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Optimization of Road Distribution for Traffic System Based on Vehicle’s Priority

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PRICAI 2016: Trends in Artificial Intelligence (PRICAI 2016)

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Abstract

Instead of making the traffic system work fluently by focusing on each car’s way to choose their routes, in this paper, we proposed a way to make the vehicles avoid being involved into the traffic congestion by allocating the roads which are regarded as one kind of resources to the vehicles. In order to make the road allocation fair, we introduce the parameter to show each vehicle’s priority. We allocate the roads by regarding it as a linear programming problem and use linear programming to solve it. The experiment was done by using simulator SUMO and we testified that our proposal can make the vehicles avoid getting involved into traffic congestion and verified the usefulness of the vehicle’s priority.

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References

  1. Arnott, R., Rave, T., Schob, R.: Alleviating Urban Traffic Congestion. MIT Press, Cambridge (2005)

    Google Scholar 

  2. Jihang, Z., Minjie, Z., Fenghui, R., Jiakun, L.: A multiagent-based domain transportation approach for optimal resource allocation in emergency management. In: The Proceedings of the 2nd International Workshop on Smart Simulation and Modelling for Complex Systems, Buenos Aires, Argentina, 25 July 2015

    Google Scholar 

  3. Wei, D.: An overview of in-vehicle route guidance system. In: Australasian Transport Research Forum Proceedings (2011)

    Google Scholar 

  4. Zou, L., Xu, J.M., Zhu, L.X.: Application of genetic algorithm in dynamic route guidance system. J. Transp. Syst. Eng. Inf. Technol. 7(3), 45–48 (2007)

    Google Scholar 

  5. Geng, Y., Cassandras, C.: New “smart parking” system based on resource allocation and reservations. IEEE Trans. Intell. Transp. Syst. 14(3), 1129–1139 (2013)

    Article  Google Scholar 

  6. Tokuda, S., Kanamori, R., Ito, T.: Development of traffic simulator based on stochastic cell transmission model for urban network. In: Dam, H.K., Pitt, J., Xu, Y., Governatori, G., Ito, T. (eds.) PRIMA 2014. LNCS, vol. 8861, pp. 150–165. Springer, Heidelberg (2014)

    Google Scholar 

  7. Ito, T., Kanamori, R., Chakraborty, S., Otsuka, T., Hara, K.: A survery of multi-agents research that supports future societal systems(1)-economic paradigm, negotiating agents, and transportation management. J. JSAI 28(3), 360–367 (2013)

    Google Scholar 

  8. Zhang, C., Lesser, V., Shenoy, P.J.: A multi-agent learning approach to online distributed resource allocation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI 2009, Pasadena, California, USA, 11–17 July 2009

    Google Scholar 

  9. Dresner, K., Stone, P.: Multiagent traffic management: an improved intersection control mechanism. In: 4th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2005, Utrecht, Netherlands, 25–29 July 2005

    Google Scholar 

  10. Takahashi, J., Kanamori, R., Ito, T.: Evaluation of automated negotiation system for changing route assignment to acquire efficient traffic flow. In: 2013 IEEE 6th International Conference on Service-Oriented Computing and Applications, Koloa, HI, pp. 351–355, 16–18 December 2013

    Google Scholar 

  11. Braess, D., Nagurney, A., Wakolbinger, T.: On a paradox of traffic planning. Transp. Sci. 39, 446–450 (2005)

    Article  Google Scholar 

  12. Yen, J.Y.: Finding the k-shortest loopless paths in a network. Manag. Sci. 17, 712–716 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  13. Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3&4), 128–138 (2012)

    Google Scholar 

  14. Gurobi Optimizer. http://www.octobersky.jp/products/gurobi/

  15. Open Street Map. http://www.openstreetmap.org/

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Acknowledgement

The research results have been achieved by “Congestion Management based on Multiagent Future Traffic Prediction, Researches and Developments for utilizations and platforms of social big data”, the Commissioned Research of National Institute of Information and Communications Technology (NICT).

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Correspondence to Wen Gu .

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Gu, W., Ito, T. (2016). Optimization of Road Distribution for Traffic System Based on Vehicle’s Priority. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_61

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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