Personalized real-time traffic information provision: Agent-based optimization model and solution framework

Transportation Research Part C: Emerging Technologies

Volumn 64, Issue , 2016, Pages 164-182

  Ma, Jiaqi ^a   Smith, Brian L ^b   Zhou, Xuesong ^c  

^a FREDERICK NATIONAL LABORATORY FOR CANCER RESEARCH   (United States)

^b University of Virginia   (United States)

^c Arizona State University   (United States)

Author keywords

Agent based modeling; Dynamic traffic management; Network modeling; Traveler information provision

Indexed keywords

AUTONOMOUS AGENTS; COMPUTATIONAL METHODS; INTEGER PROGRAMMING; TRAFFIC CONTROL; VEHICLE ACTUATED SIGNALS;

AGENT-BASED MODEL; AGENT-BASED OPTIMIZATION; DYNAMIC TRAFFIC MANAGEMENT; INFORMATION AND COMMUNICATION TECHNOLOGIES; INFORMATION PROVISION; INTEGER PROGRAMMING MODELS; NETWORK MODELING; REAL-TIME TRAFFIC INFORMATION;

INFORMATION MANAGEMENT;

ADVANCED TRAVELER INFORMATION SYSTEM; INFORMATION AND COMMUNICATION TECHNOLOGY; LAGRANGIAN ANALYSIS; MODELING; OPTIMIZATION; REAL TIME; TRAFFIC CONGESTION; TRAFFIC MANAGEMENT;

EID: 84960814687     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2015.03.004     Document Type: Article

References (44)

1. Abbas, M., McCoy, P., 1999. Optimizing variable message sign locations on freeways using genetic algorithms. In: 78th Annual Meeting of the Transportation Research Board, Washington, DC.
2. Adler J.L., Blue V.J. A cooperative multi-agent transportation management and route guidance system. Transport. Res. Part C: Emerg. Technol. 2002, 10(5):433-454.
3. Bolelli, A., Mauro, V., Perono, E., 1991. Models and strategies for dynamic route guidance. Part B, a decentralized, fully dynamic, infrastructure supported route guidance. In: DRIVE Conference: Advanced Telematics in Road Transport, Brussels, Belgium.
4. Carey M., Subrahmanian E. An approach to modelling time-varying flows on congested networks. Transport. Res. B-Meth 2000, 34(3):157-183.
5. Charbonnier, C., Farges, J.L., Henry, J.J., 1991. Models and strategies for dynamic route guidance - Part C: optimal control approach. In: Proceedings of the DRIVE Conference, Brussels, Belgium, pp. 106-112.
6. Chen X.M., Zhang L., He X., Xiong C., Li Z. Surrogate-Based Optimization of Expensive-to-Evaluate Objective for Optimal Highway Toll Charges in Transportation Network. Comput-Aided. Civ. Inf 2014, 29(5):359-381.
7. Chiu Y.C., Huynh N. Location configuration design for Dynamic Message Signs under stochastic incident and ATIS scenarios. Transport. Res. Part C: Emerg. Technol. 2007, 15(1):33-50.
8. Daganzo C.F. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transport. Res. Part B 1994, 28(4):269-287.
9. Daganzo C.F. The cell transmission model, Part II: Network traffic. Transport. Res. Part B 1995, 29(2):79-93.
10. Davidsson P., Persson J.A., Holmgren J. On the integration of agent-based and mathematical optimization techniques. Agent and Multi-agent Systems: Technologies and Applications 2007, 1-10. Springer, Berlin, Heidelberg.
11. Dia H. An agent-based approach to modelling driver route choice behaviour under the influence of real-time information. Transport. Res. Part C: Emerg. Technol. 2002, 10(5):331-349.
12. Gaffuri, J., 2006. How to merge optimization and agent-based techniques in a single generalization model. In: Workshop on Generalisation and Multiple Representation, Vancouver, United States.
13. Ghali M.O., Smith M.J. A model for the dynamic system optimum traffic assignment problem. Transport. Res. Part B: Method. 1995, 29(3):155-170.
14. Gjerdrum J., Shah N., Papageorgiou L.G. A combined optimization and agent-based approach to supply chain modelling and performance assessment. Prod. Plann. Control 2001, 12(1):81-88.
15. Hawas Y.E. A cooperative distributed system for real-time route guidance. J. Transport. Technol. 2012, 2(03):230.
16. Hawas, Y., Mahmassani, H.S., 1995. A decentralized scheme for real-time route guidance in vehicular traffic networks. In: Steps Forward. Intelligent Transport Systems World Congress (No. vol. 4).
17. Huynh N., Chiu Y.C., Mahmassani H.S. Finding near-optimal locations for variable message signs for real-time network traffic management. Transport. Res. Rec.: J. Transport. Res. Board 2003, 1856(1):34-53.
18. Jahn O., Möhring R.H., Schulz A.S., Stier-Moses N.E. System-optimal routing of traffic flows with user constraints in networks with congestion. Oper. Res. 2005, 53(4):600-616.
19. Jayakrishnan R., Mahmassani H.S., Hu T.Y. An evaluation tool for advanced traffic information and management systems in urban networks. Transport. Res. Part C: Emerg. Technol. 1994, 2(3):129-147.
20. Jia A., Zhou X., Li M., Rouphail N.M., Williams B.M. Incorporating stochastic road capacity into day-to-day traffic simulation and traveler learning framework. Transport. Res. Rec.: J. Transport. Res. Board 2011, 2254(1):112-121.
21. Kittelson & Associates, Inc., 2010. Understanding the Contributions of Operations, Technology, and Design to Meeting Highway Capacity Needs. Transportation Research Board, Washington, DC. .
22. Li, M., Zhou, X., Rouphail, N.M., 2011. Quantifying benefits of traffic information provision under stochastic demand and capacity conditions: a multi-day traffic equilibrium approach. In: IEEE Intelligent Transportation Systems Conference (ITSC), Washington, DC, 2011.
23. Lu C.C., Mahmassani H.S., Zhou X. A bi-criterion dynamic user equilibrium traffic assignment model and solution algorithm for evaluating dynamic road pricing strategies. Transport. Res. Part C: Emerg. Technol. 2008, 16(4):371-389.
24. Lu C.C., Mahmassani H.S., Zhou X. Equivalent gap function-based reformulation and solution algorithm for the dynamic user equilibrium problem. Transport. Res. Part B: Method. 2009, 43(3):345-364.
25. Lu C.C., Zhou X., Zhang K. Dynamic origin-destination demand flow estimation under congested traffic conditions. Transport. Res. Part C: Emerg. Technol. 2013, 34:16-37.
26. Mahmassani H.S., Jayakrishnan R. System performance and user response under real-time information in a congested traffic corridor. Transport. Res. Part A 1999, 25(5):293-308.
27. Newell G.F. A simplified theory on kinematic waves in highway traffic, Part I: General theory. Transport. Res. Part B 1993, 27(4):281-287.
28. Paz A., Peeta S. Behavior-consistent real-time traffic routing under information provision. Transport. Res. Part C: Emerg. Technol. 2009, 17(6):642-661.
29. Peeta S., Gedela S. Real-time variable message sign-based route guidance consistent with driver behavior. Transport. Res. Rec.: J. Transport. Res. Board 2001, 1752(1):117-125.
30. Peeta S., Mahmassani H.S. Multiple user classes real-time traffic assignment for on-line operations: a rolling horizon solution framework. Transport. Res. 1995, 3C(2):83-98.
31. Peeta S., Ramos J.L., Pasupathy R. Content of variable message signs and on-line driver behavior. Transport. Res. Rec.: J. Transport. Res. Board 2000, 1725(1):102-108.
32. Persson, J.A., Davidsson, P., Johansson, S.J., Wernstedt, F., 2005. Combining Agent-Based Approaches and Classical Optimization Techniques. EUMAS, pp. 260-269.
33. Sawaya O.B., Doan D.L., Ziliaskopoulos A.K., Fourer R. Multistage stochastic system optimum dynamic traffic assignment program with recourse for incident traffic management. Transport. Res. Rec.: J. Transport. Res. Board 2001, 1748(1):116-124.
34. Vickrey W. Pricing in urban and suburban transport. Am. Econ. Rev.: Pap. Proc. 1963, 53(2):452-465.
35. Williams, C.A., 2010. A Data Mining Approach to Rapidly Learning Traveler Activity Patterns for Mobile Applications. University of Illinois at Chicago.
36. Xu T.D., Hao Y., Peng Z.R., Sun L.J. Real-time travel time predictor for route guidance consistent with driver behavior. Can. J. Civ. Eng. 2012, 39(10):1113-1124.
37. Yang L., Zhou X. Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem. Transport. Res. Part B: Method. 2014, 59:22-44.
38. Zhang L., Levinson D. Agent-based approach to travel demand modeling: exploratory analysis. Transport. Res. Rec.: J. Transport. Res. Board 2004, 1898(1):28-36.
39. Zhang L., Zhu S., Levinson D. Agent-based modeling of price competition, capacity choice, and product differentiation in congested networks. J. Transp. Econ. Policy 2008, 42(3):435-461.
40. Zhou X., Taylor J. DTALite: a queue-based mesoscopic traffic simulator for fast model evaluation and calibration. Cogent Eng. 2014, 1(1):1-19.
41. Zhou X., Mahmassani H.S., Zhang K. Dynamic micro-assignment modeling approach for integrated multimodal urban corridor management. Transport. Res. Part C: Emerg. Technol. 2008, 16(2):167-186.
42. Zhu, S., Levinson, D.M., Zhang, L., 2008. Agent-based route choice with learning and exchange of information. In: Transportation Research Board 87th Annual Meeting (No. 08-2152).
43. Ziliaskopoulos A.K. A linear programming model for the single destination system optimum dynamic traffic assignment problem. Transport. Sci. 2000, 34(1):37-49.
44. Ziliaskopoulos, A., Mahmassani, H.S., 1992. Design and implementation of a shortest path algorithm with time-dependent arc costs. In: Proceedings of the 5th Advanced Technology Conference, Washington, DC, pp. 221-242.