A Dynamic Programming Model for Operation Decision-Making in Bicycle Sharing Systems under a Sustainable Development Perspective

Factors influencing tourists’ shared bicycle loyalty in Hangzhou, China

Focusing on Hangzhou, a famous tourist city in China, in this study, four regression models were constructed through four items of tourist loyalty to investigate the influence of tourist perceptions and characteristics on male and female tourist loyalty to shared bicycles. A questionnaire survey and ordered logistic regression model techniques were used. Survey data from 467 tourists indicated that there were significant differences between male and female tourists. For male tourists, their willingness to reuse shared bicycles (Models 1 and 2) was positively correlated with ease of access to cycles, environmental awareness, psychological benefit, and management provision; however, their willingness to recommend shared bicycles (Models 3 and 4) was only affected by environmental awareness, psychological benefit, and management provision. Among female tourists, willingness to reuse shared bicycles (Models 1 and 2) was affected by ease of access to cycles, environmental awareness, and rule adherence, while their willingness to recommend shared bicycles (Models 3 and 4) was affected by ease of access to cycles, environmental awareness, psychological benefit, and managerial provision. In addition, female tourists’ socio-demographic and behavioral characteristics had a significant impact on their loyalty, among which length of stay in Hangzhou and education were significant in the four regression models, and number of visits to Hangzhou had a positive impact on female tourists’ willingness to reuse (Models 1 and 2). In addition, female tourists who used Hellobike had higher willingness to reuse and recommend than those who used Mobike. For male tourists, only length of stay in Hangzhou had a significant impact on their reuse intention (Model 2). The current study extends the theory of attribution to explore the factors which may affect tourist’s loyalty to bicycle-sharing from the perspectives of tourists’ perceptions. It will provide further support to decision makers in the development of new shared-bicycle systems at Chinese tourist destinations, with the aim of strengthening tourist loyalty to shared-bicycle programs.

Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

Free-float bike-sharing (FFBS) systems have increased in popularity as a sustainable travel mode in recent years, especially in the urban areas of China. Despite the convenience such systems offer to customers, it is not easy to maintain an effective balance in the distribution of bikes. This study considers the dynamic rebalancing problem for FFBS systems, whereby user-based tactics are employed by incentivizing users to perform repositioning activities. Motivated by the fact that the problem is frequently faced by FFBS system operators entering a new market with limited information on travel demand, we adopt the ranking and selection approach to select the optimal incentive plan. We describe the system dynamics in detail, and formulate a profit maximization problem with a constraint on customer service level. Through numerical studies, we first establish that our procedure can select the optimal incentive plan in a wide range of scenarios. Second, under our incentive plan, the profit and service level can be improved significantly compared with the scenario without incentive provision. Third, in most cases, our procedure can achieve the optimal solution with a reasonable sample size.

System Dynamics Modeling of Dockless Bike-Sharing Program Operations: A Case Study of Mobike in Beijing, China

In China, dockless bike-sharing programs (DBSPs) have changed people’s travel modes, alleviated urban traffic congestion, and reduced carbon emissions. However, a number of DBSPs have experienced financial crises since 2018. This means that research on DBSPs must be considered not only in terms of the environment and technology, but also in the operation of the program. In this paper, we modeled the DBSP operations in a certain area using a system dynamics simulation approach. The main purpose was to explore the dynamics of the program’s operation process and evaluate possible improvement strategies for maximizing the revenue of the overall DBSP. Specifically, the analysis focused on the economic profits of DBSPs in an environment of competition and government regulation. The research findings revealed that the dockless bike-sharing industry has great economic profits, but in the current environment, the market needs to be regulated by the local government. If a DBSP does not introduce new technologies or find new profit channels, it will be difficult to develop sustainably by only relying on riding profits. In addition, we provide a case study of Mobike’s operations in Beijing to support these findings and validate the developed model. Finally, we discuss Mobike’s possible improvement strategies.

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

The Bike Sharing System is a sustainable urban transport solution that consists of a fleet of bikes placed in various stations. Users will be satisfied if they find available bikes at their departure station and free docks at the destination. Despite the regulation operations of the system provider (i.e., redistribution of bikes by truck) deeper modifications (bike fleet size or station capacity) are often necessary to ensure a satisfactory service rate. In this paper, we model a sub-graph of a Bike Sharing System using the closed queuing network with a Repetitive-Service-Random-Destination blocking mechanism. This model is solved using the Maximum Entropy Method. This model faithfully reproduces the system dynamics considering the limited capacity of stations. We analyze the performance, particularly, via an overall performance indicator of the system. The various control and monitoring decisions (fleet-size, capacity of stations, incoming and outgoing flow of bikes) are applied to find out the best performance levels. The results demonstrate that the overall performance is robust enough regarding the fleet size changes but it degrades with the increase of the stations’ capacity. Finally, the arrival and departure flows control is an efficient and powerful operational leverage.