Analysis of passenger travel behavior based on public transportation OD data

With the rapid development of mobile Internet, location-based service and intelligent transportation system, it is an important part of urban traffic development to improve the service quality of public transportation and to improve the efficiency of traffic operation. At the same time, the popularization of intelligent traffic card, cloud computing and the rapid development of big data technology provide a convenient and feasible condition for researchers to analyze passengers' travel behavior features. In this paper, the author utilizes the IC card data and the public transport GPS data to extract the passengers' travel OD data through the data cleaning and the data processing, thus carries on the research about the passengers' travel time and space distribution characteristic. We select the meaningful OD data according to the Urban Functional Area Division classification, extract its features, and use the improved hierarchical clustering algorithm based on density clustering to make the same OD clustering, successfully divide the same OD passengers' travel behavior patterns into four categories. Finally, the author compares and analyzes the passengers' travel behavior patterns in different situations.