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Demand Estimation and Bottleneck Management Using Heterogeneous Traffic Data
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
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: urn:nbn:se:kth:diva-221850ISBN: 978-91-7729-663-8 (electronic)OAI: oai:DiVA.org:kth-221850DiVA, id: diva2:1178160
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
List of papers
1. c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin-destination matrix estimation
Open this publication in new window or tab >>c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin-destination matrix estimation
2015 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 55, p. 231-245Article in journal (Refereed) Published
Abstract [en]

The simultaneous perturbation stochastic approximation (SPSA) algorithm has been used in the literature for the solution of the dynamic origin-destination (OD) estimation problem. Its main advantage is that it allows quite general formulations of the problem that can include a wide range of sensor measurements. While SPSA is relatively simple to implement, its performance depends on a set of parameters that need to be properly determined. As a result, especially in cases where the gradient of the objective function changes quickly, SPSA may not be as stable and even diverge. A modification of the SPSA algorithm, referred to as c-SPSA, is proposed which applies the simultaneous perturbation approximation of the gradient within a small number of carefully constructed "homogeneous" clusters one at a time, as opposed to all elements at once. The paper establishes the theoretical properties of the new algorithm with an upper bound for the bias of the gradient estimate and shows that it is lower than the corresponding SPSA bias. It also proposes a systematic approach, based on the k-means algorithm, to identify appropriate clusters. The performance of c-SPSA, with alternative implementation strategies, is evaluated in the context of estimating OD flows in an actual urban network. The results demonstrate the efficiency of the proposed c-SPSA algorithm in finding better OD estimates and achieve faster convergence and more robust performance compared to SPSA with fewer overall number of function evaluations.

Keywords
SPSA, Origin-destination (OD) matrix estimation, Stochastic approximation, k-means clustering, Gradient estimation bias
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-171910 (URN)10.1016/j.trc.2015.01.016 (DOI)000358092100017 ()2-s2.0-84936985083 (Scopus ID)
Note

QC 20150811

Available from: 2015-08-11 Created: 2015-08-10 Last updated: 2018-01-29Bibliographically approved
2. Robust SPSA algorithms for dynamic OD matrix estimation
Open this publication in new window or tab >>Robust SPSA algorithms for dynamic OD matrix estimation
2018 (English)In: Procedia Computer Science, 2018, Vol. 130, p. 57-64Conference paper, Published paper (Refereed)
Abstract [en]

The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been used for solving the off-line dynamic origin-destination (OD) estimation problem. While the algorithm can be used with very general formulations of the problem, it can also be unstable. The paper proposes methods and evaluates their effectiveness in improving the SPSA performance at two levels: a) scaling the step size and using a hybrid gradient estimation; and b) proposing alternative clustering strategies to be used with the c-SPSA version of the algorithm, where OD flows are estimated in clusters. The proposed enhancements are evaluated through simulation experiments on a real network.

Keywords
SPSA, c-SPSA, Origin-Destination (OD) matrix estimation, stochastic approximation
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-221856 (URN)
Conference
9th International Conference on Ambient Systems, Networks and Technologies May 8-11, 2018, Porto, Portugal
Note

QC 20180129

Available from: 2018-01-28 Created: 2018-01-28 Last updated: 2018-08-20
3. Anatomy of tunnel congestion: causes and implications for tunnel traffic management
Open this publication in new window or tab >>Anatomy of tunnel congestion: causes and implications for tunnel traffic management
(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
Tunnel traffic management; data-driven analysis; clustering; simulation
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-221858 (URN)
Note

QC 20180129

Available from: 2018-01-28 Created: 2018-01-28 Last updated: 2018-02-08Bibliographically approved
4. Real-time merging traffic control for throughput maximization at motorway work zones
Open this publication in new window or tab >>Real-time merging traffic control for throughput maximization at motorway work zones
Show others...
2014 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 44, p. 242-252Article in journal (Refereed) Published
Abstract [en]

Work zones on motorways necessitate the drop of one or more lanes which may lead to significant reduction of traffic flow capacity and efficiency, traffic flow disruptions, congestion creation, and increased accident risk. Real-time traffic control by use of green-red traffic signals at the motorway mainstream is proposed in order to achieve safer merging of vehicles entering the work zone and, at the same time, maximize throughput and reduce travel delays. A significant issue that had been neglected in previous research is the investigation of the impact of distance between the merge area and the traffic lights so as to achieve, in combination with the employed real-time traffic control strategy, the most efficient merging of vehicles. The control strategy applied for real-time signal operation is based on an ALINEA-like proportional-integral (PI-type) feedback regulator. In order to achieve maximum performance of the control strategy, some calibration of the regulator's parameters may be necessary. The calibration is first conducted manually, via a typical trial-and-error procedure. In an additional investigation, the recently proposed learning/adaptive fine-tuning (AFT) algorithm is employed in order to automatically fine-tune the regulator parameters. Experiments conducted with a microscopic simulator for a hypothetical work zone infrastructure, demonstrate the potential high benefits of the control scheme.

Keywords
Work zone management, Feedback control, Merging traffic control, Adaptive fine-tuning (AFT), Regulator fine-tuning
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:kth:diva-149215 (URN)10.1016/j.trc.2014.04.006 (DOI)000339037100016 ()2-s2.0-84900303467 (Scopus ID)
Note

QC 20140819

Available from: 2014-08-19 Created: 2014-08-18 Last updated: 2018-01-29Bibliographically approved
5. Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm.
Open this publication in new window or tab >>Impact analysis of transport network disruptions using multimodal data: A case study for tunnel closures in Stockholm.
(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
Transport system disruptions; data-driven analysis
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-221857 (URN)
Note

QC 20180129

Available from: 2018-01-28 Created: 2018-01-28 Last updated: 2018-02-08Bibliographically approved

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