Title :
Study on the shortest path algorithm based on fluid neural network of in-vehicle traffic flow guidance system
Author :
Huimin, Wen ; Zhaosheng, Yang
Author_Institution :
Jilin Univ. of Technol., Changchun, China
fDate :
6/21/1905 12:00:00 AM
Abstract :
The shortest path algorithm is critical for dynamic traffic assignment (DTA) and for the realization of route guidance in ITS. In order to implement the guidance function quickly and accurately, this paper introduces the fluid neural network (FNN) and develops a new parallel method based on FNN and genetic algorithm (GA) for route guidance. A sub-searching process and parameter optimization are employed to improve the performance of FNN. It is indicated by simulation that this method can be used to find the shortest route quickly from the original node to destination node in traffic networks
Keywords :
automotive electronics; genetic algorithms; neurocontrollers; optimal control; radionavigation; road traffic; traffic control; traffic information systems; destination node; dynamic traffic assignment; fluid neural network; genetic algorithm; in-vehicle traffic flow guidance system; original node; parameter optimization; route guidance; shortest path algorithm; sub-searching process; Cities and towns; Computer networks; Genetic algorithms; Navigation; Neural networks; Neurons; Roads; Telecommunication traffic; Traffic control; Vehicle dynamics;
Conference_Titel :
Vehicle Electronics Conference, 1999. (IVEC '99) Proceedings of the IEEE International
Conference_Location :
Changchun
Print_ISBN :
0-7803-5296-3
DOI :
10.1109/IVEC.1999.830636
