Train timetable optimization of urban rail transit in rush hours based on the dynamic pricing strategy

In recent years, with the rapid growth of urban residents' travel demand, especially during rush hours, urban rail transit faces a huge challenge of serious mismatch between transportation capacity and passenger demand. To solve this problem, this paper analyzes the relationship among passenger demand, train timetables and price, tries to adjust the passenger demand distribution using a dynamic pricing strategy, and based on this, constructs a train timetable optimization model with the objective of minimizing the passengers’ waiting time at stations and designs a corresponding genetic algorithm to solve it. The practicality of the model and algorithm is verified by an example. The results of the example prove that the passenger demand during the rush hour decreases significantly based on the dynamic pricing strategy, and the optimized train timetable also adapts to the passenger demand better, effectively shortens the passenger waiting time at the station and relieves the crowded situation of the station during the rush hours.