Dockless bike sharing plays an important role in complementing urban transportation systems and promoting the sustainable development of cities worldwide. To improve system operational efficiency, it is critical to study the spatiotemporal patterns of dockless bike sharing demand as well as factors influencing these patterns. Based on bicycle trip data from Mobike, Point of Interest (POI) data and smart card data in Beijing, we built a spatially embedded network and implemented the Infomap algorithm, a community detection method to uncover the usage patterns. Then, the Gradient Boosting Decision Tree (GBDT) model was adopted to investigate the effect of the built environment and public transit services by controlling the temporal variables. The spatiotemporal distribution shows imbalanced characteristics. About half of the total trips occur in the morning/evening rush hours and at noon. The community detection results further reveal a polycentric pattern of trip demand distribution and 120 sub-regions with a significant difference in connection strength and scale. The result of the GBDT model indicates that factors including subway ridership, bus ridership, hour, residence density, office density have considerable impacts on trip demand, contributing about 62.6% of the total influence. Factors also represent complex nonlinear relationships with dockless bike sharing usage. The effect ranges of each factor were identified, it indicates rebalancing schemes could be changed according to spatial location. These findings may help planners and policymakers to determine the reasonable scale of bike deployment and improve the efficiency of redistribution in local regions while reducing rebalance costs.

As an important part of urban public transport, bus service quality is an important factor affecting the choice of passenger travel mode. This paper constructs a set of satisfaction evaluation indicator systems from the perspective of passenger perception, covering the whole travel process. It is composed of 6 first-level indexes (timeliness, safety, convenience, comfort, reliability and economy) and 21 second-level indexes. Considering the scale of bus service in Beijing, this research carried out a stratified sampling on 100 bus lines and collected 3012 field questionnaire surveys. The basic information of the bus routes investigated, demographic questions and their opinions of the satisfaction of the bus service were all recorded in the questionnaire. After testing the reliability and validity of the indicator system, the paper proposes a satisfaction evaluation model weighted by the related coefficient. The results show that overall satisfaction score is 78.2 and the proportion of bus passengers who are satisfied with the bus service nearly 70%. Multivariate analysis of variance methods were employed to evaluate the satisfaction influencing factors. Conclusions can be drawn that the satisfaction score of timeliness is lowest, which is mainly influenced by three factors: the passenger’s age, travel purpose and time. The research provides positive contributions toward normalizing performance evaluation for public transportation and enhancing the sustainable development of bus.

Based on an analysis of the factors that influence the mode choice behaviors of commuters who use public transport (subways and buses), this study developed a questionnaire using a combination of revealed preference (RP) and stated preference (SP) techniques; both online and paper surveys were conducted to gather commuters’ travel choices between subways and buses. A binary logit (BL) specification was proposed to examine public transport commuters’ travel choices. The regression coefficients were estimated using maximum likelihood estimation. Finally, based on the data obtained from Beijing public transport smart cards, a support vector machine (SVM) classification model was established to identify commuters’ mode choices, and the accuracy was found to be as high as 94.24%. The estimated mode choice model was employed to predict the market shares of both subways and buses after a new fare scheme was implemented. The results showed that the model had high prediction accuracy: the average absolute error for predicting the market share of buses was 5.93%.

For a sustainable public transport system, it is important to unveil the spatiotemporal characteristics of ridership and identify the influence mechanisms. Some studies analysed the effects of weather and built environment separately, however, their effects when incorporated remains to be determined. Using smart card data, weather information, and point of interest data from Beijing, the Light Gradient Boosted Machine was employed to investigate the relative importance of weather and built environment variables contributing to daily ridership at the traffic analysis zone level, and investigate the non‐linear relationship and interaction effects between them. Weather conditions and built environment contribute 30.22 and 55.83% to ridership fluctuations, respectively. Most variables show complex non‐linear and threshold effects on ridership. The interaction effects of weather and weekend/public holiday have a more substantial influence on ridership than weekdays, indicating weather conditions have less impact on regular commuting trips than discretionary trips. The ridership fluctuations in response to changing weather conditions vary with spatial locations. Adverse weather, such as strong wind, high humidity, or heavy rainfall, has a more disruptive impact on leisure‐related areas than on residence and office areas. This study can benefit stakeholders in making decisions about optimising public transport networks and scheduling service frequency.