A Study on the Development of Path Finding Algorithm for Passenger Flow in Railway Station

Page:

344-352

DOI:

https://doi.org/10.2495/TDI-V4-N4-344-352

© 2020 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract:

A computational path finding method has been developed to simulate the flow of passengers at railway transit stations. The concept of computational method is very similar to particle-laden flow. The basic procedures are as follows. Like general computational fluid dynamics, the computational domain is divided into meshes and potential values are calculated for each cell by providing boundary conditions for inlet and outlet. The path line is then calculated according to the potential value. The path line obtained is the basic moving path, but it is an algorithm that finds a new path by changing the path according to the situation. Representative situations in which passengers may change routes at railway stations are as follows. That is, there is a slow pedestrian in the direction that the pedestrian is going to move or encounter a pedestrian in the opposite direction. According to a specific rule-based system, we developed an algorithm to find the path to change, and the main factors such as walking speed, viewing angle, straightness, walking on the right, etc. were considered. The analysis results show that pedestrians do not move along the shortest paths but change their paths from time to time. Through the analysis of passenger flow, it is expected that it can be applied to the optimal structural design of railway stations and the convenient flow of passenger.

Keywords:

*computational method, crowd flow, path finding, railway station, rule-based system*

References

[1] Nam, S., Development of algorithm for passenger flow analysis based on DEM. Journal of the Korean Society for Railway, 8(4), pp.337-341, 2005.

[2] Nam, S., Crowd flow simulation using the potential path line method, WIT Transactions on Engineering Sciences, 125, pp.44-837, 2019.

[3] Helbing, D., Farkas, I. & Vicsek, T., Simulating dynamical features of escape panic, Nature, 407(28), pp.487-490, 2000.

[4] Helbing, D., Molnar, P., Social force model for pedestrian dynamics, Physical Review E, 51, pp.4282-4286, 1995.

[5] Seyfried, A., Steffen, B. & Lippert, T., Basics of modelling the pedestrian flow, Physica A, 368, pp.232-238, 2006.

[6] Nagal, K., Schreckenber, M., A cellular automation model for freeway traffic, Journal of Physics, 2(12), pp.2221-2229, 1992.

[7] Burstedde, C., Klauck, K. & Schadschneider, A., Simulation of pedestrian dynamics using a two-dimensional cellular automaton, Physica A, 295, pp.507-525, 2001.

[8] Blue, V., Adler, J., Cellular automata microsimulation for modelling bi-directional pedestrian walkways, Transportation Research Part B, 35, pp.293-312, 2001.

[9] Helbing, D., Isobe, M., Nagatani, T. & Takimoto, K., Lattice gas simulation of experimentally studied evacuation dynamics, Physical Review E, 67, 067101, pp.1-4, 2003.

[10] Guo, R., Huang, H., A mobile lattice gas model for simulating pedestrian evacuation, Physica A, 387, pp.580-586, 2008.

[11] Antonini, G., Bierlaire, M. & Weber, M., Discrete choice models of pedestrian walking behavior, Transportation Research Part B, 40, pp.667-687, 2006.

[12] Singh, H., Arter, R., Dodd, L., Langston, P., Lester, E. & Drury, J., Modelling subgroup behavior in crowd dynamics DEM simulation, Applied Mathematical Modelling, 33, pp.4408-4423, 2009.

[13] Heliovaara, S., Korhonen, T., Hostikka, S. & Ehtamo, H., Counterflow model for agentbased simulation of crowd dynamics, Building and Environment, 48, pp.89-100, 2012.

[14] Lohner, R., On the modelling of pedestrian motion, Applied Mathematical Modelling, 34, pp.366-382, 2010.