ABSTRACT
In an urban transit system, the demand volume and the service level are affected by the transit operations plan. An effective transit operations plan should be drawn based on both the demand volume and the service level. The artificial neural network (ANN) can satisfactorily describe the complex nonlinear relationship between inputs and outputs. In this chapter, to understand how the demand volume and the service level affect the transit operations plan, a two-mode system of urban transit equilibrium integrating the mode choice model and the fuzzy multiobjective model is developed. The artificial neural network is used to establish the mode choice model. The fuzzy multiobjective model is used to formulate the optimal transit operations plan with uncertain parameters. A reasonable performance is achieved by the ANN with a few training data. The resultant transit operations plan provides a better service level at a reasonable operating cost.
