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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arnott, R., Rave, T., Schob, R.: Alleviating Urban Traffic Congestion. MIT Press, Cambridge (2005)
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
Wei, D.: An overview of in-vehicle route guidance system. In: Australasian Transport Research Forum Proceedings (2011)
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)
Geng, Y., Cassandras, C.: New “smart parking” system based on resource allocation and reservations. IEEE Trans. Intell. Transp. Syst. 14(3), 1129–1139 (2013)
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)
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)
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
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
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
Braess, D., Nagurney, A., Wakolbinger, T.: On a paradox of traffic planning. Transp. Sci. 39, 446–450 (2005)
Yen, J.Y.: Finding the k-shortest loopless paths in a network. Manag. Sci. 17, 712–716 (1971)
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)
Gurobi Optimizer. http://www.octobersky.jp/products/gurobi/
Open Street Map. http://www.openstreetmap.org/
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-42911-3_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42910-6
Online ISBN: 978-3-319-42911-3
eBook Packages: Computer ScienceComputer Science (R0)