|Table of Contents|

Dynamic traffic signal control strategies considering traffic incidents(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年06期
Page:
182-190
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Dynamic traffic signal control strategies considering traffic incidents
Author(s):
YU Hao123 LIU Pan1 BAI Lu12 LU Xiao-bo2
(1. School of Transportation, Southeast University, Nanjing 210096, Jiangsu, China; 2. School of Automation, Southeast University, Nanjing 210096, Jiangsu, China; 3. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu 96822, Hawaii, USA)
Keywords:
traffic control double queue model traffic incident traffic simulation performance evaluation
PACS:
U491.51
DOI:
10.19818/j.cnki.1671-1637.2019.06.017
Abstract:
The numerical simulation approach was applied to evaluate the dynamic operation efficiency of road network under 3 typical traffic signal control strategies, including the fixed traffic signal control(FSC), the adaptive signal control minimizing vehicle delay(ASC-VD), and the adaptive signal control maximizing intersection capacity(ASC-IC). The dynamic traffic simulation platform was constructed by the double queue(DQ)model. An intersection traffic flow transfer optimization model was proposed, and the running state of traffic flow at the intersection in the DQ model was analysed. It was assumed that the users selected their routes according to the instantaneous user optimal(IUO)principle, and the IUO constraint was proposed considering the penalty term caused by the traffic signal control. The system total travel time and the travel time affected by traffic incidents or not were taken as evaluation indexes, the signal control effects under low, medium and high levels of traffic demands were studied. Analysis result shows that under the low and medium levels of traffic demand conditions, the system total travel time of ASC-VD is the lowest. Compared to the ASC-IC, the ASC-VD reduces the system total travel times by 0.45% and 0.18% without the influence of traffic incidents, respectively, and by 5.95% and 2.52% with the influence of traffic incidents, respectively. Under the high levels of traffic demand condition, the system total travel time of ASC-IC is the lowest. Compared to the ASC-IC, the ASC-VD reduces the system total travel time by 5.31% without the influence of traffic incidents, and by 5.46% with the influence of traffic incidents. Compared with the change range of system total travel time with or without the influence of traffic incidents, the FSC shows the highest stability under different traffic demands. Under the low and medium levels of traffic demand conditions, the ASC-VD performs more stable than the ASC-IC, while under the high level of traffic demand condition, the stabilities of the two strategies have no significant difference. Therefore, when the traffic demand is high, the intersection capacity should be improved, and when the traffic demand is low, the vehicle delay should be reduced. 4 tabs, 3 figs, 30 refs.

References:

[1] KHATTAK A J, WANG Xin, ZHANG Hong-bing, et al. Primary and secondary incident management: predicting durations in real time[R]. Charlottesville: Virginia Center for Transportation Innovation and Research, 2011.
[2] MARTIN P T, CHAUDHURI P, TASIC I, et al. Traffic incident management state of the art review[R]. Salt Lake City: University of Utah, 2011.
[3] KOOREY G, MCMILLAN S, NICHOLSON A. Incident
management and network performance[J]. Transportation Research Procedia, 2015, 6: 3-16.
[4] HOJATI A H, FERREIRA L, WASHINGTON S, et al. Modelling total duration of traffic incidents including incident detection and recovery time[J]. Accident Analysis and Prevention, 2014, 71: 296-305.
[5] YU Hao, LIU Pan, MA Rui, et al. Performance evaluation of integrated strategy of vehicle route guidance and traffic signal control using traffic simulation[J]. IET Intelligent Transport Systems, 2018, 12(7): 696-702.
[6] YAO Zhi-hong, ZHAO Bin, QIN Ling-qiao, et al. An efficient heterogeneous platoon dispersion model for real-time traffic signal control[J]. Physica A: Statistical Mechanics and its Applications, 2020, 539: 1-12.
[7] DING Chuan, WU Xin-kai, YU Gui-zhen, et al. A gradient boosting logit model to investigate driver's stop-or-run behavior at signalized intersections using high-resolution traffic data[J]. Transportation Research Part C: Emerging Technologies, 2016, 72: 225-238.
[8] YU Hao, MA Rui, ZHANG Hong-jun. Optimal traffic signal
control under dynamic user equilibrium and link constraints in a general network[J]. Transportation Research Part B: Methodological, 2018, 110: 302-325.
[9] CHEN Shu-kai, SUN Jian. An improved adaptive signal
control method for isolated signalized intersection based on dynamic programming[J]. IEEE Intelligent Transportation Systems Magazine, 2016, 8(4): 4-14.
[10] HUNTER M P, WU S K, KIM H K, et al. A probe-vehicle-based evaluation of adaptive traffic signal control[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 704-713.
[11] HU Jia, FONTAINE M D, PARK B B, et al. Field
evaluations of an adaptive traffic signal—using private-sector probe data[J]. Journal of Transportation Engineering, 2016, 142(1): 04015033-1-9.
[12] MAJID H, LU C, KARIM H. An integrated approach for dynamic traffic routing and ramp metering using sliding mode control[J]. Journal of Traffic and Transportation Engineering(English Edition), 2018, 5(2): 116-128.
[13] ASLANI M, MESGARI M S, WIERING M. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events[J]. Transportation Research Part C: Emerging Technologies, 2017, 85: 732-752.
[14] BALDI S, MICHAILIDIS I, NTAMPASI V, et al. A
simulation-based traffic signal control for congested urban traffic networks[J]. Transportation Science, 2019, 53(1): 6-20.
[15] NIE Xiao-jian, ZHANG Hong-jun. A comparative study of some macroscopic link models used in dynamic traffic assignment[J]. Networks and Spatial Economics, 2005, 5: 89-115.
[16] 李瑞敏.过饱和交叉口交通信号控制研究现状与展望[J].交通运输工程学报,2013,13(6):119-126.
LI Rui-min. Study status and prospect of traffic signal control for over-saturated intersection[J]. Journal of Traffic and Transportation Engineering, 2013, 13(6): 119-126.(in Chinese)
[17] ROBERTSON D I, BRETHERTON R D. Optimizing networks of traffic signals in real time—the SCOOT method[J]. IEEE Transactions on Vehicular Technology, 1991, 40(1): 11-15.
[18] LIU H X, OH J S, RECKER W. Adaptive signal control
system with on-line performance measure for single intersection[R]. Berkerly: University of California, 2002.
[19] REN Yi-long, WANG Yun-peng, YU Gui-zhen, et al. An adaptive signal control scheme to prevent intersection traffic blockage[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(6): 1519-1528.
[20] SOH A C, RAHMAN R Z A, RHUNG L G, et al. Traffic signal control based on adaptive neural fuzzy inference system applied to intersection[C]∥IEEE. 2nd International Conference on Open Systems. New York: IEEE, 2011: 237-242.
[21] SMITH M, HUANG Wei, VITI F. Equilibrium in capacitated network models with queueing delays, queue-storage, blocking back and control[J]. Procedia—Social and Behavioral Science, 2013, 80: 860-879.
[22] SMITH M. A local traffic control policy which automatically maximizes the overall travel capacity of an urban road network[J]. Traffic Engineering and Control, 1980, 21: 11-31.
[23] SMITH M. Traffic signal control and route choice: a new
assignment and control model which designs signal timings[J]. Transportation Research Part C: Emerging Technologies, 2015, 58: 451-473.
[24] LAMMER S, HELBING D. Self-control of traffic signal
lights and vehicle flows in urban road networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008(8): 1-34.
[25] MA Rui, BAN Xue-gang, PANG Jong-shi. Continuous-time dynamic system optimum for single destination traffic networks with queue spillbacks[J]. Transportation Research Part B: Methodological, 2014, 68: 98-122.
[26] MA Rui, BAN Xue-gang, PANG Jong-shi. A link-based differential complementarity system formulation for continuous-time dynamic user equilibria with queue spillbacks[J]. Transportation Science, 2018, 52(3): 564-592.
[27] RAN Bin, BOYCE D E, LEBLANC L J. A new class of instantaneous dynamic user-optimal traffic assignment models[J]. Operations Research, 1993, 41(1): 192-202.
[28] 赵 靖,马万经,韩 印.出口车道左转交叉口几何及信号组合优化模型[J].中国公路学报,2017,30(2):120-127.
ZHAO Jing, MA Wan-jing, HAN Yin. Integrated optimization model of layouts and signal timings of exit-lanes for left-turn intersections[J]. China Journal of Highway and Transport, 2017, 30(2): 120-127.(in Chinese)
[29] GAWRON C. An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model[J]. International Journal of Modern Physics C, 1998, 9(3): 393-407.
[30] CETIN N, BURRI A, NAGEL K. A large-scale agent-based traffic micro simulation based on queue model[C]∥STRC. Proceedings of 3rd Swiss Transport Research Conference. Ascona: STRC, 2003: 1-22.

Memo

Memo:
-
Last Update: 2020-01-13