Integrating an Agent-Based Travel Behavior Model with Large-Scale Microscopic Traffic Simulation for Corridor-Level and Subarea Transportation Operations and Planning Applications
Publication: Journal of Urban Planning and Development
Volume 139, Issue 2
Abstract
Application of microscopic traffic simulation beyond the corridor level analysis is not widely seen in literature. This is partly because of the fact that a simulation model cannot capture behavior responses such as peak spreading. This study develops a framework that integrates agent-based travel behavior models with large-scale traffic simulation to capture the regional impacts of new development. The proposed model is then applied to the I-270/I-495/I-95 corridor in the north Washington, DC metropolitan area in a case study. Findings from this study reveal the potential of the proposed model to capture network dynamics and behavioral reactions. This framework also provides a valuable tool for the evaluation of new transportation infrastructure, such as the intercounty connector (ICC) corridor currently under construction, and its operation strategies.
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Acknowledgments
This research was funded partially by the Maryland State Highway Administration, Federal Highway Administration Exploratory Advanced Research Program, and the Center for Integrated Transportation Systems Management at the Univ. of Maryland. The views in this paper do not necessarily reflect the views of the funding agencies. The authors are solely responsible for all statements in the paper.
References
Ashok, K., and Ben-Akiva, M. E. (1993). “Dynamic origin-destination matrix estimation for real-time traffic management systems.” Transportation and traffic theory, Proc., 12th Int. Symp., C. F. Daganzo, ed., Elsevier, 465–484.
Banister, D. (1978). “The influence of habit formation on modal choice: A heuristic model.” Transportation, 7(1), 5–18.
Barceló, J., and Casas, J. (2005). “Dynamic network simulation with AIMSUN.” Simul. Approaches Transp. Anal., 31, 57–98.
Bekhor, S., Dobler, C., and Axhausen, K. W. (2011). “Integration of activity-based and agent-based models.” Transportation Research Record 2255, Transportation Research Board, Washington, DC, 38–47.
Bradley, M., Bowman, J. L., and Griesenbeck, B. (2009). “SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution.” J. Choice Modell., 3(1), 5–31.
Cascetta, E., Inaudi, D., and Marquis, G. (1993). “Dynamic estimators of origin destination matrices using traffic counts.” Transp. Sci., 27(4), 363–373.
Cendrowska, J. (1987). “PRISM: An algorithm for inducing modular rules.” Int. J. Man Mach. Stud., 27(4), 349–370.
Chen, A., Lee, D. H., and Jayakrishnan, R. (2002). “Computational study of state-of-the-art path-based traffic assignment algorithms.” Math. Comput. Simul., 59(6), 509–518.
Cohen, W. W. (1995). “Fast effective rule induction.” 12th Int. Conf. on Machine Learning, A. Prieditis and S. Russell, eds., Morgan Kaufmann, Lake Tahoe, CA, 115–123.
Esser, J., and Nagel, K. (2001). “Iterative demand generation for transportation simulations.” The leading edge of travel behavior research, D. Hensher and J. King, eds., Pergamon, Oxford, UK, 659–681.
Ettema, D. F., Tamminga, G., Timmermans, H. J. P., and Arentze, T. A. (2005). “A micro-simulation model system of departure time using a perception updating model under travel time uncertainty.” Transp. Res. Part A: Policy Pract., 39(4), 325–344.
Fisk, C. S. (1988). “On combining maximum entropy trip matrix estimation with user optimal assignment.” Transp. Res. Part B, 22(1), 245–250.
Flötteröd, G., Bierlaire, M., and Nagel, K. (2011). “Bayesian demand calibration for dynamic traffic simulations.” Transp. Sci., 45(4), 541–561.
Flötteröd, G., Chen, Y., Rieser, M., and Nagel, K. (2009). “Behavioral calibration of a large-scale travel behavior microsimulation.” Proc., 12th Int. Conf. on Travel Behaviour Research (IATBR), Jaipur, India.
Frank, E., and Witten, I. H. (1998). “Generating accurate rule sets without global optimization.” 15th Int. Conf. on Machine Learning, J. Shavlik, ed., Morgan Kaufmann, Burlington, MA, 144–151.
Golledge, R., and Stimson, R. (1997). Spatial behavior, Guilford, New York.
Gomes, G., May, A., and Horowitz, R. (2004). “Congested freeway micro-simulation model using VISSIM.” Transportation Research Record 1876, Transportation Research Board, Washington, DC, 71–81.
Hao, J. Y., Hatzopoulou, M., and Miller, E. J. (2010). “Integrating an activity-based travel demand model with dynamic traffic assignment and emission models: An implementation in the greater Toronto area.” Transportation Research Record 2176, Transportation Research Board, Washington, DC, 1–13.
Horni, A., and Axhausen, K. W. (2012). “How to improve MATSim destination choice for discretionary activities?” Proc., 12th Swiss Transport Research Conf., Monte Verità/Ascona.
Jintanakul, K. (2011). “Dynamic demand input preparation for planning applications.” Ph.D. dissertation, Univ. of California, Irvine.
Lin, D.-Y., Eluru, N., Waller, S. T., and Bhat, C. R. (2008). “Integration of activity-based modeling and dynamic traffic assignment.” Transportation Research Record 2076, Transportation Research Board, Washington, DC, 52–61.
Lin, P. W., and Chang, G. L. (2006). “Modeling measurement errors and missing initial values in freeway dynamic origin–destination estimation systems.” Transp. Res. Part C, 14(6), 384–402.
Liu, S., and Fricker, J. D. (1996). “Estimation of a trip table and the Θ parameter in astochastic network.” Transp. Res. Part A, 30(4), 287–305.
The Maryland-National Capital Park and Planning Commission. (2009). Gaithersburg west master plan: The Life Sciences Center, Montgomery County Planning Department, 〈http://www6.montgomerycountymd.gov/content/exec/cpus/pdfs/MNCPPCStaffDraft.pdf〉 (Apr. 07, 2013).
Miller, E. J., and Roorda, M. J. (2003). “Prototype model of household activity scheduling.” Transportation Research Record 1831, Transportation Research Board, Washington, DC, 114–121.
Nielson, O. A. (1997). “Multi-path OD-matrix estimation (MPME) based on stochastic user equilibrium traffic assignment.” Presented at the 76th Annual Meeting of Transportation Research Board, Transportation Research Board, Washington, DC.
de Palma, A., and Marchal, F. (2002). “Real cases applications of the fully dynamic METROPOLIS tool-box: An advocacy for large-scale mesoscopic transportation systems.” Networks Spatial Econ., 2(4), 347–369.
Prevedouros, P. D., and Wang, Y. (1999). “Simulation of large freeway and arterial network with CORSIM, INTEGRATION, and WATSim.” Transportation Research Record 1678, Transportation Research Board, Washington, DC, 197–207.
Quinlan, J. R. (1986). “Induction of decision trees.” Mach. Learn., 1(1), 81–106.
Resource Systems Group, et al. (2009). “SHRP2 C10A: Partnership to develop an integrated, advanced travel demand model and a fine-grained time-sensitive network, task 1 report: Project approach and industry synthesis.” Transportation Research Board online publications, 〈http://onlinepubs.trb.org/onlinepubs/conferences/2012/4thITM/PPT/w1C.pdf〉 (Jun. 25, 2012).
Rothschild, M. (1974). “Searching for the lowest price when the distribution of prices is unknown.” J. Political Econ., 82(4), 689–711.
Small, K. A. (1982). “The scheduling of consumer activities: Work trips.” Am. Econ. Rev., 72(3), 467–479.
Tavana, H., and Mahmassani, H. (2001). “Estimation of dynamic origin-destination flows from sensor data using bi-level optimization method.” Proc., 80th Annual Meeting of the Transportation Research Board, Paper No. 01-3241, Transportation Research Board, Washington, DC.
Witten, I. H., and Frank, E. (2000). Data mining, Morgan Kaufmann, San Francisco.
Wojtowicz, J., Wallace, W., Murrugarra, R. I., Sheckler, R., and Morgan, D. (2011). “The role of transportation in responding to a catastrophe at a planned special event.” 〈http://www.homelandsecurity.org/DHSUnivSummit2011/Wojtowicz_White%20Paper_04May11.pdf〉 (Jul. 20, 2011).
Zhang, L., and Xiong, C. (2012). “A positive model of departure time choice and peak spreading dynamics.” Transportation Research Board 91th Annual Meeting Compendium of Papers DVD #12-4514, Transportation Research Board, Washington, DC.
Van Zuylen, H. J., and Willumsen, L. G. (1980). “The most likely trip matrix from traffic counts.” Transp. Res. Part B, 14(3), 281–293.
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© 2013 American Society of Civil Engineers.
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Received: Feb 22, 2012
Accepted: Sep 7, 2012
Published online: Sep 10, 2012
Published in print: Jun 1, 2013
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