Hybrid genetic algorithm for dynamic multi-objective route planning with predicted traffic in a real-world road network

Car navigation equipment in practical use has treated a route planning problem as a single-objective problem. In this paper, we formulate the problem as a dynamic multi-objective problem and show how it can be solved using a GA. There are three objective functions to optimize simultaneously in this problem: route length, travel time that changes rapidly with time, and ease of driving. The proposed method gives the Pareto-optimal set by using both the predicted traffic and a hybrid multi-objective GA (GA + Dijkstra algorithm) so that a driver can choose a favorite route after looking at feasible ones. We give the results of experiments comparing the proposed method with the Dijkstra algorithm and the single-objective GA in applications with a real road map and real traffic data in wide-area road network.

[1]  Edward P. F. Chan,et al.  Finding shortest paths in large network systems , 2001, GIS '01.

[2]  Qingfu Zhang,et al.  Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization , 2007, EMO.

[3]  G. Chakraborty,et al.  Multiobjective route selection for car navigation system using genetic algorithm , 2005, Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05..

[4]  Kalyanmoy Deb,et al.  Dynamic multiobjective optimization problems: test cases, approximations, and applications , 2004, IEEE Transactions on Evolutionary Computation.

[5]  Bo Huang,et al.  Bi-level GA and GIS for Multi-objective TSP Route Planning , 2006 .

[6]  Gerrit K. Janssens,et al.  Evolutionary Algorithms for the Multiobjective Shortest Path Problem , 2007 .

[7]  David Wallace,et al.  Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach , 2006, GECCO.

[8]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[9]  B. Golden.  Shortest-Path Algorithms: A Comparison , 1975 .

[10]  Mengyin Fu,et al.  A practical route planning algorithm for vehicle navigation system , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[11]  KanohHitoshi.  Dynamic route planning for car navigation systems using virus genetic algorithms , 2007 .

[12]  Hitoshi Kanoh,et al.  Dynamic route planning for car navigation systems using virus genetic algorithms , 2007, Int. J. Knowl. Based Intell. Eng. Syst..

[13]  Oscar Castillo,et al.  Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots , 2006, Soft Comput..

[14]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[15]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

[16]  Dongjoo Park,et al.  Dynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty , 2003 .

[17]  S. Tsukahara,et al.  Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[18]  Yuping Wang,et al.  Dynamic Multi-objective Optimization Evolutionary Algorithm , 2007, Third International Conference on Natural Computation (ICNC 2007).

[19]  K. Yasumoto,et al.  Personal Navigation System with a Schedule Planning Facility Based on Multi-Objective Criteria , 2005 .