Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters
Abstract
Reinforcement Learning (RL) has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.
References
Zhang G, Wang Y. Optimizing coordinated ramp metering: a preemptive hierarchical control approach. Comput-Aided Civ Inf. 2013;28(1):22-37. doi: 10.1111/j.1467-8667.2012.00764.x
Papageorgiou M, Kotsialos A. Freeway ramp metering: an overview. IEEE Trans Intell Transport Syst. 2002;3(4):271-281. doi: 10.1109/TITS.2002.806803
Masher DP, Ross DW, Wong PJ, Tuan PL, Zeidler HM, Petracek S. Guidelines for design and operation of ramp control systems. Stanford: Stanford Research Institute; 1975.
Papageorgiou M, Hadj-Salem H, Blosseville JM. ALINEA: A local feedback control law for on-ramp metering. Transport Res Rec, 1991;1320:58-64.
Hegyi A, De Schutter B, Hellendoorn H. Model predictive control for optimal coordination of ramp metering and variable speed limits. Transport Res C-Emer. 2005;13(3):185-209. doi: 10.1016/j.trc.2004.08.001
Papamichail I, Kotsialos A, Margonis I, Papageorgiou M. Coordinated ramp metering for freeway networks – a model-predictive hierarchical control approach. Transport Res C- Emer. 2010;18(3):311-331. doi: 10.1016/j.trc.2008.11.002
Jacob C, Abdulhai B. Machine learning for multi-jurisdictional optimal traffic corridor control. Transport Res A-Pol. 2010;44(2):53-64. doi: 10.1016/j.tra.2009.11.001
Jacob C, Abdulhai B. Automated adaptive traffic corridor control using reinforcement learning: approach and case studies. Transport Res Rec. 2006;1959:1-8. doi: 10.3141/1959-01
Veljanovska K, Bombol K, Maher T. Reinforcement learning technique in multiple motorway access control strategy design. Promet – Traffic & Transportation. 2010;22(2):117-123. doi: 10.7307/ptt.v22i2.170
Rezaee K, Abdulhai B, Abdelgawad H. Application of reinforcement learning with continuous state space to ramp metering in real-world conditions. Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems. 2012 Sept 16-19; Anchorage, USA. IEEE; 2012.
Lu C, Chen H, Grant-Muller S. An indirect reinforcement learning approach for ramp control under incident-induced congestion. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems. 2013 Oct 6-9; The Hague, the Netherlands. IEEE; 2013.
Lu C, Chen H, Grant-Muller S. Indirect reinforcement learning for incident-responsive ramp control. Procedia Soc Behav Sci. 2014;111:1112-1122. doi: 10.1016/j.sbspro.2014.01.146
Rezaee K, Abdulhai B, Abdelgawad H. Self-learning adaptive ramp metering: analysis of design parameters on a test case in Toronto, Canada. Transport Res Rec. 2013;2013:10-18. doi: 10.3141/2396-02
Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge: the MIT press; 1998.
[Watkins CCH, Dayan P. Q-learning. Mach Learn. 1992;8(3-4):279-292. doi: 10.1007/BF00992698
Even-Dar E, Mansour Y. Learning rates for Q-learning. J Mach Learn Res. 2004;5:1-25.
Sun X, Horowitz R. Set of new traffic-responsive ramp-metering algorithms and microscopic simulation results. Transport Res Rec. 2006;1959:9-18. doi: 10.3141/1959-02
Gomes G, Horowitz R. Optimal freeway ramp metering using the asymmetric cell transmission model. Transport Res C-Emer. 2006;14(4):244-262. doi: 10.1016/j.trc.2006.08.001
Haddad J, Ramezani M, Geroliminis N. Cooperative traffic control of a mixed network with two urban regions and a freeway. Transport Res B-Meth. 2013;54:17-36. doi: 10.1016/j.trb.2013.03.007
Gomes G, Horowitz R. A study of two onramp metering schemes for congested freeways. in, 2003. Proceedings of the 2003 American Control Conference. 2003 June 4-6; Denver, USA. IEEE; 2003.
Arnold ED. Ramp metering: a review of the literature. Virginia: Virginia Transportation Research Council; 1998.
Saltelli A. Sensitivity analysis: Could better methods be used. J Geophys Res-Atmos. 1999;104(D3):3789-3793. doi: 10.1029/1998JD100042
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
