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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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
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)
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
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)
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)
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)
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)
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)
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)
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)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-27137-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27136-1
Online ISBN: 978-3-319-27137-8
eBook Packages: Computer ScienceComputer Science (R0)