Abstract: The construction of intelligent public transport system is an effective way to solve the pro-blem of urban traffic and to facilitate the residents to travel. Auto fare collection (AFC) system and vehicle GPS, which records passengers' trip and bus track data, are widely used in hyper-megalopolis. Using the bus big data efficiently to identify passengers' alighting stations is very important for urban transportation operations and organization. Based on AFC and GPS data, this paper presents an algorithm to identify passengers' alighting stations. We use the time matching method and density clustering to identify the bus stops. Considering the passengers' trip-chain and trip-section, this paper proposes an algorithm that combines the high frequency sites and site heat to identify the location of passengers' alighting stations possibility. The distance between the actual stations and the weight of the estimated points determines the accuracy of the forecast. The results illustrate the effectiveness and usefulness of the proposed method in identifying the passengers' alighting stations.