Genetic Algorithm for Shortest Driving Time in Intelligent Transportation Systems

The route guidance system, which provides driving advice based on traffic information about an origin and a destination, has become very popular along with the advancement of handheld devices and the global position system. Since the accuracy and efficiency of route guidance depend on the accuracy of the traffic conditions, the route guidance system needs to include more variables in calculation, such as real time traffic flows and allowable vehicle speeds. As variables considered by the route guidance system increase, the cost to compute multiplies. As handheld devices have limited resources, it is not feasible to use them to compute the exact optimal solutions by some well- known algorithm, such as the Dijkstra's algorithm, which is usually used to find the shortest path with a map of reasonable numbers of vertices. To solve this problem, we propose to use the genetic algorithm to alleviate the rising computational cost. We use the genetic algorithm to find the shortest time in driving with diverse scenarios of real traffic conditions and varying vehicle speeds. The effectiveness of the genetic algorithm is clearly demonstrated when applied on a real map of modern city with very large vertex numbers.

[1]  Young-uk Chung,et al.  Enhanced soft-handoff scheme for real-time streaming services in intelligent transportation systems based on CDMA , 2006, IEEE Trans. Intell. Transp. Syst..

[2]  Miki Haseyama,et al.  A genetic algorithm for determining multiple routes and its applications , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[3]  R. Dupas,et al.  A hybrid GA approach for solving the Dynamic Vehicle Routing Problem with Time Windows , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[4]  Masaharu Munetomo,et al.  A migration scheme for the genetic adaptive routing algorithm , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[5]  Victor I. Chang.  Web Service Testing and Usability for Mobile Learning , 2006, International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL'06).

[6]  Mitsuo Gen,et al.  A new approach for shortest path routing problem by random key-based GA , 2006, GECCO '06.

[7]  Hitoshi Kanoh,et al.  Route guidance with unspecified staging posts using genetic algorithm for car navigation systems , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[8]  Dario Floreano,et al.  Analog Genetic Encoding for the Evolution of Circuits and Networks , 2007, IEEE Transactions on Evolutionary Computation.

[9]  Chang Wook Ahn,et al.  A genetic algorithm for shortest path routing problem and the sizing of populations , 2002, IEEE Trans. Evol. Comput..

[10]  J. J. Garcia-Luna-Aceves,et al.  A distributed approach for multi-constrained path selection and routing optimization , 2006, QShine '06.

[11]  Shaojun Feng,et al.  Assisted GPS and its impact on navigation in intelligent transportation systems , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[12]  Meng Joo Er,et al.  Investigation on genetic representations for vehicle routing problem , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[13]  James C. Bean,et al.  A Genetic Algorithm for Scheduling Parallel Non-identical Batch Processing Machines , 2007, 2007 IEEE Symposium on Computational Intelligence in Scheduling.

[14]  Qingfu Zhang,et al.  An orthogonal genetic algorithm for multimedia multicast routing , 1999, IEEE Trans. Evol. Comput..

[15]  Jing Yuanwei,et al.  Solving the Problem of the Link Optimizing and Delay-constrained Multicast Routing Based on GA , 2006, 2006 Chinese Control Conference.