Operational Scenario Definition in Traffic Simulation-Based Decision Support Systems: Pattern Recognition Using a Clustering Algorithm
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 4
Abstract
This paper is intended to mine historical data by presenting a scenario clustering approach to identify appropriate scenarios for mesoscopic simulation as a part of the evaluation of transportation projects or operational measures. It provides a systematic and efficient approach to select and prepare effective input scenarios for a given traffic simulation model. The scenario clustering procedure has two primary applications: travel time reliability analysis, and traffic estimation and prediction systems. The ability to systematically identify similarity and dissimilarity among weather scenarios can facilitate the selection of critical scenarios for reliability studies. It can also support real-time weather-responsive traffic management (WRTM) by quickly classifying a current or predicted weather condition into predefined categories and suggesting relevant WRTM strategies that can be tested via real-time traffic simulation before deployment. A detailed method for clustering weather time series data is presented and demonstrated using historical data. Two clustering algorithms with different similarity measures are compared. Clustering results using a -means clustering algorithm with squared Euclidean distance are illustrated in the travel time reliability application.
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Acknowledgments
This paper is based in part on work funded by the US Department of Transp. under contract DTFH61-06-D-00005, T-10-005 to Northwestern University’s Transportation Center (NUTC). The authors acknowledge helpful comments provided by Roemer Alfelor of the US Department of Transportation and Robert Haas of SAIC, Inc.
References
Adamson, G. W., and J. A. Bush. 1975. “A comparison of the performance of some similarity and dissimilarity measures in the automatic classification of chemical structures.” J. Chem. Inf. Comput. Sci. 15 (1): 55–58. https://doi.org/10.1021/ci60001a016.
Amarendra Kumar, S., and S. Ashoke Kumar. 2013. “Clustering of pavement stretches and determining optimum number of clusters for pavement maintenance.” In Proc., Transportation Research Board 92nd Annual Meeting. Washington, DC: National Research Council.
Antoniou, C., H. N. Koutsopoulos, and G. Yannis. 2013. “Dynamic data-driven local traffic state estimation and prediction.” Transp. Res. Part C: Emerging Technol. 34: 89–107. https://doi.org/10.1016/j.trc.2013.05.012.
Azimi, M., and Y. Zhang. 2010. “Categorizing freeway flow conditions by using clustering methods.” Transp. Res. Rec. 2173: 105–114. https://doi.org/10.3141/2173-13.
Buxi, G., and M. Hansen. 2013. “Generating day-of-operation probabilistic capacity scenarios from weather forecasts.” Transp. Res. Part C: Emerging Technol. 33: 153–166. https://doi.org/10.1016/j.trc.2012.12.006.
Chen, Y., J. Kim, and H. S. Mahmassani. 2014. “Pattern recognition using clustering algorithm for scenario definition in traffic simulation-based decision support systems.” In Proc., IEEE 17th Int. Conf. on Intelligent Transportation Systems (ITSC), 798–803. New York: IEEE.
El Faouzi, N.-E., and E. Lefevre. 2006. “Classifiers and distance-based evidential fusion for road travel time estimation.” In Proc., Defense and Security Symp. Cardiff, UK: International Society for Optics and Photonics.
Gower, J. C., and G. Ross. 1969. “Minimum spanning trees and single linkage cluster analysis.” Appl. Stat. 18 (1): 54–64. https://doi.org/10.2307/2346439.
Green, P. E., and V. R. Rao. 1969. “A note on proximity measures and cluster analysis.” J. Marketing Res. 6 (3): 359–364. https://doi.org/10.1177/002224376900600314.
Guha, S., R. Rastogi, and K. Shim. 1998. “CURE: An efficient clustering algorithm for large databases.” In Proc., ACM SIGMOD Record, 73–84. New York: ACM.
Hu, C., N. Luo, X. Yan, and W. Shi. 2011. “Traffic flow data mining and evaluation based on fuzzy clustering techniques.” Int. J. Fuzzy Syst. 13 (4): 344.
Jain, A. K., and R. C. Dubes. 1988. Algorithms for clustering data. Upper Saddle River, NJ: Prentice-Hall.
Johnson, S. C. 1967. “Hierarchical clustering schemes.” Psychometrika 32 (3): 241–254. https://doi.org/10.1007/BF02289588.
Kianfar, J., and P. Edara. 2013. “A data mining approach to creating fundamental traffic flow diagram.” Procedia Social Behav. Sci. 104: 430–439. https://doi.org/10.1016/j.sbspro.2013.11.136.
Kim, J., H. S. Mahmassani, R. M. Alfelor, Y. Chen, T. Hou, L. Jiang, M. Saberi, O. Verbas, S. Cheng, and A. Zockaie. 2013a. “Implementation and evaluation of weather responsive traffic management strategies: Insight from different networks.” Transp. Res. Rec. 2396: 93–106. https://doi.org/10.3141/2396-11.
Kim, J., H. S. Mahmassani, P. Vovsha, Y. Stogios, and J. Dong. 2013b. “Scenario-based approach to travel time reliability analysis using traffic simulation models.” Transp. Res. Rec. 2391: 56–68. https://doi.org/10.3141/2391-06.
Liao, T. W. 2005. “Clustering of time series data: A survey.” Pattern Recognit. 38 (11): 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025.
Ma, X., Y. J. Wu, Y. Wang, F. Chen, and J. Liu. 2013. “Mining smart card data for transit riders’ travel patterns.” Transp. Res. Part C: Emerging Technol. 36: 1–12. https://doi.org/10.1016/j.trc.2013.07.010.
MacQueen, J. 1967. “Some methods for classification and analysis of multivariate observations.” In Proc., 5th Berkeley Symp. on Mathematical Statistics and Probability, 14. Berkeley, CA: University of California Press.
Mahmassani, H. S., J. Dong, J. Kim, R. B. Chen, and B. Park. 2009. Incorporating weather impacts in traffic estimation and prediction systems. Washington, DC: US Dept. of Transportation.
Mahmassani, H. S., T. Hou, and M. Saberi. 2013. “Connecting network-wide travel time reliability and the network fundamental diagram of traffic flow.” Transp. Res. Rec. 2391: 80–91. https://doi.org/10.3141/2391-08.
Mahmassani, H. S., J. Kim, T. Hou, A. Zockaie, M. Saberi, L. Jiang, Ö. Verbas, S. Cheng, Y. Chen, and R. Haas. 2012. Implementation and evaluation of weather responsive traffic estimation and prediction system. Washington, DC: US Joint Program Office for Intelligent Transportation Systems.
Milligan, G. W., and M. C. Cooper. 1987. “Methodology review: Clustering methods.” Appl. Psychol. Meas. 11 (4): 329–354. https://doi.org/10.1177/014662168701100401.
Nadolski, V. 1998. Automated surface observing system (ASOS) user’s guide. Washington, DC: Dept. of Defense, Federal Aviation Administration, US Navy, National Oceanic and Atmospheric Administration.
Sneath, P. H. A., and R. R. Sokal. 1973. Numerical taxonomy: The principles and practice of numerical classification. San Francisco: W. H. Freeman and Co.
Sun, L., and J. Zhou. 2005. “Development of multiregime speed-density relationships by cluster analysis.” Transp. Res. Rec. 1934: 64–71.
Willett, P. 1983. “Similarity coefficients and weighting functions for automatic document classification: An expirical comparison.” Int. Classification 10 (3): 138–142.
Willett, P. 1988. “Recent trends in hierarchic document clustering: A critical review.” Inf. Process. Manage. 24 (5): 577–597. https://doi.org/10.1016/0306-4573(88)90027-1.
Xia, J., and M. Chen. 2007. “A nested clustering technique for freeway operating condition classification.” Comput.-Aided Civ. Infrastruct. Eng. 22 (6): 430–437. https://doi.org/10.1111/j.1467-8667.2007.00498.x.
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©2019 American Society of Civil Engineers.
History
Received: Jul 24, 2016
Accepted: Aug 27, 2018
Published online: Feb 8, 2019
Published in print: Apr 1, 2019
Discussion open until: Jul 8, 2019
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