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Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, United States.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.ORCID iD: 0000-0002-4106-3126
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The paper explores the utilization of heterogeneous data sources to analyze the multimodal impacts of transport network disruptions. A systematic data-driven approach is proposed for the analysis of impacts with respect to two aspects: (a) spatiotemporal network changes, and (b) multimodal effects. The feasibility and benefits of combining various data sources are demonstrated through a case study for a tunnel in Stockholm, Sweden which is often prone to closures. Several questions are addressed including the identification of impacted areas, and the evaluation of impacts on network performance, demand patterns and performance of the public transport system. The results indicate significant impact of tunnel closures on the network traffic conditions due to the redistribution of vehicles on alternative paths. Effects are also found on the performance of public transport. Analysis of the demand reveals redistribution of traffic during the tunnel closures, consistent with the observed impacts on network performance. Evidence for redistribution of travelers to public transport is observed as a potential effect of the closures. Better understanding of multimodal impacts of a disruption can assist authorities in their decision-making process to apply adequate traffic management policies.

Keywords [en]
Transport system disruptions; data-driven analysis
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-221857OAI: oai:DiVA.org:kth-221857DiVA, id: diva2:1178109
Note

QC 20180129

Available from: 2018-01-28 Created: 2018-01-28 Last updated: 2018-02-08Bibliographically approved
In thesis
1. Demand Estimation and Bottleneck Management Using Heterogeneous Traffic Data
Open this publication in new window or tab >>Demand Estimation and Bottleneck Management Using Heterogeneous Traffic Data
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Congestion on urban and freeway networks has become a major problem, leading to increased travel times and reduced traffic safety. In order to suggest traffic management solutions to improve the transport system efficiency, it is important to capture the travel demand patterns, expressed as origin-destination (OD) matrices, and understand the mechanisms of traffic bottlenecks. The increasing availability of traffic data offers significant opportunities to effectively address these issues. The thesis uses heterogeneous traffic data to improve three important problems.

The first problem relates to the dynamic OD estimation problem, which entails significant challenges due to its complexity. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been commonly used to solve the problem, which can handle any available data that can improve the estimation accuracy. However, it encounters stability and convergence issues. The thesis proposes a general modification of SPSA, called cluster-wise SPSA (c-SPSA), that has more robust performance and finds better solutions. Its efficiency is demonstrated through simulation experiments for a network from Stockholm.

The second problem focuses on the development of methods for utilizing heterogeneous traffic data for the analysis and management of freeway work zone and tunnel bottlenecks. Simulation is used as the means to evaluate and optimize various mitigation strategies for each case.

The third problem analyzes multimodal impacts due to network disruptions for the case of tunnel bottlenecks, using a data-driven approach. Tunnel congestion is often dealt with temporary closures, which may cause significant disruptions. It is crucial to identify the potential multimodal impacts of such interventions so as to design efficient and proactive mitigation strategies. The thesis shows the benefits of combining multiple data sources to analyze the impacts of temporary tunnel closures for a freeway tunnel in Stockholm.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 41
Series
TRITA-ABE-DLT ; 1802-001
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-221850 (URN)978-91-7729-663-8 (ISBN)
Public defence
2018-02-23, Kollegiesalen, Brinellvägen 8, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20180129

Available from: 2018-01-29 Created: 2018-01-29 Last updated: 2018-01-29Bibliographically approved

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