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On Exploring a Virtual Agent Negotiation Inspired Approach for Route Guidance in Urban Traffic Networks

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Book cover Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9530))

Abstract

The traditional route guidance system often provides the same shortest route to different drivers regardless of their different traffic conditions. As a result, many vehicles may rush into the same road segments at the same time that would lead to traffic congestion. Such uncontrolled dispersion of vehicles can be avoided by evenly distributing vehicles along the potential routes. This paper proposes a practical Virtual Agent Negotiation based Route Guidance Approach (VANRGA). In the proposed approach, vehicle agents (VAs) in the local vicinity communicate with each other before the intersections to achieve a real-time and dynamic route selection. Based on the route preference of the drivers and the traffic conditions, the vehicles are distributed on the routes equally, which can avoid the traffic congestion and maximize the utility of the road resources. After presenting the design and implementation methodology of VANRGA, this paper carries out extensive experiments on synthetic and real-world road networks. The experimental results show that compared to the shortest path algorithms, VANRGA offers a 22 %–37 % decrease in travel time (when traffic demand is below network capacity) and a 15 %–18 % decrease in travel time (when traffic demand exceeds network capacity).

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References

  1. El-Tantawy, S., Abdulhai, B.: Multi-agent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 319–326. IEEE (2012)

    Google Scholar 

  2. Zolfpour-Arokhlo, M., Selamat, A., Mohd Hashim, S.Z., et al.: Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms. Eng. Appl. Artif. Intell. 29, 163–177 (2014)

    Article  Google Scholar 

  3. Hu, W., Wang, H., Yan, L.: An actual urban traffic simulation model for predicting and avoiding traffic congestion. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC 2014), pp. 2681–2686. Qingdao, China, 8–11 Oct 2014

    Google Scholar 

  4. Yamada, K., Ma, J., Fukuda, D.: Simulation analysis of the market diffusion effects of risk-averse route guidance on network traffic. Procedia Comput. Sci. 19, 874–881 (2013)

    Article  Google Scholar 

  5. Hu, W., Yan, L., Wang, H.: Traffic jams prediction method based on two-dimension cellular automata model. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC 2014), pp. 2023–2028. Qingdao, China, 8–11 Oct 2014

    Google Scholar 

  6. Tumer, K., Proper, S.: Coordinating actions in congestion games: impact of top–down and bottom–up utilities. Auton. Agents Multi-agent Syst. 27(3), 419–443 (2013)

    Article  Google Scholar 

  7. Tumer, K., Agogino, A.K., Welch, Z., et al.: Traffic congestion management as a learning agent coordination problem. In: Multiagent Architectures for Traffic and Transportation Engineering. Springer, Berlin (2009)

    Google Scholar 

  8. Tumer, K., Welch, Z.T., Agogino, A.: Aligning social welfare and agent preferences to alleviate traffic congestion. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 655–662 (2008)

    Google Scholar 

  9. Lakas, A., Chaqfeh, M.: A novel method for reducing road traffic congestion using vehicular communication. In: Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, pp. 16–20. ACM (2010)

    Google Scholar 

  10. Wenbin, Hu, Liang, Huanle, Peng, Chao, Bo, Du, Qi, Hu: A hybrid chaos-particle swarm optimization algorithm for the vehicle routing problem with time window. Entropy 15, 1247–1270 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Adler, J.L., Satapathy, G., Manikonda, V., et al.: A multi-agent approach to cooperative traffic management and route guidance. Transp. Res. Part B Methodol. 39(4), 297–318 (2005)

    Article  Google Scholar 

  12. Airiau, S., Endriss, U.: Multiagent resource allocation with sharable items: simple protocols and Nash equilibria. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 167–174 (2010)

    Google Scholar 

  13. Desai, P., Loke, S.W., Desai, A., et al.: CARAVAN: congestion avoidance and route allocation using virtual agent negotiation. IEEE Trans. Intell. Transp. Syst. 14(3), 1197–1207 (2013)

    Article  Google Scholar 

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Acknowledgments

This work is supported in part by the National Basic Research Program of China (973 Program) under Grant 2012CB719905, the National Natural Science Foundation of China under Grant 61572369 and 61471274, the National Natural Science Foundation of Hubei Province under Grant 2015CFB423, the Wuhan major science and technology program under Grant 2015010101010023.

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Correspondence to Wenbin Hu .

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Hu, W., Yan, L., Wang, H., Du, B. (2015). On Exploring a Virtual Agent Negotiation Inspired Approach for Route Guidance in Urban Traffic Networks. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9530. Springer, Cham. https://doi.org/10.1007/978-3-319-27137-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-27137-8_1

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  • Publisher Name: Springer, Cham

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