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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 146070, 10 pages
Research Article

A Cooperative -Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

School of Information Science and Engineering, Central South University, 22 South Shaoshan Road, Changsha 410075, China

Received 25 May 2015; Accepted 21 September 2015

Academic Editor: Chronis Stamatiadis

Copyright © 2015 Xiaoyong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperative -learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form that -learning can be applied firstly. Then the gray prediction algorithm is combined into -learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm.