Autonomous Decentralized Control of Traffic Signals that can Adapt to Changes in Traffic
DOI:
https://doi.org/10.17815/CD.2016.5Keywords:
Traffic signal control, decentralized control, virtual impulseAbstract
A major challenge for traffic signal control is adapting to unpredictable changes in traffic. To address this issue, we propose an autonomous decentralized control scheme for traffic signals that is based on physics. More specifically, “virtual impulses” given by red signals or preceding cars, which are defined in a similar manner as the impulses generally used in physics, are calculated at each traffic signal by using an optimal velocity model, and traffic signals are switched to reduce these virtual impulses. We performed simulations under various traffic conditions, and the results showed that the proposed control scheme works adaptively and resiliently in response to each set of circumstances. Thus, the virtual impulse can be a key physical quantity for designing adaptive traffic systems.
References
P.B. Hunt, D.L. Robertson, and R.D. Bretherton: The SCOOT On-line Traffic Signal Optimization Technique. Traffic Eng. Control 23, 190–192 (1982).
A.G. Sime and K. W. Dobinson: The Sydney Coordinated Adaptive Traffic (SCAT) System Philosophy and Benefits. IEEE Trans. Vehicul. Tech. VT-29, 130–137 (1980). doi:10.1109/T-VT.1980.23833
M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotstalos, and Y. Wang: Review of Road Traffic Control Strategies. Proc. of the IEEE 91, 2043–2067 (2003). doi:10.1109/JPROC.2003.819610
S. P. Shepherd: A Review of Traffic Signal Control, Working Paper 349, Institute of Transport Studies, University of Leeds, Leeds, UK (1992).
J.J. Sanchez-Medina, M.J. Galan-Moreno, and E. Rubio-Royo: Traffic Signal Optimization in “La Almozara” District in Saragossa under Congestion Conditions, Using Genetic Algorithms, Traffic Microsimulation, and Cluster Computing. IEEE Trans. Intel. Transport. Sys. 11, 132–141 (2010). doi:10.1109/TITS.2009.2034383
S. M. Rahman and N. T. Ratrout: Review of the Fuzzy Logic Based Approach in Traffic Signal Control: Prospects in Saudi Arabia. J. Trans. Sys. Eng. Info. Tech. 9, 58–70 (2009). doi:10.1016/S1570-6672(08)60080-X
H. Hong-Di, L. Wei-Zhen, and D. Li-Yun, An Improved Cellular Automaton Model Considering the Effect of Traffic Lights and Driving Behaviour, Chin. Phys. B, 20, 040514 (2011). doi:10.1088/1674-1056/20/4/040514
M. Sasaki and T. Nagatani, Transition and Saturation of Traffic Flow Controlled by Traffic Lights, Physica A 325 (2003) 531–546 (2003). doi:10.1016/S0378-4371(03)00148-1
D. Srinivasan, M.C. Choy, and R.L. Cheu: Neural Networks for Real-time Traffic Signal Control. IEEE Trans. Intel. Trans. Sys. 7 261–272 (2006). doi:10.1109/TITS.2006.874716
C. Gershenson, Self-organizing Traffic Lights, Complex Systems, 16, 29–53 (2005)
C. Gershenson and D.A. Rosenblueth, Self-organizing Traffic Lights at Multiple-street Intersections, Complexity, 17, 23–39 (2012). doi:10.1002/cplx.20392
C. Gershenson and D.A. Rosenblueth, Modeling Self-organizing Traffic Lights with Elementary Cellular Automata, arXiv:0907.1925 (2009)
C. Gershenson and D.A. Rosenblueth, Adaptive Self-organization vs Static Optimization – A Qualitative Comparison in Traffic Light Coordination, Kybernetes, 41, 386–403 (2012) doi:10.1108/03684921211229479
D. Zubillaga, G. Cruz, Luis D. Aguilar, J. Zapotecatl, N. Fernandez, J. Aguilar, D.A. Rosenblueth and C. Gershenson, Measuring the Complexity of Self-Organizing Traffic Lights, Entropy 16, 2384-2407 (2014). doi:10.3390/e16052384
J. de Gier, T.M. Garoni, and O. Rojas, Traffic Flow on Realistic Road Networks with Adaptive Traffic Lights, J. Stat. Mech.: Theory Exp. 2011, P04008 (2011). doi:10.1088/1742-5468/2011/04/P04008
S.B. Cools, C. Gershenson, B. D’ Hooghe, Self-organizing Traffic Lights: A Realistic Simulation, In: Advances in Applied Self-Organizing Systems (Part of the series Advanced Information and Knowledge Processing), (Ed.) M. Prokopenko (London: Springer) Chapter 3, 41–50. (2008). doi:10.1007/978-1-84628-982-8_3
A. Reztsov, On Self-Organising Traffic Lights Technology, Complexity (2015). doi:10.1002/cplx.21659
A. Reztsov, Self-Organising Traffic Lights (SOTL) as an Upper Bound Estimate, Complex Syst. 24, 175–183 (2015). doi:10.2139/ssrn.2467948
L. Zhang, T.M. Garoni, J. de Gier, A Comparative Study of Macroscopic Fundamental Diagrams of Arterial Road Networks Governed by Adaptive Traffic Signal Systems, Trans. Res. Part B, 49, 1–23 (2013). doi:10.1016/j.trb.2012.12.002
K. Sekiyama, J. Nakanishi, I. Takagawa, T. Higashi, and T. Fukuda: Self-Organizing Control of Urban Traffic Signal Network. In: IEEE Int. Conf. Sys. Man. Cybern. 4, 2481–2486 (2001). doi:10.1109/ICSMC.2001.972930
I. Nisikawa: Dynamics of Oscillator Network and Its Application to Offset Control of Traffic Signals. In: Proc. IEEE Int. Joint Conf. on Neural Networks 2, 1273–1277 (2004). doi:10.1109/IJCNN.2004.1380126
M. Sugi, H. Yuasa, J. Ota, and T. Arai: Autonomous Decentralized Control of Traffic Signals with Closed-Loop Constraints on Offsets, In: Proc. SICE Ann. Conf., 1954– 1960 (2003).
T. Ohira, K. Inoue, and Y. Takeshima: Adaptive Traffic Control Model with Neural Network Analogy. In: Proc. Int. Conf. Neural Info. Proc. Intel. Info. Sys. (ICONIP), 939–942 (1997).
H. Suzuki, J. Imura, and K. Aihara: Chaotic Ising-like Dynamics in Traffic Signals. Sci. Reports 3, 1127 (2013). doi:10.1038/srep01127
M.E. Fouladvand, M.R. Shaebani, Z. Sadjadi, Intelligent Controlling Simulation of Traffic Flow in a Small City Network, J. Phys. Soc. Japan, 73, 3209–3214 (2004). doi:10.1143/JPSJ.73.3209
M.E. Fouladvand, Z. Sadjadi, M.R. Shaebani, Optimised Traffic Flow at a Single Intersection: Traffic Responsive Signalisation, J. Phys. A: Math. Gen. 37 561–576 (2004). doi:10.1088/0305-4470/37/3/002
M.E. Fouladvand and M. Nematollahi, Optimization of Green-times at an Isolated Urban Crossroads, Eur. Phys. J. B 22 395–401 (2001). doi:10.1007/PL00011149
S. Lämmer and D. Helbing, Self-control of Traffic Lights and Vehicle Flows in Urban Road Networks, J. Stat. Mech.: Theory Exp. 2008, P04019 (2008). doi:10.1088/1742-5468/2008/04/P04019
S. Lämmer, H. Kori, K. Peters, and D. Helbing, Decentralised Control of Material for Traffic Flows in Networks Using Phase-synchronisation, Physica A, 363, 39–43 (2006). doi:10.1016/j.physa.2006.01.047
R. Kutadinata, W. Moase, C. Manzie, L. Zhang, and T. Garoni, Enhancing the Performance of Existing Urban Traffic Light Control through Extremum-seeking, Transport. Res. Part C: Emerg. Tech., 62, 1–20 (2016). doi:10.1016/j.trc.2015.10.016
M. Bando, K. Hasebe, A. Nakayama, A. Shibata, and Y. Sugiyama: Dynamical Model of Traffic Congestion and Numerical Simulation. Phys. Rev. E 51, 1035– 1042 (1995). doi:10.1103/PhysRevE.51.1035
M. Bando, K. Hasebe, K. Nakanishi, A. Nakayama, A. Shibata, and Y. Sugiyama: Phenomenological Study of Dynamical Model of Traffic Flow. Journal de Physique I, EDP Sciences, 5, 1389–1399, (1995). doi:10.1051/jp1:1995206
T. Ezaki, D. Yanagisawa, and K. Nishinari: Dynamics of Assembly Production Flow. Physica A 427, 62–74 (2015). doi:10.1016/j.physa.2015.02.005
M. Gerla and L. Kleinrock: Flow Control: A Comparative Survey. IEEE Trans. Comm., COM-28, 553–573 (1980). doi:10.1109/TCOM.1980.1094691
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2016 Takeshi Kano
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to Collective Dynamics agree to publish their articles under the Creative Commons Attribution 4.0 license.
This license allows:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Authors retain copyright of their work. They are permitted and encouraged to post items submitted to Collective Dynamics on personal or institutional websites and repositories, prior to and after publication (while providing the bibliographic details of that publication).
