An Optimization of Bus Timetable During Peak Periods Based on Forecasts Passenger Flow Using Neural Network

To enhance coordination between departure time and fluctuation of passenger flow demand, timetable of buses need to be optimized based on real-time passenger demand.Based on boarding data of passengers collected from IC card, sectional passenger flows can be predicted and calculated individually using BP neural network and RBF neural network.Based on prediction of traffic flows, the optimization decisions and an evaluation model are employed to design a dynamic optimization process of bus timetable.The data of passenger flows of bus lines in Wenshan city is used and analyzed.The results show that accuracy of sectional passenger flows from RBF neural network is higher than which of BP neural network.Comparing to the former timetable, the travel cost can be reduced by 4.11% and 1.35% based on the optimized timetable using RBF neural network and BP neural network, respectively.The operating costs of enterprises can be reduced 7.06% and 4.60%, respectively.The feasibility and effectiveness of the proposed dynamic optimization method is verified.