Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84440
Title: A multiagent-based approach for vehicle routing by considering both arriving on time and total travel time
Authors: Cao, Zhiguang
Guo, Hongliang
Zhang, Jie
Keywords: Intelligent Transportation Systems
Multiagent-based Route Guidance
Engineering::Computer science and engineering
Issue Date: 2018
Source: Cao, Z., Guo, H., & Zhang, J. (2018). A multiagent-based approach for vehicle routing by considering both arriving on time and total travel time. ACM Transactions on Intelligent Systems and Technology, 9(3), 25-. doi:10.1145/3078847
Series/Report no.: ACM Transactions on Intelligent Systems and Technology
Abstract: Arriving on time and total travel time are two important properties for vehicle routing. Existing route guidance approaches always consider them independently, because they may conflict with each other. In this article, we develop a semi-decentralized multiagent-based vehicle routing approach where vehicle agents follow the local route guidance by infrastructure agents at each intersection, and infrastructure agents perform the route guidance by solving a route assignment problem. It integrates the two properties by expressing them as two objective terms of the route assignment problem. Regarding arriving on time, it is formulated based on the probability tail model, which aims to maximize the probability of reaching destination before deadline. Regarding total travel time, it is formulated as a weighted quadratic term, which aims to minimize the expected travel time from the current location to the destination based on the potential route assignment. The weight for total travel time is designed to be comparatively large if the deadline is loose. Additionally, we improve the proposed approach in two aspects, including travel time prediction and computational efficiency. Experimental results on real road networks justify its ability to increase the average probability of arriving on time, reduce total travel time, and enhance the overall routing performance.
URI: https://hdl.handle.net/10356/84440
http://hdl.handle.net/10220/49185
ISSN: 2157-6904
DOI: 10.1145/3078847
Rights: © 2017 ACM. All rights reserved. This paper was published by ACM in ACM Transactions on Intelligent Systems and Technology and is made available with permission of ACM.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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