Optimization Method of Customized Shuttle Bus Lines under Random Condition

Transit network optimization can effectively improve transit efficiency, traffic conditions, and reduce the pollution of environment. In order to better meet travel demands passengers, factors influencing passengers’ satisfaction with a customized bus are fully analyzed. Taking minimum operating cost enterprise as objective considering random time constraints routes optimized. The K-means clustering analysis is used classify needs based on passenger demand shuttle bus, stochastic uncertainty under environment line considered. On basis meeting requirements minimizing service operation, an model that maximizes overall passengers public enterprises structured. smaller value function is, lower cost. When negative, it means there profit. processed by deterministic processing method constraints, then hybrid intelligent algorithm solve model. A simulation technique train approximate uncertain functions. Then, improved immune clonal vehicle routing problem. Finally, proved case reasonably efficiently realize lines in region.