Article
DEFINING PEDESTRIAN LEVEL OF SERVICE AT SIGNALIZED INTERSECTIONS THROUGH MODELLED PARAMETERS AND SOM CLUSTERING
DOI: 10.7708/ijtte.2017.7(4).10
7 / 4 / 534-548 Pages
Author(s)
Prasanta Kumar Bhuyan - Deptartment of Civil Engineering, NIT Rourkela, India -
Abstract
Aim of this study is to define pedestrian levels of service (PLOS) categories (A-F) at signalized intersections under the influence of mixed traffic flow. Both quantitative and qualitative factors affecting the service measure have been identified and modelled using ridge regression method. Along with road geometric and traffic operational data, effective 675 pedestrian perceptions of real-time sense of satisfaction have been used in the model development. A PLOS score model has been developed taking perceived satisfaction score as the dependent variable and factors such as no of lanes, 85th percentile speed of vehicles, volume of through moving vehicles, left turning motorized and non-motorized vehicles, permissible right turning motorized and non-motorized vehicles and pedestrian delay as independent variables. Delays used in PLOS model were estimated by the combination of waiting time delay and vehicle interaction delay. Having R2 value of 0.986 PLOS model was well validated. PLOS scores of six categories were defined by applying Self-Organizing Map (SOM) in Artificial Neural Network (ANN) clustering. Analysis shows that about 75% of total crosswalks were offering average or above service levels, whereas only 4 % were contributing very poor kind of service to pedestrians in the study areas.
Number of downloads: 1704
References:
Ahhajyaseen, W.K.; Asano, M.; Nakamura, H. 2012. Estimation of left-turning vehicle maneuvers for the assessment of pedestrian safety at intersections, IATSS Research 36(1): 66–74. DOI:10.1016/j.iatssr.2012.03.002.
Al-Garni, S.; Abdennour, A. 2008. A neural network based traffic flow evaluation system for highways, Journal of King Saud University of Engineering Sciences 20(1): 37-46.
Bian, Y.; Ma. J.; Rong. J; Wang, W.; Lu, J. 2009. Pedestrians’ level of service at signalized intersections in China, Transportation Research Record: Journal of the Transportation Research Board 2114: 83-89. DOI: 10.3141/2114-10.
Braun, R.R.; Roddin, M.F. 1978. Quantifying the benefits of separating pedestrians and vehicles. NCHRP Report 189. Transportation Research Board. Washington, D.C. 127 p.
Cetiner, B.G.; Sari, M.; Borat, O. 2010. A neural network based traffic-flow model, Mathematical and Computational Applications15(2): 269-278. DOI:10.3390/mca15020269.
Chen, X.M.; Shao, C.F.; Hao, Y. 2008. Influence of Pedestrian Traffic on Capacity of Right-Turning Movements at Signalized Intersections, Transportation Research Record: Journal of the Transportation Research Board 2073: 114-124. DOI: 10.3141/2073-13
Furth, P.G.; Wang, Y.D. 2015. Delay Estimation and Signal Timing Design Techniques for Multi-Stage Pedestrian Crossings and Two-Stage Bicycle Left Turns. In Proceedings of the Transportation Research Board 94th Annual Meeting (No. 15-5365). 1-16.
Hagiwara, T.; Hamaoka, H.; Yaegashi, T.; Miki, K.; Ohshima, I.; Naito, M. 2008. Estimation of time lag between right-turning vehicles and pedestrians approaching from the right side, Transportation Research Record: Journal of the Transportation Research Board 2069: 65–76. DOI: 10.3141/2069-09.
HCM. 2010. Highway Capacity Manual. Transportation Research Board, Washington, D.C. Hubbard, S.M.; Bullock, D.M.; Mannering, F.L. 2009. Right turn on green and pedestrian level of servive: statistical assessment, Journal of Transportation Engineering 135(4): 153-159.
Hubbard, S.; Bullock, D.; Day, C. 2008. Integration of real-time pedestrian performance measures into existing infrastructure of traffic signal system, Transportation Research Record: Journal of the Transportation Research Board 2080: 37–47. DOI: 10.3141/2080-05.
Jain, A.; Gupta, A.; Rastogi, R. 2014. Pedestrian Crossing Behaviour Analysis at Intersections, International Journal for Traffic and Transport Engineering 4(1): 103-116. DOI: http://dx.doi.org/10.7708/ijtte.2014.4(1).08.
Jian-Ming, C. 2010. Traffic Prediction Based on Improved Neural Network, Journal of Convergence Information Technology 5(9): 85-89.
Khisty, C. J. 1994. Evaluation of pedestrian facilities: Beyond the level-of-service concept. Transportation Research Record, Transportation Research Board, Washington, D.C. 1438: 45–50.
Landis, B.; Petritsch, T.; McLeod, P.; Huang, H.; Guttenplan, M. 2005. Video Simulation of Pedestrian Crossings at Signalized Intersections, Transportation Research Record: Journal of the Transportation Research Board 1920: 49-55. DOI: 10.3141/1920-06.
Li, Q.; Wang, Z.; Yang, J; Wang, J. 2005. Pedestrian delay estimation at signalized intersections in developing cities, Transportation Research Part A: Policy and Practice 39(1): 61-73. DOI:10.1016/j.matcom.2005.01.017.
Lipovac, K.; Vujanic, M; Maric, B; Nesic, M. 2012. Pedestrian behavior at signalized pedestrian crossings, Journal of transportation engineering 139(2): 165-172. DOI: 10.1061/(ASCE)TE.1943-5436.0000491.
Mantecchini, L.; Paganelli, F. 2015. Emperical Analysis of Pedestrian Delay Models at Urban Intersections, Contemporary Engineering Science 8(21): 981-990. http://dx.doi.org/10.12988/ces.2015.57202.
Marisamynathan, S.; Vedagiri, P. 2013. Modeling pedestrian delay at signalized intersection crosswalks under mixed traffic condition, Procedia-Social and Behavioral Sciences 104: 708-717. doi: 10.1016/j.sbspro.2013.11.165.
Muraleetharan, T.; Adachi, T.; Hagiwara, T.; Kagaya, S. 2005. Method to Determine Pedestrian Level-of-Service for Crosswalk at Urban Intersections, Journal of the Eastern Asia Society for Transportation Studies 6: 127-136. DOI:10.4028/www.scientific.net/AMM.253-255.1936.
Nagraj, R.; Vedagiri, P. 2013. Modeling Pedestrian Delay and Level of Service at Signalized Intersection Crosswalks under Mixed Traffic Conditions, Transportation Research Record: Journal of the Transportation Research Board 2394: 70-76. DOI: 10.3141/2394-09.
NCHRP. 2008. NCHRP Report 616: Multimodal Level of Service Analysis for Urban Streets. Transportation Research Board, National Research Council, Washington, D.C. 60 p.
Pulugurtha, S.; Repaka, S. 2008. Assessment of Models to Measure Pedestrian Activity at Signalized Intersections, Transportation Transportation Research Record: Journal of the Transportation Research Board 2073: 39-48. DOI: 10.3141/2073-05.
Sarkar, S. 1993. Determination of service levels for pedestrians, with European examples, Transportation Research Record 1405: 35–42.
Soh, A.; Marhaban, M.; Khalid, M.; Yusof, R. 2007. Modelling and Optimization of a Traffic Intersection Based on Queue Theory and Markov Decision Control Methods. In Proceedings of the First Asia International Conference on Modelling & Simulation (AMS'07). 478-483. DOI: 10.1109/AMS.2007.64
Tiwari, G.; Bangdiwala, S.; Saraswat, A.; Gaurav, S. 2007. Survival analysis: pedestrian risk exposure at signalized intersections, Transportation research part F: traffic psychology and behaviour, 10(2): 77-89.
Wang, X.; Tian, Z. 2010. Pedestrian Delay at Signalized Intersections with a Two-Stage Crossing Design, Transportation Research Record: Journal of the Transportation Research Board 2173: 133-138. DOI: 10.3141/2173-16.
Yang, H.; Qiao, F. 1998. Neural network approach to classification of traffic flow states, Journal of Transportation Engineering 124(6): 521-525.
Yang, J.; Li, Q.; Wang, Z.; Wang, J. 2005. Estimating pedestrian delays at signalized intersections in developing cities by Monte Carlo method, Mathematics and Computers in Simulation 68(4): 329-337.
Zeng, W.; Nakamura, H.; Chen, P. 2014. A Modified Social Force Model for Pedestrian Behavior Simulation at Signalized Crosswalks, Procedia - Social and Behavioral Sciences 138: 521-530. DOI: 10.1016/j.sbspro.2014.07.233.
Quoted IJTTE Works
Related Keywords