Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin-destination matrix estimation
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, United States .
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.ORCID iD: 0000-0002-4106-3126
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.

Place, publisher, year, edition, pages
2015. Vol. 55, p. 231-245
Keyword [en]
SPSA, Origin-destination (OD) matrix estimation, Stochastic approximation, k-means clustering, Gradient estimation bias
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-171910DOI: 10.1016/j.trc.2015.01.016ISI: 000358092100017Scopus ID: 2-s2.0-84936985083OAI: oai:DiVA.org:kth-171910DiVA, id: diva2:845398
Note

QC 20150811

Available from: 2015-08-11 Created: 2015-08-10 Last updated: 2018-01-29Bibliographically 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

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Jenelius, Erik

Search in DiVA

By author/editor
Tympakianaki, AthinaKoutsopoulos, Hans N.Jenelius, Erik
By organisation
Transport planning, economics and engineering
In the same journal
Transportation Research Part C: Emerging Technologies
Transport Systems and Logistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 78 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf