Modelling pedestrian level of service on sidewalks with multi-factors based on different pedestrian flow rates
Abstract
Pedestrian Level of Service (PLOS) is influenced by the factors of traffic conditions, road facility conditions and environmental conditions. Pedestrian flow rate was the key factor influencing PLOS for the reason that pedestrians’ visual scopes of pavement and the influencing degree of each influencing factor on sidewalks was differed under different pedestrian flow rates. In order to evaluate PLOS more accurately, this paper classified pedestrian flow rates into 6 stages. Then, significant influencing factors of traffic conditions, road facility conditions and environmental conditions, which influenced pedestrians’ satisfaction, were extracted respectively under each pedestrian flow rate by Spearman rank correlation method. Finally, the evaluation method of PLOS with multi-factors based on classification of pedestrian flow rates was put forward. In addition, the models got training with fuzzy neural network method. The test showed that the accuracy of the comprehensive evaluation model of PLOS under different pedestrian flow rates based on fuzzy neural network reaches to 92%, which is much higher than the model accuracy of previous researches.
First published online 20 January 2022
Keyword : pedestrian level of service (PLOS), pedestrians’ behaviour, pedestrians’ satisfaction, pedestrian flow, evaluation model with multi-factors, fuzzy neural network
How to Cite
Shu, S., Bian, Y., Zhao, L., Rong, J., & Liu, X. (2021). Modelling pedestrian level of service on sidewalks with multi-factors based on different pedestrian flow rates. Transport, 36(6), 486-498. https://doi.org/10.3846/transport.2021.16276
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kim, S.; Choi, J.; Kim, S.; Tay, R. 2014. Personal space, evasive movement and pedestrian level of service, Journal of Advanced Transportation 48(6): 673–684. https://doi.org/10.1002/atr.1223
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Roper, J. A. M.; Hassan, S. E. 2014. How do vision and hearing impact pedestrian time-to-arrival judgments?, Optometry and Vision Science 91(3): 303–311. https://doi.org/10.1097/OPX.0000000000000161
Sahani, R.; Bhuyan, P. K. 2017. Pedestrian level of service criteria for urban off-street facilities in mid-sized cities, Transport 32(2): 221–232. https://doi.org/10.3846/16484142.2014.944210
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Sieben, A.; Schumann, J.; Seyfried, A. 2017. Collective phenomena in crowds – where pedestrian dynamics need social psychology, Plos One 12(6): e0177328. https://doi.org/10.1371/journal.pone.0177328
Steiner, R. L.; Landis, B. W.; Miller, D. W. 2002. Refinement of the Roadway Segment Level of Service. Florida Department of Transportation (FDOT), Tallahassee, FL, US. 15 p. Available from Internet: https://www.fsutmsonline.net/images/uploads/reports/fdot_bc354_45_rpt.pdf
Tang, M. 2010. Research on Pedestrian Traffic Behavior Model and Simulation Algorithm in Passenger Transfer Hub. PhD Thesis. Jilin University, Jilin, China. (in Chinese).
TRB. 2010. Highway Capacity Manual. Transportation Research Board (TRB), Washington, DC, US. 1650 p.
Wahba, M. A.; Bridwell, L. G. 1976. Maslow reconsidered: a review of research on the need hierarchy theory, Organizational Behavior and Human Performance 15(2): 212–240. https://doi.org/10.1016/0030-5073(76)90038-6
Zhang, Z.; Jia, L.; Qin, Y. 2016. Level-of-service based hierarchical feedback control method of network-wide pedestrian flow, Mathematical Problems in Engineering 2016: 9617890. https://doi.org/10.1155/2016/9617890
Chen, S.-H.; Wu, C.-C.; Li, P.-Y.; Paramitha, P. A. 2017. Evaluation of pedestrian transportation facilities in Taiwan using linear regression and support vector regression, Road Materials and Pavement Design 18(Sup 3): 170–179. https://doi.org/10.1080/14680629.2017.1329872
Cheng, F. H. 2012. A Study on Air Quality Assessment Applied BP Neural Network and Fuzzy Neural Networks. MSc Thesis. Guangdong University of Technology, Guangzhou, China. 97 p. (in Chinese).
CHTS. 1998. Traffic Engineering Manual. China Highway and Transportation Society (CHTS). China Communications Press, Beijing, China. (in Chinese).
Dixon, L. B. 1996. Bicycle and pedestrian level-of-service performance measures and standards for congestion management systems, Transportation Research Record: Journal of the Transportation Research Board 1538: 1–9. https://doi.org/10.1177/0361198196153800101
Dowling, R.; Flannery, A.; Landis, B.; Petritsch, T.; Rouphail, N.; Ryus, P. 2008. Multimodal level of service for urban streets, Transportation Research Record: Journal of the Transportation Research Board 2071: 1–7. https://doi.org/10.3141/2071-01
Fruin, J. J. 1971. Pedestrian Planning and Design. Metropolitan Association of Urban Designers and Environmental Planners. 206 p.
Fu, D.; Feng, W.-D.; Yu, Q.-F.; Xu, Y.-D. 2008. Linear self-calibration method for camera, Opto-Electronic Engineering 35(1): 71–75. (in Chinese).
Guo, H.; Zhao, F.; Wang, W.; Zhou, Y.; Zhang, Y.; Wets, G. 2014. Modeling the perceptions and preferences of pedestrians on crossing facilities, Discrete Dynamics in Nature and Society 2014: 949475. https://doi.org/10.1155/2014/949475
Kadali, B. R.; Vedagiri, P. 2015. Evaluation of pedestrian cross-walk level of service (LOS) in perspective of type of landuse, Transportation Research Part A: Policy and Practice 73: 113–124. https://doi.org/10.1016/j.tra.2015.01.009
Kang, L.; Xiong, Y.; Mannering, F. L. 2013. Statistical analysis of pedestrian perceptions of sidewalk level of service in the presence of bicycles, Transportation Research Part A: Policy and Practice 53: 10–21. https://doi.org/10.1016/j.tra.2013.05.002
Khisty, C. J. 1994. Evaluation of pedestrian facilities: beyond the level-of-service concept, Transportation Research Record: Journal of the Transportation Research Board 1438: 45–50.
Kim, S.; Choi, J.; Kim, S.; Tay, R. 2014. Personal space, evasive movement and pedestrian level of service, Journal of Advanced Transportation 48(6): 673–684. https://doi.org/10.1002/atr.1223
Kim, S.; Choi, J.; Kim, Y. 2011. Determining the sidewalk pavement width by using pedestrian discomfort levels and movement characteristics, KSCE Journal of Civil Engineering 15(5): 883–889. https://doi.org/10.1007/s12205-011-1173-1
Landis, B. W.; Petritsch, T. A.; McLeod, P. S.; Huang, H. F.; Guttenplan, M. 2005. Video simulation of pedestrian crossings at signalized intersections, Transportation Research Record: Journal of the Transportation Research Board 1920: 49–55. https://doi.org/10.1177/0361198105192000106
Landis, B. W.; Vattikuti, V. R.; Ottenberg, R. M.; McLeod, D. S.; Guttenplan, M. 2001. Modeling the roadside walking environment: pedestrian level of service, Transportation Research Record: Journal of the Transportation Research Board 1773: 82–88. https://doi.org/10.3141/1773-10
Li, X.; Guo, F.; Kuang, H.; Zhou, H. 2017. Effect of psychological tension on pedestrian counter flow via an extended cost potential field cellular automaton model, Physica A: Statistical Mechanics and its Applications 487: 47–57. https://doi.org/10.1016/j.physa.2017.05.070
Liang, K. 2006. Study on Fuzzy Evaluation Mode of Levels of Service for Sidewalks. MSc Thesis. Huazhong University of Science and Technology, Wuhan, Hubei, China. (in Chinese).
Muraleetharan, T.; Hagiwara, T. 2007. Overall level of service of urban walking environment and its influence on pedestrian route choice behavior: analysis of pedestrian travel in Sapporo, Japan, Transportation Research Record: Journal of the Transportation Research Board 2002: 7–17. https://doi.org/10.3141/2002-02
Myers, J. L.; Well, A. D. 2003. Research Design and Statistical Analysis. Psychology Press. 736 p. https://doi.org/10.4324/9781410607034
Petritsch, T. A.; Landis, B. W.; Huang, H. F.; Dowling, R. 2008. Pedestrian level-of-service model for arterials, Transportation Research Record: Journal of the Transportation Research Board 2073: 58–68. https://doi.org/10.3141/2073-07
Petritsch, T., A.; Landis, B. W.; McLeod, P. S.; Huang, H. F.; Challa, S.; Skaggs, C. L.; Guttenplan, M.; Vattikuti, V. 2006. Pedestrian level-of-service model for urban arterial facilities with sidewalks, Transportation Research Record: Journal of the Transportation Research Board 1982: 84–89. https://doi.org/10.1177/0361198106198200111
Roper, J. A. M.; Hassan, S. E. 2014. How do vision and hearing impact pedestrian time-to-arrival judgments?, Optometry and Vision Science 91(3): 303–311. https://doi.org/10.1097/OPX.0000000000000161
Sahani, R.; Bhuyan, P. K. 2017. Pedestrian level of service criteria for urban off-street facilities in mid-sized cities, Transport 32(2): 221–232. https://doi.org/10.3846/16484142.2014.944210
Shan, X.; Ye, J.; Chen, X. 2016. Proposing a revised pedestrian walkway level of service based on characteristics of pedestrian interactive behaviours in China, Promet – Traffic & Transportation 28(6): 583–591. https://doi.org/10.7307/ptt.v28i6.1947
Sieben, A.; Schumann, J.; Seyfried, A. 2017. Collective phenomena in crowds – where pedestrian dynamics need social psychology, Plos One 12(6): e0177328. https://doi.org/10.1371/journal.pone.0177328
Steiner, R. L.; Landis, B. W.; Miller, D. W. 2002. Refinement of the Roadway Segment Level of Service. Florida Department of Transportation (FDOT), Tallahassee, FL, US. 15 p. Available from Internet: https://www.fsutmsonline.net/images/uploads/reports/fdot_bc354_45_rpt.pdf
Tang, M. 2010. Research on Pedestrian Traffic Behavior Model and Simulation Algorithm in Passenger Transfer Hub. PhD Thesis. Jilin University, Jilin, China. (in Chinese).
TRB. 2010. Highway Capacity Manual. Transportation Research Board (TRB), Washington, DC, US. 1650 p.
Wahba, M. A.; Bridwell, L. G. 1976. Maslow reconsidered: a review of research on the need hierarchy theory, Organizational Behavior and Human Performance 15(2): 212–240. https://doi.org/10.1016/0030-5073(76)90038-6
Zhang, Z.; Jia, L.; Qin, Y. 2016. Level-of-service based hierarchical feedback control method of network-wide pedestrian flow, Mathematical Problems in Engineering 2016: 9617890. https://doi.org/10.1155/2016/9617890