How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Transportation Research Part C: Emerging Technologies

Volumn 67, Issue , 2016, Pages 266-279

  Guériau, Maxime ^a,d   Billot, Romain ^b   El Faouzi, Nour Eddin ^a,d   Monteil, Julien ^c   Armetta, Frédéric ^a   Hassas, Salima ^a  

^a UNIVERSITÉ DE LYON   (France)

^b ECOLE DES MINES DE NANTES   (France)

^c TRINITY COLLEGE DUBLIN   (Ireland)

^d UNIVERSITÉ GUSTAVE EIFFEL   (France)

Author keywords

C ITS; Cooperative vehicles; Data mining; Modeling; Simulation; Traffic management

Indexed keywords

ADVANCED DRIVER ASSISTANCE SYSTEMS; ADVANCED TRAFFIC MANAGEMENT SYSTEMS; AUTOMOBILE DRIVERS; DATA MINING; ENVIRONMENTAL TECHNOLOGY; HIGHWAY PLANNING; MODELS; MOTOR TRANSPORTATION; TRAFFIC CONTROL; VEHICLE ACTUATED SIGNALS; VEHICLE TO INFRASTRUCTURE; VEHICLE TO VEHICLE COMMUNICATIONS; VEHICLES;

COOPERATIVE VEHICLES; ENVIRONMENTAL-FRIENDLY; INFORMATION AND COMMUNICATION TECHNOLOGIES; INFORMATION EXCHANGES; SIMULATION; TRAFFIC MANAGEMENT; TRAFFIC MANAGEMENT STRATEGIES; TRAFFIC STATE ESTIMATION;

ADVANCED TRAVELER INFORMATION SYSTEMS;

COMPUTER SIMULATION; DATA MINING; INFORMATION AND COMMUNICATION TECHNOLOGY; STRATEGIC APPROACH; TRAFFIC MANAGEMENT;

EID: 84960107465     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2016.01.020     Document Type: Article

References (47)

1. Backfrieder, C., Mecklenbrauker, C., Ostermayer, G., 2013. Traffsim - a traffic simulator for investigating benefits ensuing from intelligent traffic management. In: 2013 European Modelling Symposium (EMS), pp. 451-456.
2. Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., Axhausen, K., 2009. Matsim-t: architecture and simulation times. Multi-Agent Systems for Traffic and Transportation Engineering, pp. 57-78.
3. Barceló J., Casas J. Dynamic network simulation with aimsun. Simulation Approaches in Transportation Analysis 2005, 57-98. Springer.
4. Castelfranchi, C., Falcone, R., 1988. Principles of trust for mas: cognitive anatomy, social importance, and quantification. In: Proceedings of the International Conference on Multi Agent Systems, pp. 72-79.
5. Choi J., Oh S., Horowitz R. Distributed learning and cooperative control for multi-agent systems. Automatica 2009, 45(12):2802-2814. doi: http://dx.doi.org/10.1016/j.automatica.2009.09.025.
6. C. . .C Communication Consortium, 2008.
7. Consortium, N., 2012. The harmonised research programme. <>, RoadMap D14. http://www.nearctis.org.
8. Dinh, T.-U.J., Billot, R., Pillet, E., Faouzi, E., et al., 2014. Real-time queue-end detection on freeways with fcd: practice: a practice-ready algorithm. In: Transportation Research Board 93rd Annual Meeting, No. 14-2725.
9. Espié S., Auberlet J.M., et al. Archisim: a behavioral multi-actors traffic simulation model for the study of a traffic system including its aspects. Int. J. ITS Res. 2007, (n1).
10. Fellendorf, M., 1994. Vissim: a microscopic simulation tool to evaluate actuated signal control including bus priority. In: 64th Institute of Transportation Engineers Annual Meeting, pp. 1-9.
11. Ferber J. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence 1999, vol. 1. Addison-Wesley, Reading.
12. Ge H.X., Dai S.Q., Dong L.Y. An extended car-following model based on intelligent transportation system application. Physica A: Stat. Mech. Appl. 2006, 365(2):543-548.
13. Guériau, M., Billot, R., El Faouzi, N.-E., Salima, H., Frédéric, A., 2015. Multi-agent dynamic coupling for cooperative vehicles modeling. In: The Twenty-Ninth Conference on Artificial Intelligence AAAI'2015 - (DEMO Track), Austin, Texas. <>. http://youtu.be/uv60iu5hfMw.
14. Helbing D., Balietti S. Social Self-Organization 2012, Springer, Chapter: Agent-Based Modeling.
15. HomChaudhuri B., Lin R., Pisu P. Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads. Transport. Res. Part C: Emerging Technol. 2016, 62:70-86.
16. Huynh T.D., Jennings N.R., Shadbolt N.R. An integrated trust and reputation model for open multi-agent systems. Autonom. Agents Multi-Agent Syst. 2006, 13(2):119-154.
17. Jin I.G., Orosz G. Dynamics of connected vehicle systems with delayed acceleration feedback. Transport. Res. Part C: Emerging Technol. 2014, 46:46-64.
18. Kesting A., Treiber M., Helbing D. General lane-changing model mobil for car-following models. Transport. Res. Rec.: J. Transport. Res. Board 2007, 86-94. vol. 1999/2007. Traffic Flow Theory 2007.
19. Kesting A., Treiber M., Helbing D. General lane-changing model mobil for car-following models. Transport. Res. Rec.: J. Transport. Res. Board 2007, 1999(1):86-94.
20. Kesting, A., Treiber, M., Helbing, D., 2009. Multi-Agent Systems: Simulation and Applications. Chapter: Agents for Traffic Simulation, pp. 325-356.
21. Kesting A., Treiber M., Helbing D. Connectivity statistics of store-and-forward intervehicle communication. IEEE Trans. Intell. Transport. Syst. 2010, 11(1):172-181.
22. Krajzewicz D., Erdmann J., Behrisch M., Bieker L. Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Measure. 2012, 5(3&4):128-138.
23. Lu M., Wevers K., Van Der Heijden R. Technical feasibility of advanced driver assistance systems (adas) for road traffic safety. Transport. Planning Technol. 2005, 28(3):167-187.
24. Martinez F.J., Toh C.K., Cano J.-C., Calafate C.T., Manzoni P. A survey and comparative study of simulators for vehicular ad hoc networks (vanets). Wireless Commun. Mobile Comput. 2011, 11(7):813-828.
25. Monteil J., Billot R., Sau J., Armetta F., Hassas S., El Faouzi N.-E. Cooperative highway traffic: multi-agent modeling and robustness assessment to local perturbations. Transport. Res. Rec.: J. Transport. Res. Board 2013, 2391(1):1-10.
26. Monteil, J., Billot, R., Armetta, F., Hassas, S., El Faouzi, N.-E., 2013. Cooperative highway traffic: multi-agent modeling and robustness assessment to local perturbations. In: the 92nd Annual Meeting of the Transportation Research Board, reviewed by TRB's Traffic Flow Theory and Characteristics Committee (AHB45).
27. Monteil J., Billot R., Sau J., El Faouzi N.-E. Linear and weakly nonlinear stability analyses of cooperative car-following models. IEEE Trans. Intell. Transport. Syst. 2014, pp(99):1-13.
28. Monteil J., Billot R., Sau J., Buisson C., El Faouzi N.-E. Calibration, estimation, and sampling issues of car-following parameters. Transport. Res. Rec.: J. Transport. Res. Board 2014, 2422(1):131-140.
29. Ngoduy D., Wilson R. Multianticipative nonlocal macroscopic traffic model. Comput.-Aid. Civil Infrastruct. Eng. 2014, 29(4):248-263.
30. Osman O.A., Ishak S. A network level connectivity robustness measure for connected vehicle environments. Transport. Res. Part C: Emerging Technol. 2015, 53:48-58.
31. Pinyol I., Sabater-Mir J. Computational trust and reputation models for open multi-agent systems: a review. Artif. Intell. Rev. 2013, 40(1):1-25. doi: http://dx.doi.org/10.1007/s10462-011-9277-z.
32. Ploeg J., Semsar-Kazerooni E., Lijster G., van de Wouw N., Nijmeijer H. Graceful degradation of cooperative adaptive cruise control. IEEE Trans. Intell. Transport. Syst. 2015, 16(1):488-497.
33. Sau J., Monteil J., Billot R., Faouzi N.-E.E. The root locus method: application to linear stability analysis and design of cooperative car-following models. Transportmetrica B: Transport Dynam. 2014, 1-23. no. ahead-of-print.
34. Schillo M., Funk P., Rovatsos M. Using trust for detecting deceitful agents in artificial societies. Appl. Artif. Intell. 2000, 14(8):825-848.
35. Schönhof M., Treiber M., Kesting A., Helbing D. Autonomous detection and anticipation of jam fronts from messages propagated by intervehicle communication. Transport. Res. Rec.: J. Transport. Res. Board 2007, 1999(1):3-12.
36. Sommer C., German R., Dressler F. Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Trans. Mobile Comput. 2011, 10(1):3-15.
37. Thiemann C., Treiber M., Kesting A. Longitudinal hopping in intervehicle communication: theory and simulations on modeled and empirical trajectory data. Phys. Rev. E 2008, 78:036102. .
38. Treiber M., Kesting A. An open-source microscopic traffic simulator. IEEE Intell. Transport. Syst. Mag. 2010, 2(3):6-13.
39. Treiber M., Kesting A. Traffic Flow Dynamics: Data, Models and Simulation 2013, Springer.
40. Treiber M., Kesting A. Traffic Flow Dynamic: Data, Models and Simulation 2013, Springer.
41. Van Arem B., van Driel C.J., Visser R. The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans. Intell. Transport. Syst. 2006, 7(4):429-436.
42. Van Willigen W., Haasdijk E., Kester L. A multi-objective approach to evolving platooning strategies in intelligent transportation systems. Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference 2013, 1397-1404. ACM.
43. Vu Q.-A.N., Canal R., Gaudou B., Hassas S., Armetta F. Trustsets: using trust to detect deceitful agents in a distributed information collecting system. J. Ambient Intell. Humanized Comput. 2012, 3(4):251-263.
44. Wang M., Daamen W., Hoogendoorn S.P., van Arem B. Rolling horizon control framework for driver assistance systems. Part ii: Cooperative sensing and cooperative control. Transport. Res. Part C: Emerging Technol. 2014, 40:290-311.
45. Yan, X., Gu, F., Hu, X., Engstrom, C., 2013. Dynamic data driven event reconstruction for traffic simulation using sequential monte carlo methods. In: 2013 Winter Simulation Conference (WSC), pp. 2042-2053.
46. Zheng, H., Son, Y.-J., Chiu, Y.-C., Head, L., Feng, Y., Xi, H., Kim, S., Hickman, M., 2013. A primer for agent-based simulation and modeling in transportation applications. US Federal Highway Administration, Tech. Rep.
47. Zohdy, I., Rakha, H., 2012. Game theory algorithm for intersection-based cooperative adaptive cruise control (cacc) systems. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1097-1102.