To simulate the passenger behavior in subway system, a Dynamic Parameters Cellular Automaton (DPCA) model is put forward in this paper. Pedestrian traffic flows during waiting, getting on or off, and traveling can be simulated. The typical scenario in Beijing Subway Line 13 is modeled to analyze the passenger behavior in subway system. By comparing simulation results with statistical ones, the correctness and practicality of the DPCA model are verified. At last, the additional results made by DPCA model can make contribution to passenger comfort analysis and pedestrian facility planning and guidance.
References
[1]
Helbing D., Farkas I., Vicsek T., Simulating dynamical features of escape panic, Nature, vol. 407, no. 6803, pp. 487–490, 2000.
[2]
Shende A., Singh M. P., Kachroo P., Optimal feedback flow rates for pedestrian evacuation in a network of corridors, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1053–1066, 2013.
[3]
Wan Y., Taylor C., Roy S., Wanke C., Zhou Y., Dynamic queuing network model for flow contingency management, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1380–1392, 2013.
[4]
Zou B., Hu J., Wang Q., Ke G., A distributed shortest-path routing algorithm for transportation systems, in Traffic and Transportation Studies 2010, ASCE, 2010.
[5]
Muramatsu M., Irie T., Nagatani T., Jamming transition in pedestrian counter flow, Physica A: Statistical Mechanics and its Applications, vol. 267, no. 3, pp. 487–498, 1999.
[6]
Wakim C. F., Capperon S., Oksman J., A Markovian model of pedestrian behavior, in 2004 IEEE International Conference on Systems, Man and Cybernetics, IEEE, 2004.
[7]
Yuen J. K. K., Lee E. W. M., Lo S. M., Yuen R. K. K., An intelligence-based optimization model of passenger flow in a transportation station, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1290–1300, 2013.
[8]
Lee J. Y. S., Lam W. H. K., Wong S. C., Pedestrian simulation model for Hong Kong underground stations, in 2001 IEEE Intelligent Transportation Systems Conference Proceeding, Oakland, CA, USA, 2001, pp. 25–29.
[9]
Zhu N., Jia B., Shao C.-F., Pedestrian evacuation with the obstacles based on cellular automata, in presented at 2012 Fifth International Joint Conference on Computational Sciences and Optimizationl, Harbin, China, 2012.
[10]
Blue V., Adler J., Emergent fundamental pedestrian flows from cellular automata microsimulation, Transportation Research Record: Journal of the Transportation Research Board, vol. 1644, pp. 29–36, 1998.
[11]
Ma K., Xie Y., Long G., Feng F., Passenger transport special railway, in presented at 7th International Conference on Traffic and Transportation Studies, Kunming, China, 2010.
[12]
Zheng Y., Guo W., Zhang Y., Hu J., A generalized comfort function of subway systems based on a nested logit model, Tsinghua Science and Technology, vol. 19, no. 3, pp. 300–306, 2014.
[13]
Helbing D., Molnar P., Social force model for pedestrian dynamics, Physical Review E, vol. 51, no. 5, p. 4282, 1995.
[14]
Zhang R., Li Z., Hong J., Han D., Research on characteristics of pedestrian traffic and simulation in the underground transfer hub in Beijing, in presented at 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, Korea, 2009.
[15]
Gandhi T., Trivedi M. M., Pedestrian collision avoidance systems: A survey of computer vision based recent studies, in presented at 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada, 2006.
[16]
Yang H., Wu M., Zhang H., Liu Z., A modeling study of the walking speed of the passengers in different areas of a subway station for transfer, (in Chinese), Journal of Transportation Systems Engineering and Information Technology, vol. 11, no. , pp. 141–146, 2011.
