TY - GEN
T1 - Techniques for improving the effectiveness of the SPSA algorithm in dynamic demand calibration
AU - Kostic, Bojan
AU - Gentile, Guido
AU - Antoniou, Constantinos
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/8
Y1 - 2017/8/8
N2 - The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors. To overcome this drawback, some authors suggested modifications of basic SPSA to improve its performance. In this paper, we investigate various techniques and approaches to improve the SPSA performance, and overcome, or at least alleviate, its shortcomings. We concentrate our analysis mostly on SPSA coefficients and gradient control. The comparison of investigated settings is conducted on a real-world network. This establishes a path to identify critical aspects that influence the calibration process and suggests an optimal SPSA configuration for practice. The contribution of this paper is to provide a detailed analysis of the SPSA behavior in cases its configuration is subject to various modifications. The findings are primarily intended for the offline context. However, the insights can also be used for the selection of the most efficient SPSA configuration given time constraint, particularly suitable for on-line applications.
AB - The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors. To overcome this drawback, some authors suggested modifications of basic SPSA to improve its performance. In this paper, we investigate various techniques and approaches to improve the SPSA performance, and overcome, or at least alleviate, its shortcomings. We concentrate our analysis mostly on SPSA coefficients and gradient control. The comparison of investigated settings is conducted on a real-world network. This establishes a path to identify critical aspects that influence the calibration process and suggests an optimal SPSA configuration for practice. The contribution of this paper is to provide a detailed analysis of the SPSA behavior in cases its configuration is subject to various modifications. The findings are primarily intended for the offline context. However, the insights can also be used for the selection of the most efficient SPSA configuration given time constraint, particularly suitable for on-line applications.
KW - Dynamic Traffic Assignment
KW - derivative-free optimization
KW - estimation of origin-destination matrices
KW - fine tuning of Simultaneous Perturbation Stochastic Approximation
UR - http://www.scopus.com/inward/record.url?scp=85030259664&partnerID=8YFLogxK
U2 - 10.1109/MTITS.2017.8005699
DO - 10.1109/MTITS.2017.8005699
M3 - Conference contribution
AN - SCOPUS:85030259664
T3 - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
SP - 368
EP - 373
BT - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017
Y2 - 26 June 2017 through 28 June 2017
ER -