Abstract
To better solve the passenger assignment problem, which is a subproblem of the transit network optimization problem, we build an artificial urban transit system (AUTS) and adopt a day-to-day learning mechanism to describe passengers' route and departure-time-choice behaviors. With the support of AUTS to handle the lower level assignment problem, we are able to solve the upper level transit network design problem. Compared with other bilevel models, our approach better accommodates passengers' dynamic learning behavior and their heterogeneity. Based on AUTS, we solve the frequency optimization problem and compare the results with an analytical method. We also perform some numerical experiments on AUTS and discover some interesting issues on the capacity of public transportation system and passengers' heterogeneity.
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