Traffic Research (CTR) [grant number TRV 2020/118663].

    Available from: 2024-04-30 Created: 2024-04-30 Last updated: 2024-04-30Bibliographically approved
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  • 2.
    Danielsson, Anna
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Analysis of Route Sets and Attributes in Route Choice Estimation for Urban Traffic Management Using GPS Data2024In: Proceedings of the 103rd Transportation Research Board Annual Meeting, 2024Conference paper (Refereed)
    Abstract [en]

    Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context.

  • 3.
    Danielsson, Anna
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Gundlegård, David
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Rydergren, Clas
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Tsanakas, Nikolaos
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Transition towards more efficient road transports: insights from mobility analytics2023In: Handbook on Climate Change and Technology / [ed] Frauke Urban, Johan Nordensvärd, Northampton: Edward Elgar Publishing, 2023, p. 180-195Chapter in book (Refereed)
1 - 3 of 3
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