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Anatomy of tunnel congestion: causes and implications for tunnel traffic management
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]

Tunnel congestion is an important safety problem and is often dealt with using disruptive traffic management strategies, such as closures. The paper proposes an approach to identify the underlying causes of recurrent congestion in tunnels and tests the hypothesis that the cause may vary from day to day. It also suggests that the appropriate tunnel management strategy to deploy depends on the cause. Utilizing traffic sensor data the approach consists of: (i) cluster analysis of historical traffic data to identify distinct congestion patterns; (ii) in-depth analysis of the underlying demand patterns and associated bottlenecks; (iii) simulation to evaluate alternative strategies for each demand pattern; (iv) on-line classification analysis which is able to identify, in real time, the emerging congestion pattern, and inform the type of mitigation strategy to be implemented. The methodology is demonstrated for a congested tunnel in Stockholm, Sweden revealing two different spatiotemporal congestion patterns. The results show that, if the current strategy of closures is to be used, the timing should depend on the congestion pattern. However, metering is the most promising strategy. The on-line classification of the emerging congestion pattern is effective and can inform appropriate strategy proactively. The analysis emphasizes that the effectiveness of tunnel traffic management can be increased by identifying the causes of congestion on a given day. 

Keywords [en]
Tunnel traffic management; data-driven analysis; clustering; simulation
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-221858OAI: oai:DiVA.org:kth-221858DiVA, id: diva2:1178111
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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