Modeling urban traffic control systems from the perspective of real time calculus

被引：0
|
作者：
Sun J.-H. [1 ]
Guan N. [1 ]
Deng Q.-X. [1 ]
Zhang X. [1 ]
Yang F.-Y. [1 ]
机构：
[1] School of Information Science and Engineering, Northeastern University, Shenyang
来源：
Ruan Jian Xue Bao/Journal of Software | 2016年 / 27卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Congestion factor; Real time calculus; Traffic signal control; Urban traffic network;
D O I：
10.13328/j.cnki.jos.004979
中图分类号：
学科分类号：
摘要：
This work presents a new framework for urban traffic flow control based on the real time calculus (RTC) method. The queuing behavior of the traffic flow is transformed into an arrival curve, and the capacity of the intersection is characterized by a service curve. According to different signal control strategies, the service and arrival curves at an intersection are used to calculate the outgoing arrival curve. This result curve at each intersection is further integrated with the curves at the adjacent intersections, which finally exhibits the RTC model of the whole traffic network. The presented model can evaluate the bounds of the delay D of a vehicle and the backlog B of an intersection. The experiments are settled on the urban girds, and reveal the changing trend of the congestion factors D and B that are under fixed-time and adapted control strategies respectively. This is followed by a discussion of how this modeling method helps to estimate the effect of different signal control strategies. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：527 / 546
页数：19
相关论文
共 30 条
  • [1] Li S.B., Wu J.J., Gao Z.Y., Lin Y., Fu B.B., The analysis of traffic congestion and dynamic propagation properties based on complex network, Acta Physica Sinica, 60, 5, (2011)
  • [2] Julvez J., Boel R.K., A continuous Petri net approach for model predictive control of traffic systems, IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, 40, 4, pp. 686-697, (2010)
  • [3] Kim Y.W., Kato T., Okuma S., Narikiyo T., Traffic network control based on hybrid dynamical system modeling and mixed integer nonlinear programming with convexity analysis, IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, 38, 2, pp. 346-357, (2008)
  • [4] Schadschneider A., Schreckenberg M., Cellular automation models and traffic flow, Journal of Physics A: Mathematical and General, 26, 15, pp. 679-683, (1993)
  • [5] Foulaadvand M.E., Belbasi S., Vehicular traffic flow at an intersection with the possibility of turning, Journal of Physics A: Mathematical and Theoretical, 44, 10, (2011)
  • [6] Hu W.B., Wang H., Du B., Li P.Y., A multi-intersection model and signal timing plan algorithm for urban traffic signal control, Proc. Of the Transport, pp. 1-11, (2014)
  • [7] Jing M., Deng W., Wang H., Ji Y.J., Two-Lane cellular automaton traffic model based on car following behavior, Acta Physica Sinica, 61, 24, pp. 323-331, (2012)
  • [8] Zhao H.T., Mao H.Y., Cellular automaton simulation of muti-lane traffic flow including emergency vehicle, Acta Physica Sinica, 62, 6, (2013)
  • [9] Zhang X.Q., Wang Y., Hu Q.H., Research and simulation on cellular automaton model of mixed traffic flow at intersection, Acta Physica Sinica, 63, 1, (2014)
  • [10] Gallego J.L., Farges J.L., Henry J.J., Design by Petri nets of an intersection signal controller, Transportation Research Part C: Emerging Technologies, 4, 4, pp. 231-248, (1996)
123