Conventional passenger flow distribution forecasting models for urban rail transit rarely consider the impact of station surrounding area' s land-use, which results in models' accuracies decline dramatically when the land- use around stations changes, so it is necessary to establish the passenger flow distribution forecasting model considering the land-use and their matching degree. First, based on clustering analysis, the matching degree of land-use (MDLU) is defined as an indicator to reflect the correlation between the stations' land-use and passenger flow distribution. Second, the urban rail passenger flow distribution forecasting model is established based on the disaggregate theory, in which, the effects of station surrounding area' s land- use, MDLU, attraction of destination, travel time and etc. on destination choice behavior are considered. Finally, the passenger flow data collected from Guangzhou metro system is used for the case study, and the result shows that the mean absolute errors of the proposed model are successfully limited to 29.30 and 29.52 respectively when the land-use has no-change or change, which demonstrates that the forecasting accuracy of proposed model is satisfactory.
