Understand Group Travel Behaviors in an Urban Area Using Mobility Pattern Mining

With the development of cities, especially in developing countries, public transport is a major choice for millions city dwellers, which can ease the traffic pressure, such as crowdedness. However, for cities, in particular cities of developing countries, the continuous development of urban construction leads to the function of regions are changed, and then the collective that travel to specific locations in the city are redistributed, such as a new shopping mall in operation or a new-built community comes into service to meet travel demands of citizens. Currently, Automated Fare Collection Systems (AFCS) are widely used in cities around the world and large amounts of data from AFCS have been acquired. In this paper, we present a new framework to use the data through AFCS to discovering regions with high passenger gathering intensity and classify points in these regions with similar passenger gathering feature varying with time in dynamic way, which is called spark region. Furthermore, the novel definition group mobility pattern (GMP) is proposed to mine the regular group behavior among these spark regions. A series of analysis is employed by using large-scale and real-world data, which consists of nearly 17million people's daily public transit records, bus trajectories generated by over 14,854 buses organizations in Beijing at 20seconds interval. The actual application indicates group mobility pattern is helpful for diagnosis and understanding residence of each region with their demand for public transportation in a significant way.