A DMB-TCA Simulation Method for On-Road Traffic Travel Demand Impact Analysis

Authors: Zundong Zhang, Limin Jia, Zhao Tian, Yanfang Yang

Abstract:

Travel Demands influence micro-level traffic behavior, furthermore traffic states. In order to evaluate the effect of travel demands on traffic states, this paper introduces the Demand- Motivation-Behaviors (DMB) micro traffic behavior analysis model which denotes that vehicles behaviors are determines by motivations that relies on traffic demands from the perspective of behavior science. For vehicles, there are two kinds of travel demands: reaching travel destinations from orientations and meeting expectations of travel speed. To satisfy travel demands, the micro traffic behaviors are delivered such as car following behavior, optional and mandatory lane changing behaviors. Especially, mandatory lane changing behaviors depending on travel demands take strong impact on traffic states. In this paper, we define the DMB-based cellular automate traffic simulation model to evaluate the effect of travel demands on traffic states under the different δ values that reflect the ratio of mandatory lane-change vehicles.

Keywords: Demand-Motivation-Behavior, Mandatory Lane Changing, Traffic Cellular Automata.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087928

References:

[1] S. Wolfram, Theory and applications of cellular automata. Singapore: World Scientific, 1986.
[2] M.Cremer, J.Ludwig, “A fast simulation model for traffic flow on the basis of boolean operations”, Mathematics and Computers in Simulation, vol. 28, no. 4, pp. 297 C 303, 1986.
[3] K. Nagel and M. Scheckenberg, “A Cellular Automaton Model for Freeway Traffic”, J Phys I France, vol. 2, pp.2221-2229,1992.
[4] R. Barlovic and L. Santen, “Metastable States in Cellular Automata for Traffic Flow”, Eur Phys J B, vol.5, no. 3, pp.793-800, 1998.
[5] M. Takayasu and H. Takayasu, “1/f Noise in a Traffic Model”, Factral, vol.1, no. 5, pp.860-866, 1993.
[6] S.C. Benjamin, N.F. Johnson and P.M. Hui, “Cellular automata models of traffic flow along a highway containing a junction”, J. Phys A, vol.29, p.3119, 1996.
[7] D. E. Wolf, “Cellular Automata for Traffic Simulations”, Phys A, vol. 263, pp.438-451, 1999.
[8] M. Fukui and Y. Ishibashi, “Traffic Flow in 1D Cellular Automata Model Including CarsMoving with High Speed”, Japan: J Phys Soc, vol.65, no.1, pp.868-870,1996.
[9] T. Nagatani, “Self-organization and phase transition in traffic-flow model of a two-lane roadway”, J. Phys. A: Math. Gen., vol. 26, p. L781, 1993.
[10] T. Nagatani, “Dynamical jamming transition induced by a car accident in traffic-flow model of a two-lane roadway,” Physica A: Statistical Mechanics and its Applications, vol. 202, no. 3-4, pp. 449 C 458, 1994.
[11] M. Rickert, K. Nagel, M. Schreckenberg and A. Latour, “Two lane traffic simulations using cellular automata,” Physica A: Statistical Mechanics and its Applications, vol. 231, no. 4, pp. 534 C 550, 1996.
[12] P. Wagner, K. Nagel, and D. E. Wolf, “Realistic multi-lane traffic rules for cellular automata,” Physica A: Statistical Mechanics and its Applications, vol. 234, no. 3, pp. 687 C 698, 1997.
[13] K. Nagel, D. E. Wolf, P. Wagner and P. Simon, “Two-lane traffic rules for cellular automata: A systematic approach,” Pys Rev E, vol. 58, pp. 1425C1437, 1998.
[14] W. Knospe, L. Santen, A. Schadschneider and M. Schreckenberg, “A realistic two-lane traffic model for highway traffic,” Journal of Physics A: Mathematical and General, vol. 35, no. 15, p. 3369, 2002.