Transit-oriented Development and Traffic Congestion in Beijing: An Empirical Analysis Based on Geo Big Data

Transit-oriented development (TOD) has become a popular planning strategy for many large cities moving toward sustainable urban spatial structure and transport systems. However, the challenges that need to be overcome in the development process of TOD have not been extensively investigated in previous studies owing to insufficient data on TOD areas, which would reduce the potential benefits of TOD. In this study, we examine the relationship between traffic congestion and TOD performance, and measure the relative importance of TOD components on congestion in station areas of Beijing during weekday morning peak hours. Using the node-place model and a clustering method, we will use geo-tagged big data, such as taxi trajectory data and metro card swiping data originating from multiple sensors, to assess TOD performance and traffic congestion in Beijing subway station areas. An analytical framework is proposed to understand their relationships, and a quantitative analysis is conducted using a multilevel regression model. The results indicate that 94.58% of metro station areas in Beijing face light or higher congestion in morning peak hours. The performance of the 'transport' component needs to be improved compared with the 'development' and 'interrelation' components of TOD. The 'transit', 'development', and 'interrelation' components of TOD are significantly associated with Beijing's morning peak hours. It is difficult to reduce congestion simply by hoping for a traffic modal shift to public transit use because of the lack of public transportation availability. Optimizing the structure of travel demand in TOD areas and creating a pedestrian friendly environment were shown to be related to congestion reduction. The results of this study provide insights for developing targeted strategies to reduce congestion in TOD areas and promote better TOD performance in station areas.