Heterogeneous Pedestrian Traffic Flow Modelling at Signalised Intersections

  • Huiling Zhang Transportation School, Chongqing Jiaotong University
  • Bangshun Xi Transportation School, Chongqing Jiaotong University
DOI: https://doi.org/10.7307/ptt.v34i2.3829
Keywords: pedestrian, pedestrian traffic flow models, pedestrian heterogeneity, signalised intersections, pedestrian flow

Abstract

Different types of pedestrians exhibit different speed characteristics and heterogeneity. In the case of mixed pedestrian flow at signalised intersections, pedestrian traffic flow modelling is important in research of the con-ditions at signalised intersections and the evaluation of services for pedestrians. The characteristics of pedestri-an traffic flow at signalised intersections were investi-gated in this study against the background of pedestrian heterogeneity using videos of pedestrians crossing three signalised intersections in Chongqing recorded in a field survey. The pedestrian walking speeds were manually ex-tracted from the videos and used as the data basis for dis-tinguishing pedestrian heterogeneity. The walking speed data of three types (young, middle-aged, and elderly) of pedestrians at different pedestrian flows were obtained by using a microsimulation software. Based on this, a pe-destrian traffic flow model for mixed-type pedestrians at signalised intersections was established and verified by actual cases. In comparison with the HCM model, the model outperforms the HCM model overall in practical applications, indicating its strong applicability and reli-ability.

References

Li B, et al. Age differences analysis of pedestrian travel demand at signalized intersection. Journal of Wuhan University of Technology (Transportation Science & Engineering). 2016;40(2): 309-314. doi: 10.3963/j.issn.2095-3844.2016.02.022.

Transportation Research Board. Highway capacity manual 2010. Washington，D．C.: Transportation Research Board, National Research Council; 2010.

Tanaboriboon Y, Hwa SS, Chor CH. Pedestrian characteristics study in Singapore. Journal of Transportation Engineering. 1986;112(3): 229-235. doi: 10.1061/(ASCE)0733-947X(1986)112:3(229).

Polus A, Schofer JL, Ushpiz A. Pedestrian flow and level of service. Journal of Transportation Engineering. 1983;109(1): 46-56. doi: 10.1061/(ASCE)0733-947X(1983)109:1(46).

Lam WHK, Morrall JF, Ho H. Pedestrian flow characteristics in Hong Kong. Transportation Research Record. 1995;1487: 56-62.

Das S, Mukherjee D, Saha P, Roy SK. Pedestrian flow characteristics at signalized intersections in mixed traffic situations: A case study in Kolkata, India. Procedia Computer Science. 2018;130: 150-156. doi: 10.1016/j.procs.2018.04.024.

Sharifi MS, Song Z, Esfahani HN, Christensen K. Exploring heterogeneous pedestrian stream characteristics at walking facilities with different angle intersections. Physica A: Statistical Mechanics and Its Applications. 2020;540: 112-123. doi: 10.1016/j.physa.2019.123112.

Wu HL, et al. A capacity simulation model of stairway in subway station considering pedestrian heterogeneity. Journal of Transportation Systems Engineering and Information Technology. 2016;16(3): 141-147.

Ren X, Zhang J, Song W, Cao S. The fundamental diagrams of elderly pedestrian flow in straight corridors under different densities. Journal of Statistical Mechanics: Theory and Experiment. 2019;02(34): 03-21.

Gates TJ, Noyce DA, Bill AR, Van Ee N. Recommended walking speeds for timing of pedestrian clearance intervals based on characteristics of the pedestrian population. Transportation Research Record Journal of the Transportation Research Board. 2006;1982(1): 38-47. doi: 10.3141/1982-07.

Zhang HL. Threshold research on the elderly pedestrian ratio in pedestrian crossing speed setting on signalized crosswalk in China. Promet – Traffic&Transportation. 2020;32(1): 55-63. doi: 10.7307/ptt.v32i1.3110.

Callisaya ML, et al. Cognitive status, fast walking speed and walking speed reserve—the Gait and Alzheimer Interactions Tracking (GAIT) study. Geroscience. 2017;39(6): 1-9. doi: 10.1007/s11357-017-9973-y.

Yang WY, Zhang X, Chen H, Jin WQ. A model of pedestrian trajectory prediction for autonomous vehicles based on social force. Journal of Highway and Transportation Research and Development. 2020;37(08): 127-135. doi: 10.3969/j.issn.1002-0268.2020.08.016.

Huber MJ. Estimation of passenger car equivalents of tracks in traffic stream. Transportation Research Record; 1982. p. 60-70.

Published
2022-03-31
How to Cite
1.
Zhang H, Xi B. Heterogeneous Pedestrian Traffic Flow Modelling at Signalised Intersections. Promet [Internet]. 2022Mar.31 [cited 2024Nov.21];34(2):165-77. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3829
Issue
Vol 34 No 2 (2022)
Section
Articles

Copyright (c) 2022 Huiling ZHANG, Bangshun XI

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