Study On Schedule-based Transit Fare Optimization Problem

Urban transit system plays an indispensable role in the whole city system, for it has the advantage of large capacity, low energy consumption, high station coverage, high accessibility, and low transit fare. It is the context of the system throughout the city, and can effectively relief city traffic congestion and reduce air pollution, which has positive effect in promoting the entire city’s economic and cultural development and comprehensive construction. However, in the premise to continuously implement transit priority policy, the transit system has brought out many problems. On the time passenger flow is very high, the transit platform is so confused and the transit system is also crowded, which affects the normal operation of the bus, and brings many potential safety risks to the entire system. While on the time passenger flow is low, there are not many passengers on the transit, which causes the serious waste of capacity. So, in this paper, we suggest that through transit fare adjustment on the premise of not changing the transit schedule, so we can change the choice of travelers, reduce the potential risks, and improve the utilization rate of transit capacity, promoting the scientific and standardized operation of the transit system.On the basis of previous researches, we extended the given transit network to form a new network, then changed travelers’ route choose behavior on the network through fare adjustment. The hard core of this article consist of two parts:(1) supposed the demand is fixed, the system manager expected to change travelers’ travel behavior by increasing the fare on peak hour, so as to reach the goal of reducing the total cost of the system, while travelers choose the routes by user equilibrium principle. In order to solve this problem, we set up a bi-level program model, and used simulated annealing algorithm to solve it, the example was also given.(2) took the common line problem and stochastic demand problem into account, we also set up a bi-level program model, the upper is to minimize the sum of deviation square of the transit load rate, the lower is the result of choosing according to effective cost, and bus fare connected the two layers. The corresponding algorithms and numerical example is also given.