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Towards efficient urban road transport using multimodal traffic management
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1588-3809
2024 (English)Licentiate 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. 

Abstract [sv]

I takt med en ökande urbaniseringstrend, i kombination med ökat resande, ökar även trängseln i vägnätet. Det resulterar i minskad produktivitet, begränsad tillgänglighet och negativ miljöpåverkan. De negativa konsekvenserna av trängsel kan minskas med hjälp av trafikledningsåtgärder. Det kan till exempel vara reglering av trafiksignaler, variabla hastighetsskyltar och påfartsramper eller spridning av trafikinformation om restider och köer via radio, digitala vägskyltar och navigationsappar. Multimodal trafikledning utnyttjar olika transportslag i trafikledningsåtgärderna för att öka trafiksystemets effektivitet och robusthet. Med hjälp av stora mängder observationer av resor med både kollektivtrafiken och privata fordon kan förståelsen för multimodala resmönster öka. Trafikdata kan även användas för att skapa tillförlitliga trafikmodeller som i sin tur kan vara ett stöd för att utvärdera och prioritera åtgärdsplaner för trafikledning.

Syftet med denna avhandling är att identifiera möjligheter och utmaningar med multimodal trafikledning. Syftet inkluderar även att använda stora mängder passiva mobilitetsdata för att analysera, utveckla och utvärdera dynamiska ruttvalsmodeller att använda som beslutsstöd i multimodal trafikledning. Först utforskas trender mot ett effektivt transportsystem, där vikten av observering och modellering av trafik betonas. Därefter presenteras en litteraturöversikt om multimodal trafikledning där möjligheter och utmaningar diskuteras. Där identifieras även krav på data och modeller att använda som beslutsstöd för att prioritera mellan multimodala trafikledningsåtgärder. Baserat på dessa krav analyseras vidare ruttval i vägnätet med hjälp av GPS-spår. Detta ger insikter om hur datadrivna ruttvalsmodeller kan användas som en komponent i multimodal trafikledning.

Sammanfattningsvis bidrar avhandlingen till en förståelse av hur beslutsstöd för multimodal trafikledning kan utvecklas, hur ruttvalsmodeller kan användas i ett sådant system, och betydelsen av multimodal trafikledning i skiftet mot ett mer effektivt transportsystem.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. , p. 31
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1991
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-203156DOI: 10.3384/9789180756204ISBN: 9789180756198 (print)ISBN: 9789180756204 (electronic)OAI: oai:DiVA.org:liu-203156DiVA, id: diva2:1855179
Presentation
2024-05-21, K3, Önnesjösalen, Campus Norrköping, 10:15 (English)
Opponent
Supervisors
Note

Funding: The work was funded by the Swedish Transport Administration via the Centre for Traffic Research, CTR.

Available from: 2024-04-30 Created: 2024-04-30 Last updated: 2024-04-30Bibliographically approved
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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