Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Abstract

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

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Notes

  1. 1.

    U.S. Environmental Protection Agency (2012) Report to Congress on Black Carbon. Department of the Interior, Environment, and Related Agencies Appropriations Act. EPA-450/R-12-001.

  2. 2.

    Transport ScotlandÕs Instantaneous Emissions Software AIRE. 2011. http://www.sias.com/2013/AIRE.html.

  3. 3.

    Transport Scotland. AIRE (Analysis of Instantaneous Road Emissions) User Guidance In, Scotland, 2011.

References

  1. Arel L, Liu C, Urbanik T, Kohls AG (2010) Reinforcement learning-based multi-agent system for network traffic signal control. IET Intell Transp Syst 4 (2):128–135

    Article  Google Scholar 

  2. Balaji PG, German X, Srinivasan D (2010) Urban traffic signal control using reinforcement learning agents. IET Intell Transp Syst 4(3):177–188

    Article  Google Scholar 

  3. Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. Part I: background and development. Nat Comput 6(4):467–484

    Article  Google Scholar 

  4. Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev 53(3):464–501

    Article  Google Scholar 

  5. Cai C, Wong CK, Heydecker BG (2009) Adaptive traffic signal control using approximate dynamic programming. Transp Res Part C: Emerg Technol 17 (5):456–474

    Article  Google Scholar 

  6. Castro GB, Hirakawa AR, Martini JS (2017) Adaptive traffic signal control based on bio-neural network. Procedia Comput Sci 109:1182–1187

    Article  Google Scholar 

  7. Chang L, Hui W (2016) Traffic emission control based on emission pricing and signal timing. In: 2016 12th World congress on intelligent control and automation (WCICA). IEEE, pp 467–472

  8. Chang TH, Sun GY (2004) Modeling and optimization of an oversaturated signalized network. Transp Res B 38(8):687–707

    Article  Google Scholar 

  9. Chen H, Bai R, Ma J, Wang D (2012) Research on intersection signal timing model considering emissions effects. In; CICTP 2012. American Society of Civil Engineers, pp 1024–1034

  10. Christofa E, Skabardonis A (2011) Traffic signal optimization with application of transit signal priority to an isolated intersection. Transp Res Record: J Transp Res Board 2259:192–201

    Article  Google Scholar 

  11. Christofa E, Ampountolas K, Skabardonis A (2016) Arterial traffic signal optimization: a person-based approach. Transp Res C 66:27–47

    Article  Google Scholar 

  12. Feng Y, Head K, Khoshmagham S, Zamanipour M (2015) A real-time adaptive signal control in a connected vehicle environment. Transp Res C 55:460–473

    Article  Google Scholar 

  13. Friesz TL (2010) Dynamic optimization and differential games. Springer, New York

    Google Scholar 

  14. Gartner NH (1983) OPAC: a demand-responsive strategy for traffic signal control. Transp Res Record 906:75–81

    Google Scholar 

  15. Gkatzoflias D, Kouridis C, Ntziachristos L, Samaras Z (2006) COPERT 4 manual. European Environment Agency (EEA)

  16. Han K (2017) Framework for real-time traffic management with case studies. Transp Res Record: J Transp Res Board 2658:35–43

    Article  Google Scholar 

  17. Han K, Gayah VV (2015) Continuum signalized junction model for dynamic traffic networks: offset, spillback, and multiple signal phases. Transp Res B 77:213–239

    Article  Google Scholar 

  18. Han K, Gayah VV, Piccoli B, Friesz TL, Yao T (2014) On the continuum approximation of the on-and-off signal control on dynamic traffic networks. Transp Res B 61:73–97

    Article  Google Scholar 

  19. Han K, Sun Y, Liu H, Friesz TL, Yao T (2015) A bi-level model of dynamic traffic signal control with continuum approximation. Transp Res C 55:409–431

    Article  Google Scholar 

  20. Han K, Liu H, Gayah VV, Friesz TL, Yao T (2016) A robust optimization approach for dynamic traffic signal control with emission considerations. Transp Res C 70:3–26

    Article  Google Scholar 

  21. Hauser TA, Scherer WT (2001) Data mining tools for real-time traffic signal decision support. 2001 IEEE International Conference on & Maintenance, Systems Man, and Cybernetics 3:1471–1477

    Google Scholar 

  22. He Q, Head K, Ding J (2014) Multi-modal traffic signal control with priority, signal actuation and coordination. Transp Res C 46:65–82

    Article  Google Scholar 

  23. Henry JJ, Farges JL, Tuffal J (1983) The PRODYN real time traffic algorithm. In: Proceedings of the fourth IFAC-IFIP-IFORS conference on control in transportation systems, pp 307–311

  24. Hunt PB, Robertson DI, Bretherton RD (1982) The SCOOT on-line traffic signal optimization technique. Traffic Eng Control 25:14–22

    Google Scholar 

  25. Jamshidnejad A, Papamichail I, Papageorgiou M, De Schutter B (2017) Sustainable model-predictive control in urban traffic networks: efficient solution based on general smoothening methods. IEEE Trans Control Syst Technol 26(3):813–827

    Article  Google Scholar 

  26. Janssen NAH, Gerlofs-Nijland ME, Lanki T, Salonen RO, Cassee F, Hoek G, Fischer P, Brunekreef B, Krzyzanowski M (2013) Health effects of black carbon. In Europe WROf (ed)

  27. Ji Y, Hu B, Han J, Tang D (2014) An improved algebraic method for transit signal priority scheme and its impact on traffic emission. Mathematical Problems in Engineering, 11 pages

  28. Lefebvre W, Fierens F, Trimpeneers E, Janssen S, Van de Vel K, Deutsch F, Viaene P, Vankerkom J, Dumont G, Vanpoucke C, Mensink C, Peelaerts W, Vliegen J (2011) Modeling the effects of a speed limit reduction on traffic-related elemental carbon (EC) concentrations and population exposure to EC. Atmos Environ 45(1):197–207

  29. Li L, Lv Y, Wang F (2016) Traffic signal timing via deep reinforcement learning. IEEE/CAA J Automatica Sinica 3(3):247–254

    Article  Google Scholar 

  30. Lin S, De Schutter B, Xi Y, Hellendoorn H (2013) Integrated urban traffic control for the reduction of travel delays and emissions. IEEE Trans Intell Transp Syst 14(4):1609–1619

    Article  Google Scholar 

  31. Liu H, Han K, Gayah VV, Friesz TL, Yao T (2015) Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control. Transp Res C 59:260–277

    Article  Google Scholar 

  32. Lowrie P (1982) The Sydney coordinated adaptive traffic system-principles, methodology, algorithms. International Conference on Road Traffic Signalling

  33. Lucas D, Mirchandani P, Head K (2000) Remote simulation to evaluate real-time traffic control strategies. Transp Res Record: J Transp Res Board 1727:95–100

    Article  Google Scholar 

  34. Mascia M, Hu K, Han K, North R (2015) Simulation output for traffic scenarios for the city of Glasgow. Technical report, CARBOTRAF, D3.4

  35. Mascia M, Hu K, Han K, North R, Van Poppel M, Theunis J, Beckx C, Litzenberger M (2016) Impact of traffic management on black carbon emissions: a microsimulation study. Netw Spatial Econ 17(1):269–291

    Article  Google Scholar 

  36. Osorio C, Nanduri K (2015) Urban transportation emissions mitigation: coupling high-resolution vehicular emissions and traffic models for traffic signal optimization. Transp Res B Methodol 81:520–538

    Article  Google Scholar 

  37. Paramics S, Paramics S (2011) Reference manual. In: SIAS limited. Edinburg

  38. Papatzikou E, Stathopoulos A (2015) An optimization method for sustainable traffic control in urban areas. Transp Res C 55:179–190

    Article  Google Scholar 

  39. Samah E, Baher A, Hossam A (2013) Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): methodology and large-scale application on downtown Toronto. IEEE Trans Intell Transp Syst 14 (3):1140–1150

    Article  Google Scholar 

  40. Savsani V, Rao RV, Vakharia DP (2010) Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mech Mach Theory 45(3):531–541

    Article  Google Scholar 

  41. Sha DY, Hsu CY (2008) A new particle swarm optimization for the open shop scheduling problem. Comput Oper Res 35(10):3243–3261

    Article  Google Scholar 

  42. Smith S, Barlow G, Xie X-F, Rubinstein Z (2013) SURTRAC: scalable urban traffic control. Transportation Research Board 92nd Annual Meeting, Jan. 2013

  43. Sobrino N, Monzon A, Hernandez S (2016) Reduced carbon and energy footprint in highway operations: the Highway Energy Assessment (HERA) methodology. Netw Spatial Econ 16:395–414

    Article  Google Scholar 

  44. Srinivasan D, Choy MC, Cheu RL (2006) Neural networks for real-time traffic signal control. IEEE Trans Intell Transp Syst 7(3):261–272

    Article  Google Scholar 

  45. Stevanovic A, Stevanovic J, So J, Ostojic M (2015) Multi-criteria optimization of traffic signals: mobility, safety, and environment. Transp Res C 55:46–68

    Article  Google Scholar 

  46. Sun D, Benekohal RF, Waller ST (2006) Bi-level programming formulation and heuristic solution approach for dynamic traffic signal optimization. Comput-Aided Civil Infrastruct Eng 21(5):321–333

    Article  Google Scholar 

  47. Sundaram S, Kumar SS, Divya Shree MS (2015) Hierarchical clustering technique for traffic signal decision support. Int J Innov Sci 2(6):72–82

    Google Scholar 

  48. Sunkari S (2004) The benefits of retiming traffic signals. Institute Transp Eng ITE J 74(4):26

    Google Scholar 

  49. Ukkusuri SV, Ramadurai G, Patil G (2010) A robust transportation signal control problem accounting for traffic dynamics. Comput Oper Res 37(5):869–879

    Article  Google Scholar 

  50. Wiering MA (2000) Multi-agent reinforcement learning for traffic light control. In: Proceedings of the 17th international conference on machine learning, pp 1151–1158

  51. Yin PY (2006) Particle swarm optimization for point pattern matching. J Vis Commun Image Represent 17(1):143–162

    Article  Google Scholar 

  52. Yin Y (2008) Robust optimal traffic signal timing. Transp Res B 42(10):911–924

    Article  Google Scholar 

  53. Zhang L, Yin Y, Lou Y (2010) Robust signal timing for arterials under day-to-day demand variations. Transp Res Record: J Transp Res Board 2192:156–166

    Article  Google Scholar 

  54. Zhang K, Batterman S, Dion F (2011) Vehicle emissions in congestion: comparison of work zone, rush hour and free-flow conditions. Atmos Environ 45 (11):1929–1939

    Article  Google Scholar 

  55. Zhang L, Yin Y, Chen S (2013) Robust signal timing optimization with environmental concerns. Transp Res C 29:55–71

    Article  Google Scholar 

  56. Zhou Z, Cai M (2014) Intersection signal control multi-objective optimization based on genetic algorithm. J Traffic Transp Eng (English Edition) 1(2):153–158

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially supported by the EU 7th Framework Program project CARBOTRAF (28786), National Social Science Foundation of China (15BGL143), and the Zhejiang University/University of Illinois at Urbana-Champaign Institute.

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Correspondence to Ke Han.

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Appendix: : the PSO Algorithm

Appendix: : the PSO Algorithm

Given the objective function to be minimized, denoted f(⋅), and the feasible domain S, the following pseudo code summarizes the PSO procedure.

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Song, J., Hu, S., Han, K. et al. Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation. Netw Spat Econ (2020). https://doi.org/10.1007/s11067-020-09497-3

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Keywords

  • Real-time signal control
  • Nonlinear decision rule
  • Congestion
  • Emissions
  • Neural networks