Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2016

Abstract

Transportation and mobility are central to sustainable urban development, where multiagent-based route guidance is widely applied. Traditional multiagent-based route guidance always seeks LET (least expected travel time) paths. However, drivers usually have specific expectations, i.e., tight or loose deadlines, which may not be all met by LET paths. We thus adopt and extend the probability tail model that aims to maximize the probability of reaching destinations before deadlines. Specifically, we propose a decentralized multiagent approach, where infrastructure agents locally collect intentions of concerned vehicle agents and formulate route guidance as a route assignment problem, to guarantee their arrival on time. Experimental results on real road networks justify its ability to increase the chance of arrival on time.

Keywords

Multiagent-based Route Guidance, Probability Tail Model, Intelligent Transportation System

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 30th AAAI Conference on Artificial Intelligence, Arizona, United States of America, 2016 Feb 12-17

First Page

3814

Last Page

3820

ISBN

9781577357605

Identifier

10.1609/aaai.v30i1.9893

Publisher

AAAI press

City or Country

Washington

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1609/aaai.v30i1.9893

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