Abstract:The primary objective of this study was to explore the factors that influence metro-bikeshare ridership from a spatial perspective. First, a reproducible method of identifying metro-bikeshare transfer trips was derived using two types of smart-card data (metro and bikeshare). Next, a geographically weighted Poisson regression (GWPR) model was established to explore the relationships between metro-bikeshare transfer volume and several types of independent variables, including sociodemographic, travel-related, and built-environment variables. Moran's I statistic was applied to examine the spatial autocorrelation of each explanatory variable. The modeling and spatial visualization results show that riding distance is negatively correlated with metro-bikeshare transfer demand, and the coefficient values are generally lower at the edge of the city, especially in underdeveloped areas. Moreover, the density of bus, bikeshare, and other metro stations within 2 km of a metro station has different impacts on metro-bikeshare transfer volume. Travelers whose origin or destination is entertainment related tend to choose bikeshare as a feeder mode to metro if this trip mode is available to them. These results improve our understanding of metro-bikeshare transfer spatial patterns, and several suggestions are provided for improving the integration between metro and bikeshare.

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Long-distance school commuting is a key aspect of students' choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students' metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students' metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students' metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead.

How to meet the daily demand for resident transport while limiting the transmission of infectious diseases is a problem of social responsibility of urban transport systems during major public health emergencies. Considering the novel coronavirus pneumonia epidemic (COVID-19), a bus timetable system based on the “if early, wait, and if late, leave soon” strategy is proposed. Based on public transport vehicle constraints in this system, the concept of reliability is introduced to evaluate public transport timetable systems, and a model based on an event tree is built to calculate the failure rate of urban bus timetables. Then, the public transport situation in Yixing city is used as an example to perform confirmatory analysis, and the fluctuations in the reliability of the bus timetable during the novel coronavirus pneumonia epidemic are discussed. The research results show that the method proposed in this paper can obtain the overall failure rate of urban bus timetable by traversing the calculation of each round-trip interval and achieve an accurate evaluation of the reliability of bus timetables. During the early, middle, and more recent stages of the COVID-19 outbreak, the failure rate of bus timetables in Yixing city initially decreased and then increased. In the early stage of the outbreak, the failure rate of the Yixing bus timetable was 7.8142. However, the failure rate decreased to 4.3306 and 5.0160 in the middle and late stages of the epidemic, respectively. In other words, the failure rate of the public transport network in the middle and late stages decreased by 44.58% and 35.81%, respectively, compared with that in the early stage. Thus, during major health emergencies, such as the novel coronavirus pneumonia outbreak, the reliability of the urban bus timetable system can be improved by at least 35%, and cross-infection at bus stations can be prevented. The research results verify the feasibility and reliability of the implementation of bus timetabling strategies during major health emergencies.