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Determining an influencing area affecting walking speed on footpath: A case study of a footpath in CBD Bangkok, Thailand

Listed author(s):
  • Tipakornkiat, Chalat
  • Limanond, Thirayoot
  • Kim, Hyunmyung
Registered author(s):

    Intuitively, the crowd density in front of a pedestrian will affect his walking speed along a footpath. Nevertheless, the size of the influencing area affecting walking speed has rarely been scrutinized in the past. This study attempts to determine the distance in front of pedestrians that principally affects their walking speed under normal conditions, using a case study of a footpath in Bangkok. We recorded pedestrian activities along a test section of 20 m, with an effective walking width of 2.45 m in the morning and at noon. The morning dataset was extracted for analyzing various influencing distances, ranging from 1 to 20 m in front of the pedestrian. The bi-directional walking speed–pedestrian density models were developed, for each tested distance, using linear regression analysis. It was found that an influencing length in the range of 5–8 m yields the highest correlation coefficients. In the case of high density conditions, the walking speed of the equally-split flow (50:50) was found to be higher than other proportional flow analyzed. The finding has useful implications on the improvement of the walking simulations in mesoscopic models.

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    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 391 (2012)
    Issue (Month): 22 ()
    Pages: 5453-5464

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    Handle: RePEc:eee:phsmap:v:391:y:2012:i:22:p:5453-5464
    DOI: 10.1016/j.physa.2012.06.001
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    1. Lam, William H. K. & Lee, Jodie Y. S. & Chan, K. S. & Goh, P. K., 2003. "A generalised function for modeling bi-directional flow effects on indoor walkways in Hong Kong," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(9), pages 789-810, November.
    2. Blue, Victor J. & Adler, Jeffrey L., 2001. "Cellular automata microsimulation for modeling bi-directional pedestrian walkways," Transportation Research Part B: Methodological, Elsevier, vol. 35(3), pages 293-312, March.
    3. Seyfried, Armin & Steffen, Bernhard & Lippert, Thomas, 2006. "Basics of modelling the pedestrian flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(1), pages 232-238.
    4. Isobe, Motoshige & Adachi, Taku & Nagatani, Takashi, 2004. "Experiment and simulation of pedestrian counter flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 638-650.
    5. Yuan, Weifeng & Tan, Kang Hai, 2007. "An evacuation model using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 549-566.
    6. Jian, Li & Lizhong, Yang & Daoliang, Zhao, 2005. "Simulation of bi-direction pedestrian movement in corridor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 619-628.
    7. Li, Xiaomeng & Chen, Tao & Pan, Lili & Shen, Shifei & Yuan, Hongyong, 2008. "Lattice gas simulation and experiment study of evacuation dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5457-5465.
    8. O’Connor, A. & Zerger, A. & Itami, B., 2005. "Geo-temporal tracking and analysis of tourist movement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 135-150.
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