Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China

Bus stops are important traffic facilities that affect the efficiency of transportation system as well as the characteristics of bus emissions, and the bus stop influence zone (BSIZ) is the basic to estimate the bus emissions. The primary objective of this study is to investigate how the potential factors affect the length of BSIZ. In this study, the geographically weighted regression (GWR) model was implemented to build a relationship between the length of BSIZ and various contributing factors. The spatial heterogeneity of the length of BSIZ was explored, and the spatial distributions of parameter estimations were visualized. Five types of data including bus emission data, global positioning system (GPS) data, point of interest (POI) data, bus stop feature data, and road feature data were collected from Zhenjiang in China to illustrate the procedure. The results indicated that the urban form has a significant impact on the length of BSIZ, and strong spatial variability for the length of BSIZ is observed. The number of enterprises and companies around bus stops, the distance between the stop and intersection, road hierarchy, the number of public facilities, the queue length of buses, as well as traffic volume can significantly affect the length of BSIZ, and the estimated coefficients of each bus stop vary across regions. The results provided valuable insights which contribute to quantify and estimate the emissions generated near bus stops.