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.

As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies.

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

Dockless bike-sharing has provided a flexible and convenient travel mode in recent years, with which shared bikes can be rented and returned easily. However, addressing the imbalance between demand and supply effectively becomes a major challenge to guarantee the sustainability of such systems. Vehicle-based approach results in substantial operating costs and more emissions. This study proposed a user-based model to incentivize users to participate in bike rebalancing. This model contains three important components: 1) determination of a bike surplus region by sorting surplus level in descend order; 2) determination of a bike deficient region from the perspective of users’ utility maximization; and 3) updating the states of regions. Several numerical tests were implemented to evaluate the model’s performance based on predefined parameters and historical trip data in downtown of Nanjing. As the results indicate, unmet demand can be greatly reduced if providing a certain level of monetary incentive. Different values of the maximum acceptable walking distance make little difference in demand loss and operating cost at a low level of incentive. With regard to the users, it implies that higher proportion of cooperative customers who are willing to participate in rebalancing can lead to a larger reduction of unmet demand. In addition, using surplus level as the index to define bike surplus regions is beneficial to saving the overall operating cost.

As an indispensable part of public transportation systems, the bike-sharing system (BSS) can improve road resource utilization and alleviate traffic congestion, significantly improving urban mobility. The disproportion between the demand and supply creates a giant gap for maintaining the smooth functioning of the system. To address the issue, this paper proposes a dynamic optimization rebalancing model for docked bike-sharing systems, which aims at minimizing the operation cost of rebalancing while maximizing the user satisfaction. The rebalancing demand is evaluated using both historical and predicted data so that a second time service for each station could be avoided within a rebalancing horizon. A time-window based satisfaction modeling is put forward to evaluate user satisfaction. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) under the rolling horizon strategy is adopted to solve the model. To improve the algorithmic performance, local search based on station priority is applied. Numerical experiments using real-world data were implemented to demonstrate the proposed model and the advantage of the improved algorithm. As the results indicate, the proposed algorithm outperforms the nondominated sorting genetic algorithm II (NSGA-II) and MOEA/D without priority-based local search. Solutions with higher satisfaction profit can be discovered with the help of a local search heuristic based on station priority. The experiment also revealed that a slight increase of 7.02% (Yen29.2) in the rebalancing cost could yield a significant growth of 1233.7% (Yen288.7) in satisfaction profit.

The main objective of this study is to explore the spatiotemporal activities pattern of bicycle sharing system by combining together temporal and spatial attributes variables through clustering analysis method. Specifically, three clustering algorithms, i.e., hierarchical clustering, K-means clustering, expectation maximization clustering, are chosen to group the bicycle sharing stations. The temporal attributes variables are obtained through the statistical analysis of bicycle sharing smart card data, and the spatial attributes variables are quantified by point of interest (POI) data around bicycle sharing docking stations, which reflects the influence of land use on bicycle sharing system. According to the performance of the three clustering algorithms and six cluster validation measures, K-means clustering has been proven as the better clustering algorithm for the case of Ningbo, China. Then, the 477 bicycle sharing docking stations were clustered into seven clusters. The results show that the stations of each cluster have their own unique spatiotemporal activities pattern influenced by people’s travel habits and land use characteristics around the stations. This analysis will help bicycle sharing operators better understand the system usage and learn how to improve the service quality of the existing system.