A novel model and its algorithms for the shortest path problem of dynamic weight-varying networks in Intelligent Transportation Systems

• Published in: Journal of intelligent & fuzzy systems Vol. 33; no. 5; pp. 3095 - 3102
• Main Authors: Zhong-Zhong, Jiang, Yi-Ru Jiao, Sheng, Ying, Chen, Xiaohong
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2017
• Subjects: Adaptive algorithms; Algorithms; Ant colony optimization; Complexity; Computation; Critical path; Dynamic programming; Greedy algorithms; Intelligent transportation systems; Mathematical models; Route selection; Shortest-path problems; Systems engineering; Transportation networks
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-169361

Intelligent Transportation Systems (ITS) are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems. In this paper, a novel route selection problem based on the envisaged driving mode with dynamic signals in ITS is proposed. It belongs to a kind of the shortest path problem of dynamic weight-varying networks, and the arc-weights of the network vary with the arc-chosen, so it cannot be solved by the existing greedy algorithms. According to the characteristics of the proposed problem, firstly, a dynamic programming model for the driving mode on a single path is established. Secondly, two algorithms for solving the route selection problem based on the former mode are developed. One is a brute-force algorithm based on matrix expansion with the computational complexity of O  (Nt × n2). The other is an improved adaptive ant colony algorithm with the complexity of O  (Nc × m × n2). Finally, the computational experiments show the advantages and disadvantages of the two effective algorithms with numerical examples.