An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models. (February 2015)
- Record Type:
- Journal Article
- Title:
- An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models. (February 2015)
- Main Title:
- An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models
- Authors:
- Lu, Lu
Xu, Yan
Antoniou, Constantinos
Ben-Akiva, Moshe - Abstract:
- Highlights: W-SPSA, an enhanced SPSA algorithm that limits the impact of noise, is presented. W-SPSA incorporates information of spatial and temporal correlation in a traffic network to a «weight» matrix. Implementation details in the context of Dynamic Traffic Assignment (DTA) models are discussed. Benefits of W-SPSA are illustrated in a case study of the entire expressway network in Singapore. W-SPSA appears to outperform the original SPSA algorithm. Abstract: Simultaneous perturbation stochastic approximation (SPSA) is an efficient and well established optimization method that approximates gradients from successive objective function evaluations. It is especially attractive for high-dimensional problems and has been successfully applied to the calibration of Dynamic Traffic Assignment (DTA) models. This paper presents an enhanced SPSA algorithm, called Weighted SPSA (W-SPSA), which incorporates the information of spatial and temporal correlation in a traffic network to limit the impact of noise and improve convergence and robustness. W-SPSA appears to outperform the original SPSA algorithm by reducing the noise generated by uncorrelated measurements in the gradient approximation, especially for DTA models of sparsely correlated large-scale networks and a large number of time intervals. Comparisons between SPSA and W-SPSA have been performed through rigorous synthetic tests and the application of W-SPSA for the calibration of real world DTA networks is demonstrated with aHighlights: W-SPSA, an enhanced SPSA algorithm that limits the impact of noise, is presented. W-SPSA incorporates information of spatial and temporal correlation in a traffic network to a «weight» matrix. Implementation details in the context of Dynamic Traffic Assignment (DTA) models are discussed. Benefits of W-SPSA are illustrated in a case study of the entire expressway network in Singapore. W-SPSA appears to outperform the original SPSA algorithm. Abstract: Simultaneous perturbation stochastic approximation (SPSA) is an efficient and well established optimization method that approximates gradients from successive objective function evaluations. It is especially attractive for high-dimensional problems and has been successfully applied to the calibration of Dynamic Traffic Assignment (DTA) models. This paper presents an enhanced SPSA algorithm, called Weighted SPSA (W-SPSA), which incorporates the information of spatial and temporal correlation in a traffic network to limit the impact of noise and improve convergence and robustness. W-SPSA appears to outperform the original SPSA algorithm by reducing the noise generated by uncorrelated measurements in the gradient approximation, especially for DTA models of sparsely correlated large-scale networks and a large number of time intervals. Comparisons between SPSA and W-SPSA have been performed through rigorous synthetic tests and the application of W-SPSA for the calibration of real world DTA networks is demonstrated with a case study of the entire expressway network in Singapore. … (more)
- Is Part Of:
- Transportation research. Volume 51(2015)
- Journal:
- Transportation research
- Issue:
- Volume 51(2015)
- Issue Display:
- Volume 51, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 51
- Issue:
- 2015
- Issue Sort Value:
- 2015-0051-2015-0000
- Page Start:
- 149
- Page End:
- 166
- Publication Date:
- 2015-02
- Subjects:
- Dynamic Traffic Assignment -- Calibration -- Simultaneous perturbation stochastic approximation (SPSA) -- Weighted SPSA (W-SPSA)
Transportation -- Periodicals
Transportation -- Technological innovations -- Periodicals
388.011 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0968090X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.trc.2014.11.006 ↗
- Languages:
- English
- ISSNs:
- 0968-090X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9026.274620
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British Library HMNTS - ELD Digital store - Ingest File:
- 6199.xml