The accuracy of the regression model of ridership of subway stations depends on the scale range of the built environment around the subway stations. Previous studies have not considered the Modifiable Area Unit Problem (MAUP) to establish the regression model of subway station ridership. Taking Beijing as an example, this paper expanded the built environment variables from “5D” category to “7D” category, added indicators such as parking fee standard and population density factor, and proposed a Multi-Scale Geographical Weighted Regression (MGWR) model of outbound ridership of subway stations with standardized variables. The goodness of fit of regression models under 10 spatial scales or built environment around subway stations are compared, and the spatial heterogeneity of built environment factors under the optimal spatial scale of outbound ridership of subway stations during the morning peak on weekdays is discussed. The results show that: (1) the scale range overlapped by 1000 m radius circular buffer zone and Thiessen polygon has the highest explanatory power for the regression model, and is regarded as the optimal scale range of built environment; (2) the density of office facilities, sports and leisure facilities, medical service facilities, building density and floor area ratio (FAR) has a significant impact on the outbound ridership of all subway stations; (3) office facilities, catering facilities, FAR, number of parking lots, and whether subway stations are transfer stations have a positive impact on outbound ridership. The number of medical service facilities, sports and leisure facilities, bus stops and building density have a negative impact on outbound ridership; (4) the two added factors in this study: parking charge standard and population density, as the influencing factors of the built environment, have a significant impact on the outbound ridership of some subway stations; and (5) the different local coefficients of the built environment factors at different stations are discussed, which indicate the spatial heterogeneity on the outbound ridership. The results can provide an important theoretical basis for the prediction and analysis of demand of ridership at subway stations and the integration of the built environment around the stations.

In this paper, by combining controller area network (CAN) bus data and bus IC card data, the influence factors of emission of transit vehicles are analyzed from two aspects: at the segment-level and the bus stop-level. Statistically, the bus lane’s average speed is fast, about 30% higher than non-bus-lane road. Then, MOVES was used for emissions. Finally, a regression of segment level emissions and bus stop level energy consumption against a number of variables was done. The results show that the emissions of the transit bus can be reduced if it is on the bus lane. On the bus stop level, the idling time and the braking time are affected. More interesting, when there are a lot of people, the emissions and energy consumption start to decrease. Our findings are most relevant for transit planners who are seeking strategies to reduce emissions and energy consumption.