Q value-based dynamic programming with evolving penalties for road networks

Nowadays, many people depend on the guidance provided by car navigation devices to travel to the destination. Generally, several routes are possible for a given origin-destination (OD) in a road network. Often car navigation devices provide the minimum traveling time or distance route. However, this route may not always be comfortable for users to follow. Out of the several available routes, users may prefer to travel on a certain route depending on their preferences. In this paper, we propose a Q value-based Dynamic Programming with Evolving Penalties to find the user preferred optimal route. In the proposed method, we consider multiple criteria and take users' preferences for them. The users' preferences are used to determine the penalty values for the criteria, which are represented as the individuals of Genetic Algorithm. In the training phase, GA population is used for the evolution of penalty values of the criteria. The values obtained from the best individual is stored and used in the route search. The algorithm was applied to the road network of Fukuoka prefecture and results show that we can find the route considering users' preferences.

[1]  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..

[2]  Yasunori Iida,et al.  Panel survey on drivers' route choice behavior under travel time information , 1994, Proceedings of VNIS'94 - 1994 Vehicle Navigation and Information Systems Conference.

[3]  Shinji Hara,et al.  Robust model predictive control for discrete uncertain time-varying systems with delay , 2009, 2009 ICCAS-SICE.

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Shingo Mabu,et al.  Dynamic optimal route search algorithm for car navigation systems with preferences by dynamic programming , 2011 .

[6]  R Bellman,et al.  On the Theory of Dynamic Programming. , 1952, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Shingo Mabu,et al.  Optimal Route Based on Dynamic Programming for Road Networks , 2008, J. Adv. Comput. Intell. Intell. Informatics.

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

[9]  S. S. Yang,et al.  Application of modified model predictive control to a gantry system , 2009, 2009 ICCAS-SICE.

[10]  Lars Kulik,et al.  "Simplest" Paths: Automated Route Selection for Navigation , 2003, COSIT.

[11]  Shingo Mabu,et al.  Optimal route of road networks by dynamic programming , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Bing Liu,et al.  Using knowledge about the road network for route finding , 1995, Proceedings the 11th Conference on Artificial Intelligence for Applications.