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Backpressure based traffic signal control considering capacity of downstream links

    Shenxue Hao Affiliation
    ; Licai Yang Affiliation
    ; Yunfeng Shi Affiliation
    ; Yajuan Guo Affiliation

Abstract

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network.

Keyword : traffic control, queuing network, stability, traffic signal control, backpressure algorithm, penalty function

How to Cite
Hao, S., Yang, L., Shi, Y., & Guo, Y. (2020). Backpressure based traffic signal control considering capacity of downstream links. Transport, 35(4), 347-356. https://doi.org/10.3846/transport.2020.13288
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Sep 1, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abdelghaffar, H. M.; Yang, H.; Rakha, H. A. 2016. Isolated traffic signal control using a game theoretic framework, in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1–4 November 2016, Rio de Janeiro, Brazil, 1496–1501. https://doi.org/10.1109/ITSC.2016.7795755

Araghi, S.; Khosravi, A.; Creighton, D. 2015. A review on computational intelligence methods for controlling traffic signal timing, Expert Systems with Applications 42(3): 1538–1550. https://doi.org/10.1016/j.eswa.2014.09.003

Cesme, B.; Furth, P. G. 2014. Self-organizing traffic signals using secondary extension and dynamic coordination, Transportation Research Part C: Emerging Technologies 48: 1–15. https://doi.org/10.1016/j.trc.2014.08.006

Gregoire, J.; Frazzoli, E.; De La Fortelle, A.; Wongpiromsarn, T. 2014. Back-pressure traffic signal control with unknown routing rates, IFAC Proceedings Volumes 47(3): 11332–11337. https://doi.org/10.3182/20140824-6-ZA-1003.01585

Gregoire, J.; Qian, X.; Frazzoli, E.; De La Fortelle, A.; Wongpiromsarn, T. 2015. Capacity-aware backpressure traffic signal control, IEEE Transactions on Control of Network Systems 2(2): 164–173. https://doi.org/10.1109/TCNS.2014.2378871

Gregoire, J.; Samaranayake, S.; Frazzoli, E. 2016. Back-pressure traffic signal control with partial routing control, in 2016 IEEE 55th Conference on Decision and Control (CDC), 12–14 December 2016, Las Vegas, NV, US, 6753–6758. https://doi.org/10.1109/CDC.2016.7799309

Hunt, P. B.; Robertson, D. I.; Bretherton, R. D.; Winton, R. I. 1981. SCOOT – a Traffic Responsive Method of Coordinating Signals. Transport and Road Research Laboratory (TRRL) Report LR1014. Crowthorne, Berkshire, UK. 41 p. Available from Internet: https://trl.co.uk/reports/LR1014

Jiao, Z.; Zhang, B.; Li, C.; Mouftah, H. T. 2016. Backpressure-based routing and scheduling protocols for wireless multihop networks: a survey, IEEE Wireless Communications 23(1): 102–110. https://doi.org/10.1109/MWC.2016.7422412

Le, T.; Kovács, P.; Walton, N.; Vu, H. L.; Andrew, L. L. H.; Hoogendoorn, S. S. P. 2015. Decentralized signal control for urban road networks, Transportation Research Part C: Emerging Technologies 58: 431–450. https://doi.org/10.1016/j.trc.2014.11.009

Little, J. D. C.; Kelson, M. D.; Gartner, N. H. 1981. MAXBAND: a program for setting signals on arteries and triangular networks, Transportation Research Record 795: 40–46.

Lowrie, P. R. 1982. The Sydney coordinated adaptive traffic system – principles, methodology, algorithms, in International Conference on Road Traffic Signalling, 30 March – 1 April 1982, London, UK, 67–70.

McKenney, D.; White, T. 2013. Distributed and adaptive traffic signal control within a realistic traffic simulation, Engineering Applications of Artificial Intelligence 26(1): 574–583. https://doi.org/10.1016/j.engappai.2012.04.008

Neely, M. J. 2006. Energy optimal control for time-varying wireless networks, IEEE Transactions on Information Theory 52(7): 2915–2934. https://doi.org/10.1109/TIT.2006.876219

Neely, M. J. 2010. Stochastic network optimization with application to communication and queueing systems, Synthesis Lectures on Communication Networks 3(1): 1–211. https://doi.org/10.2200/S00271ED1V01Y201006CNT007

Núñez-Martínez, J.; Mangues-Bafalluy, J. 2012. Distributed Lyapunov drift-plus-penalty routing for WiFi mesh networks with adaptive penalty weight, in 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 25–28 June 2012, San Francisco, CA, US, 1–6. https://doi.org/10.1109/WoWMoM.2012.6263779

Núñez-Martínez, J.; Mangues-Bafalluy, J.; Portoles-Comeras, M. 2011. Studying practical any-to-any backpressure routing in Wi-Fi mesh networks from a Lyapunov optimization perspective, in 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems, 17–22 October 2011, Valencia, Spain, 771–776. https://doi.org/10.1109/MASS.2011.86

Robertson, D. I. 1969. TRANSYT: a Traffic Network Study Tool. Transport and Road Research Laboratory (TRRL) Report LR253. Crowthorne, Berkshire, UK. 37 p. Available from Internet: https://trl.co.uk/reports/LR253

Taale, H.; Van Kampen, J.; Hoogendoorn, S. 2015. Integrated signal control and route guidance based on back-pressure principles, Transportation Research Procedia 10: 226–235. https://doi.org/10.1016/j.trpro.2015.09.072

Tassiulas, L.; Ephremides, A. 1992. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks, IEEE Transactions on Automatic Control 37(12): 1936–1948. https://doi.org/10.1109/9.182479

Varaiya, P. 2013. Max pressure control of a network of signalized intersections, Transportation Research Part C: Emerging Technologies 36: 177–195. https://doi.org/10.1016/j.trc.2013.08.014

Wongpiromsarn, T.; Uthaicharoenpong, T.; Wang, Y.; Frazzoli, E.; Wang, D. 2012. Distributed traffic signal control for maximum network throughput, in 2012 15th International IEEE Conference on Intelligent Transportation Systems, 16–19 September 2012, Anchorage, AK, US, 588–595. https://doi.org/10.1109/ITSC.2012.6338817

Wu, Q.; Li, B.; Chen, K. 2014. A multi-agent traffic control model based on distributed system, Sensors & Transducers 173(6): 60–67.

Xiao, N.; Frazzoli, E.; Li, Y.; Wang, Y.; Wang, D. 2014. Pressure releasing policy in traffic signal control with finite queue capacities, in 53rd IEEE Conference on Decision and Control, 15–17 December 2014, Los Angeles, CA, US, 6492–6497. https://doi.org/10.1109/CDC.2014.7040407

Zaidi, A. A.; Kulcsár, B.; Wymeersch, H. 2016. Back-pressure traffic signal control with fixed and adaptive routing for urban vehicular networks, IEEE Transactions on Intelligent Transportation Systems 17(8): 2134–2143. https://doi.org/10.1109/TITS.2016.2521424