Concurrent Estimation of Origin-Destination Flows and Calibration of Microscopic Traffic Simulation Parameters in a High-Performance Computing Cluster
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 144, Issue 1
Abstract
This paper is aimed at developing an optimization framework for the concurrent calibration of demand and supply parameters in a dynamic traffic assignment (DTA) model. The proposed approach calibrates route choice, along with drivers’ behavioral parameters, and estimates origin-destination (OD) flows in a large-scale network in a Paramics microscopic traffic simulation model. A mathematical formulation is defined to quantify the reliability of the observations. A genetic algorithm (GA) is selected as a suitable solution algorithm for the resulting nonlinear stochastic optimization problem. The application of the proposed methodology is implemented in the large-scale network in the business district core of downtown Toronto, Ontario, Canada. For this network, the emerging traffic surveillance data from in-vehicle navigation system technology provide an enriched source of disaggregated speed data. The empirical results from various experiments support the hypothesis that incorporating in-vehicle navigation system speed data can improve the calibration accuracy and minimize the reliance of the calibration process on a priori OD flows. The quality of the solution and convergence speed of a GA is further enhanced by dividing the GA population into multiple demes and running the GA on a high-performance computing cluster (HPCC) with multiple processors (i.e., parallel distributed GA, PDGA). In addition, this research takes a further step toward analyzing the temporal variations of the driving behavior of travelers. The case study establishes an example for modelers and practitioners who are interested in calibrating a large-scale traffic simulation model. The developed simulation model for traffic has the potential to serve as a test bed on a HPCC for more efficient computation and integration with other optimization tools such as GAs.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), a Mitacs and CIMA+ Accelerated grant, and the Alberta Motor Association-Alberta Innovates Technology Futures (AMA-AITF) collaborative grant in Smart Multimodal Transportation Systems.
References
Abdelgawad, H. M. (2010). “Optimization of multimodal evacuation of large-scale transportation networks.” Ph.D. dissertation, Univ. of Toronto, Toronto.
Amirjamshidi, G., and Roorda, M. (2011). “Calibration of the Toronto waterfront microsimulation network with OD updating.” 46th Canadian Transportation Research Forum Conf. (CTRF) Gatineau, Canada.
Antoniou, C., Ben-Akiva, M., and Koutsopoulos, H. N. (2005). “Online calibration of traffic prediction models.” Transp. Res. Rec., 1934, 235–245.
Appiah, L., and Rilett, L. R. (2010). “Joint estimation of dynamic origin-destination matrices and calibration of micro-simulation models using aggregate intersection turn count data.” Transportation Research Board 89th Annual Meeting, Transportation Research Board, Washington, DC.
Balakrishna, R., Antoniou, C., Ben-Akiva, M., and Koutsopoulos, H. N. (2007). “Calibration of microscopic traffic simulation models: Methods and application.” Transp. Res. Rec., 1999, 198–207.
Balakrishna, R., Koutsopoulos, H. N., and Ben-Akiva, M. (2005). “Calibration and validation of dynamic traffic assignment systems.” 16th Int. Symp. on Transportation and Traffic Theory, College Park, MD, 407–426.
Brackstone, M., Waterson, B., and McDonald, M. (2009). “Determinants of following headway in congested traffic.” Transp. Res. Part F, 12, 131–142.
Cascetta, E., Papola, A., Marzano, V., Simonelli, F., and Vitiello, I. (2013). “Quasi-dynamic estimation of O-D flows from traffic counts: Formulation, statistical validation and performance analysis on real data.” Transp. Res. Part B, 55, 171–187.
Cheu, R.-L., Jin, X., Ng, K.-C., Ng, Y.-L., and Srinivasan, D. (1998). “Calibration of FRESIM for Singapore expressway using genetic algorithm.” J. Transp. Eng., 526–535.
Chu, L., Liu, H. X., Oh, J. S., and Recker, W. (2003). “A calibration procedure for microscopic traffic simulation.” Proc., 2003 IEEE Int. Conf. on Intelligent Transportation Systems, Vol. 2, Shanghai, China, 1574–1579.
Ciuffo, B., and Azevedo, C. L. (2014). “A sensitivity-analysis-based approach for the calibration of traffic simulation models.” IEEE Trans. Intell. Transp. Syst., 15(3), 1298–1309.
Ciuffo, B., and Punzo, V. (2015). ““No free lunch” theorems applied to the calibration of traffic simulation models.” IEEE Trans. Intell. Transp. Syst., 15(2), 553–562.
EMME/2 [Computer software]. INRO Consultants, Inc., Quebec.
Goldberg, E. D. (1994). “Genetic and evolutionary algorithms come of age.” Commun. ACM, 37(3), 113–119.
Hu, S., Peeta, R. S., and Chu, C. H. (2009). “Identification of vehicle sensor locations for link-based network traffic applications.” Transp. Res. Part B, 43(8–9), 873–894.
Huang, E., Antoniou, C., Lopes, J., Wen, Y., and Ben-Akiva, M. (2010). “Accelerated on-line calibration of dynamic traffic assignment using distributed stochastic gradient approximation.” Proc., 13th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), Madeira, Portugal.
Huynh, N., Mahmassani, H., and Tavana, H. (2002). “Adaptive speed estimation using transfer function models for real-time dynamic traffic assignment operation.” Transp. Res. Rec., 1783, 55–65.
Kattan, L., and Abdulhai, B. (2006). “Non-iterative approach to dynamic traffic origin-destination estimation using parallel evolutionary algorithms.” Transp. Res. Rec., 1964, 201–210.
Kattan, L., and Abdulhai, B. (2011). “Comparative analysis of evolutionary, local search, and hybrid approaches to O/D traffic estimation.” J. Transp. Eng., 46–56.
Kattan, L., and Abdulhai, B. (2012). “Sensitivity analysis of an evolutionary-based time-dependent origin/destination estimation framework.” IEEE Trans. Intell. Transp. Syst., 13(3), 1442–1453.
Lenne, M., Triggs, T., and Redman, J. (1997). “Time of day variations in driving performance.” Accid. Anal. Prev., 29(4), 431–437.
Mahmassani, H., Qin, X., and Zhou, X. (2004). “DYNASMART-X evaluation for real-time TMC application: Irvine test bed.”, Univ. of Maryland, College Park, MD.
Mahut, M., Florian, M., Tremblay, N., Campbell, M., Patman, D., and McDaniel, Z. K. (2004). “Calibration and application of a simulation-based dynamic traffic assignment model.” Transp. Res. Rec., 1876, 101–111.
Omrani, R., and Kattan, L. (2012). “Demand and supply calibration of dynamic traffic assignment models: Past efforts and future challenges.” Transp. Res. Rec., 2283, 100–112.
Omrani, R., and Kattan, L. (2013). “Simultaneous calibration of microscopic traffic simulation model and estimation of origin/destination (OD) flows based on genetic algorithms in a high-performance computer.” Proc., 16th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), Delft, Netherlands.
Paramics [Computer software]. Quadstone Paramics, Pitney Bowes Software, Henley-on-Thames, Oxfordshire, U.K.
Paz, M. V., Molano, V., and Khan, A. (2014). “Calibration of micro-simulation traffic-flow models considering all parameters simultaneously.” Compendium of Papers of the 93rd Annual Meeting of Transportation Research Board, Washington, DC.
Peeta, S., and Ziliaskopoulos, A. K. (2001). “Foundations of dynamic traffic assignment: The past, the present and the future.” Networks Spatial Econ., 1(3–4), 233–265.
Punzo, V., and Ciuffo, B. (2009). “How parameters of microscopic traffic flow models relate to traffic dynamics in simulation.” Transp. Res. Rec., 2124, 249–256.
Qin, X., and Mahmassani, H. (2004). “Adaptive calibration of dynamic speed-density relations for online network traffic estimation and prediction applications.” Transp. Res. Rec., 1876, 82–89.
Smith, M. C., Sadek, A. W., and Huang, S. (2008). “Large-scale microscopic simulation: Toward an increased resolution of transportation models.” J. Transp. Eng., 273–281.
Tavana, H., and Mahmassani, H. (2000). “Estimation and application of dynamic speed-density relations by using transfer function models.” Transp. Res. Rec., 1710, 47–57.
Toledo, T., Ben-Akiva, M., Darda, D., Jha, M., and Koutsopoulos, H. N. (2004). “Calibration of microscopic traffic simulation models with aggregate data.” Transp. Res. Rec., 1876(1), 10–19.
Treiber, M., and Kesting, A. (2014). “Microscopic calibration and validation of car-following models—A systematic approach.” 20th Int. Symp. on Transportation and Traffic Theory (ISTTT 20), Vol. 80, 922–939.
Vaze, V., Antoniou, C., Wen, Y., and Ben-Akiva, M. (2009). “Calibration of dynamic traffic assignment models with point-to-point traffic surveillance.” Transp. Res. Rec., 2090, 1–9.
Verbas, I. O., Mahmassani, H., and Zhang, K. (2011). “Time-dependent origin-destination demand estimation: Challenges and methods for large-scale networks with multiple vehicle classes.” Transp. Res. Rec., 2263, 45–56.
Xiong, C., et al. (2015). “Developing a 24-hour large-scale microscopic traffic simulation model for the before-and-after study of a new tolled freeway in the Washington, DC–Baltimore region.” J. Transp. Eng., 05015001.
Zhang, H. M., Nie, Y., and Qian, Z. (2008). “Estimating time-dependent freeway origin-destination demands with different data coverage.” Transp. Res. Rec., 2047, 91–99.
Zheng, H., Nava, E., Chiu, Y.-C., Xyntarakis, M., and Sabina, E. (2013). “Development and calibration of a daily regional dynamic traffic assignment model—A case study for Denver region.” Compendium of Papers of the 92nd Annual Meeting of Transportation Research Board, Washington, DC.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 1 • January 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 23, 2016
Accepted: Jun 9, 2017
Published online: Nov 2, 2017
Published in print: Jan 1, 2018
Discussion open until: Apr 2, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.