Abstract
We expect that the traffic will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. The route selection plays an important role in optimizing the traffic. There are different models of the routing problem. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The models are important, but their applicability in real life needs further investigations. We are building a test environment, where the decision making methods of the different models can be evaluated in almost real traffic. The almost real traffic runs in a well known simulation platform. The simulation platform also provides tools to calculate a dynamic equilibrium traffic assignment. The calculation needs long time and a lot of computing resources. The routing model evaluator contains an implementation of the routing model which determines the routes for the vehicles. The route selections are injected into the simulation platform, and the simulation platform drives the vehicles. The first results of the investigations with the routing model evaluator show that the route selection of the intention-aware routing model will be able to bring the traffic close to a dynamic equilibrium in real time.
Keywords
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Beckmann, M.J., McGuire, C.B., Winsten, C.B.: Studies in the Economics of Transportation. Yale University Press (1956)
Blum, A., Even-Dar, E., Ligett, K.: Routing without regret: on convergence to Nash equilibria of regret-minimizing algorithms in routing games. In: Proceedings of the 25th ACM Symposium on Principles of Distributed Computing, PODC 2006, pp. 45–52. ACM, New York (2006). https://doi.org/10.1145/1146381.1146392
Claes, R., Holvoet, T.: Traffic coordination using aggregation-based traffic predictions. IEEE Intell. Syst. 29(4), 96–100 (2014). https://doi.org/10.1109/MIS.2014.73
Claes, R., Holvoet, T., Weyns, D.: A decentralized approach for anticipatory vehicle routing using delegate multi-agent systems. IEEE Trans. Intell. Transp. Syst. 12(2), 364–373 (2011). https://doi.org/10.1109/TITS.2011.2105867
Cominetti, R., Correa, J., Olver, N.: Long term behavior of dynamic equilibria in fluid queuing networks. In: Eisenbrand, F., Koenemann, J. (eds.) IPCO 2017. LNCS, vol. 10328, pp. 161–172. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59250-3_14
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017)
Fischer, S., Vöcking, B.: On the evolution of selfish routing. In: Albers, S., Radzik, T. (eds.) ESA 2004. LNCS, vol. 3221, pp. 323–334. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30140-0_30
Gawron, C.: An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int. J. Mod. Phys. C 09(03), 393–407 (1998). https://doi.org/10.1142/s0129183198000303
Halpern, J.Y., Moses, Y.: A procedural characterization of solution concepts in games. J. Artif. Intell. Res. 49, 143–170 (2014). https://doi.org/10.1613/jair.4220
Hosmer Jr., D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, 3rd edn. Wiley, Hoboken (2013)
Koch, R., Skutella, M.: Nash equilibria and the price of anarchy for flows over time. Theory Comput. Syst. 49(1), 71–97 (2011). https://doi.org/10.1007/s00224-010-9299-y
Lopez, P.A., et al.: Microscopic traffic simulation using SUMO. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, November 2018. https://doi.org/10.1109/itsc.2018.8569938
Merchant, D.K., Nemhauser, G.L.: A model and an algorithm for the dynamic traffic assignment problems. Transp. Sci. 12(3), 183–199 (1978). https://doi.org/10.1287/trsc.12.3.183
Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, New York (2007). https://doi.org/10.1017/CBO9780511800481
Palaiopanos, G., Panageas, I., Piliouras, G.: Multiplicative weights update with constant step-size in congestion games: convergence, limit cycles and chaos. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5874–5884. Curran Associates, USA (2017). https://doi.org/10.5555/3295222.3295337
Parkes, D.C.: Online mechanisms. In: Algorithmic Game Theory, pp. 411–439. Cambridge University Press (2007). https://doi.org/10.1017/CBO9780511800481
Peeta, S., Ziliaskopoulos, A.K.: Foundations of dynamic traffic assignment: the past, the present and the future. Netw. Spat. Econ. 1(3), 233–265 (2001). https://doi.org/10.1023/A:1012827724856
Pourabdollah, M., Bjarkvik, E., Furer, F., Lindenberg, B., Burgdorf, K.: Calibration and evaluation of car following models using real-world driving data. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, October 2017. https://doi.org/10.1109/itsc.2017.8317836
Roughgarden, T.: Routing games. In: Algorithmic Game Theory, pp. 461–486. Cambridge University Press (2007). https://doi.org/10.1017/CBO9780511800481
Sandholm, W.H.: Potential games with continuous player sets. J. Econ. Theory 97(1), 81–108 (2001). https://doi.org/10.1006/jeth.2000.2696
Schaefer, M., Čáp, M., Vokřínek, J.: AgentDrive: agent-based simulator for intelligent cars and its application for development of a lane-changing assistant. In: Alonso-Betanzos, A., et al. (eds.) Agent-Based Modeling of Sustainable Behaviors. UCS, pp. 143–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46331-5_7
Schaefer, M., Vokřínek, J., Pinotti, D., Tango, F.: Multi-agent traffic simulation for development and validation of autonomic car-to-car systems. In: McCluskey, T.L., Kotsialos, A., Müller, J.P., Klügl, F., Rana, O., Schumann, R. (eds.) Autonomic Road Transport Support Systems. AS, pp. 165–180. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25808-9_10
Torabi, B., Al-Zinati, M., Wenkstern, R.Z.: MATISSE 3.0: a large-scale multi-agent simulation system for intelligent transportation systems. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds.) PAAMS 2018. LNCS (LNAI), vol. 10978, pp. 357–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94580-4_38
Varga, L.: On intention-propagation-based prediction in autonomously self-adapting navigation. Scalable Comput.: Pract. Exp. 16(3), 221–232 (2015). http://www.scpe.org/index.php/scpe/article/view/1098
Varga, L.Z.: Two prediction methods for intention-aware online routing games. In: Belardinelli, F., Argente, E. (eds.) EUMAS/AT-2017. LNCS (LNAI), vol. 10767, pp. 431–445. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01713-2_30
Varga, L.Z.: Dynamic global behaviour of online routing games. In: Weyns, D., Mascardi, V., Ricci, A. (eds.) EMAS 2018. LNCS (LNAI), vol. 11375, pp. 202–221. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25693-7_11
Wardrop, J.G.: Some theoretical aspects of road traffic research. Proc. Inst. Civil Eng. Part II 1(36), 352–378 (1952)
de Weerdt, M.M., Stein, S., Gerding, E.H., Robu, V., Jennings, N.R.: Intention-aware routing of electric vehicles. IEEE Trans. Intell. Transp. Syst. 17(5), 1472–1482 (2016). https://doi.org/10.1109/TITS.2015.2506900
Weibull, J.W.: Evolutionary Game Theory. MIT Press Ltd., Cambridge (1997)
Acknowledgement
The work of V. Antal, T.G. Farkas, A. Kiss, and M. Miskolczi was supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002). The work of L.Z. Varga was supported by project no. ED_18-1-2019-0030 (Application domain specific highly reliable IT solutions subprogramme), and implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme funding scheme.
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Antal, V., Farkas, T.G., Kiss, A., Miskolczi, M., Varga, L.Z. (2020). Routing Model Evaluator. In: Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science(), vol 12092. Springer, Cham. https://doi.org/10.1007/978-3-030-49778-1_3
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