The public transits’ trip frequency determines the demand for urban public transit directly. The accuracy of forecasting plays a key role in planning and improvement of public transport system. In order to build a prediction model of public transit’ trip frequency on the basis of limited data survey, taking the public transit’ trip frequency as the research object, selecting independent variables from the characteristics of residents and the public transport service level. Predicting the model by using ordinal logistic regression and validating the model through the parallelism test, the Wald test and the verification test. Taking Taiyuan as an example, 723 valid questionnaires have been collected by issue questionnaires in four locations, including park, shopping mall, factory and school. The results show that: three independent variables including residents' age, average monthly income and travel time period are significantly related to the public transit’ trip frequency. The forecasting model is of obviously statistical significance, and the forecasting accuracy rate is about 60.44%.

CICTP;Public transportation;Trip frequency;Ordinal logistic regression;Wald test