Spatio-temporal Variation of Urban Bus Ridership Using Smart Card Data in a Compact City

Buses are one of the most essential parts of the urban transportation system since they can cater to residents’ daily transit demands. This study investigates the spatio-temporal patterns of urban bus traveler activities using a one-month bus trajectory dataset in Kumamoto City based on smart card data. Based on the idea of a compact city, the ordinary least squares (OLS) regression model is implemented to explore the factors that affect the bus ridership at the bus stop level in the 17 essential core districts in Kumamoto City. Then using the geographically weighted regression (GWR) model, we can get the spatial heterogeneity of the bus ridership and visualize the spatial distributions of parameter estimations. By comparing the results of the two models, we find that the two models performed similarly both in global fit and explanatory accuracy. These results can provide valuable suggestions for estimating bus demand, which may exert important implications for bus route optimization. They can also provide a basis for policy formulation by city and transportation planning and management authorities. The results of the study demonstrate the effectiveness of compact urban development in Kumamoto City from the perspective of bus ridership in each core district.