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The pricing problem for demand-responsive feeder transit (RFT) in competitive relationships with public bicycles in bicycle-sharing systems is investigated from a game-theoretic perspective to establish a feeder program for passengers traveling to and from transport hubs. First, a revenue model including the base fare and passenger travel delay penalty is constructed. In this model, the base fares of responsive feeder buses and public bicycles are functions of the distance from the demand point to the metro station and the rental period, respectively. Then, the pricing game process under mutual competition between responsive feeder buses and public bicycles is analyzed. Moreover, a nested two-layer planning pricing model is constructed. Finally, a solution algorithm based on sensitivity analyses and a genetic algorithm is designed. The results show that this method can be used to effectively determine the unit fares and passenger flow sharing rates of RFT and public bicycles. Compared with that under the single-vehicle RFT operation mode, the RFT fare under the two-vehicle RFT operation mode is lower, and the passenger flow sharing rate is higher, which can significantly increase the social benefits. However, the total revenue of the operation is significantly reduced and thus requires greater financial input from the government. Under the dual-vehicle operation mode, the fares of public bicycles are greater than those of RFT, and the passenger flow sharing rate is low, which significantly reduces both the social benefits and the total benefits of the operation. With the improvement of the operation mode of the RFT system, the competitiveness of using public bicycles decreases.

Fitting and modeling spatial-temporal processes are essential research topics in transportation studies. Recently, due to the analytically tractable formulation and good fitting accuracy, the Gaussian Process (GP) is becoming increasingly preferable in fitting transportation processes. However, conventional GPs are inapplicable for large-scale problems due to computational issues. Moreover, they dismiss physics laws when conducting multi-output fitting tasks. This paper proposes a physics regularized multi-output grid Gaussian Process Model (PRMGGP) model for fast and multi-output fitting of large-scale spatial-temporal processes in transportation systems. The PRMGGP model adopts a grid input structure to capture inherent spatial-temporal correlations in the fitting process, takes advantage of the Kronecker algebra to notably accelerate the computation speed, and utilizes a shadow GP to incorporate physics laws of the process. Model training and predictive algorithms are developed coordinately and are tested via synthetic datasets. Furthermore, we apply the proposed model and other widely used machine learning models to fit the numbers of pickups, returns, and idle bikes of a large-scale bike-sharing system based on Citi Bike data from New York City. The results demonstrate the computational efficiency, interpretable results, and the prediction accuracy of the PRMGGP model, which can be a promising methodology for modeling multi-output processes in transportation systems. [ABSTRACT FROM AUTHOR]

As an eco-friendly travel mode, bike-sharing has prevailed around the world. However, the systems are imbalanced due to the asymmetric spatial and temporal distribution of user demand. Station prioritization strategies are needed to rebalance more shared bikes for more important stations. This paper proposes an evaluation method of station importance in dynamic bike-sharing rebalancing. Firstly, a short-term demand prediction model is applied to capture the temporal and spatial characteristics of bike-sharing trip data and predict bike-sharing demand at the station level. Based on the prediction results, the method of determining rebalancing quantity is proposed with consideration of bike-sharing usage throughout the rebalancing period. Then, three criteria are employed to evaluate the importance of bike-sharing stations, including rebalancing quantity, closeness to inventory threshold, and distance from the key station. An entropy-based Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach is proposed to weigh different criteria and evaluate station importance. Furthermore, the experiments on bike-sharing data from Nanjing City demonstrate the effectiveness of the proposed methods. This research is helpful for operators and managers to dynamically rebalance shared bikes with high efficiency and improve the service quality of bike-sharing systems.

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.

Bike-sharing is adopted as a valid option replacing traditional public transports since they are eco-friendly, prevent traffic congestions, reduce any possible risk of social contacts which happen mostly on public means. However, some problems may occur such as the irregular distribution of bikes on related stations/racks/areas, and the difficulty of knowing in advance what the rack status will be like, or predicting if there will be bikes available in a specific bike-station at a certain time of the day, or if there will be a free slot to leave the rented bike. Thus, providing predictions can be useful to improve the service quality, especially in those cases where bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bike-slots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions, namely of 15, 30, 45, and 60 minutes. The study has demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offers a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. This paper has also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different cluster behaviours. Both solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure.

As a new type of short distance commuting, the station-free sharing bike effectively alleviates urban traffic congestion. Thus, they are deployed in a large scale in many cities. However, various complex factors, including spatial, temporal, and other external information, result in serious imbalance of supply and demand between regions, which makes accurate prediction a challenging issue. In this study, our primary objective is to accurately forecast supply and demand by leveraging multi-source datasets. Based on the visual analysis about spatial-temporal characteristics of GPS data in Shanghai, we presented the innovative methods of Area of Interest grading and Traffic Analysis Zone division of bike-sharing, and revealed the distribution characteristics of sharing bike trips. A multi-block hybrid model where three blocks were separately modeled according to different data types was proposed. Moreover, eight state-of-the-art models and two variant models were developed as benchmarks to compare and evaluate the proposed model. The results suggested that MBH outperforms ten baselines with the highest accuracy. In addition, we conducted practical application of prediction results to validate that the proposed model could provide effective information for scheduling and rebalancing of bike-sharing system.

Bike-sharing has experienced rapid growth in China since 2016. Notwithstanding the fast expansion, or possibly because it grew too fast, some bike-sharing companies have experienced setbacks and failure in 2018. This paper aims to close the gap due to the insufficient analysis on the influencing factors of the bike-sharing system problems in China. The study first used text mining of bikes-sharing related Chinese news reports and social web discussion boards. Subsequently, the study used association rule mining to explore the relationship between keywords generated by text mining of the original data sources. The results of news reports mined keywords show that problems with shared bikes deposits were closely related to the complaints of customers. In the context of social web discussion boards, the keywords relations implies that the users concern about the possible collapse of the bike-sharing companies and related management and economy issues. The information existed before the bike-sharing companies financial failure news took place. Our results also show that the association rule mining relationship of major keywords in news reports and social media can be an early warning sign of the financial failure of sharing bikes companies.

A docking bike-sharing system (BSS) is modeled as a network representing the underlying transportation network. Mobile agents (replenishment trucks) traverse the network making routing decisions and deciding how and when to replenish station inventories so as to prevent imbalances due to users’ one-way rides as well as time-varying demand. This load balancing process entails selecting both optimal routes for the agents and the number of bikes to load/unload at a station with an objective of minimizing a user dissatisfaction metric. First, we establish a time-dependent replenishment fill-to level policy for each station based on the demand rates and station capacities. Next, we focus on developing a receding horizon controller (RHC) to find optimal routes. The controller proceeds in an event-driven manner to determine after each event the optimal routes for a fleet of agents over a finite planning horizon, with the control applied over a shorter action horizon. The proposed controller is applied to a simulated BSS with station and demand parameters taken from the public data sets of Bluebikes, the BSS in Boston, MA, USA, and a cost-benefit analysis is performed on agent shift hours. In order to demonstrate the robustness of the RHC, sensitivity analysis is also performed on the arc travel times and the demand processes. Note to Practitioners—This paper is motivated by the load balancing problem faced by BSS with finite-capacity docking stations and time-varying demand; stations become empty or full as their popularity as an origin or destination varies over the course of a day. The proposed RHC creates dynamic cooperative routes for a fleet of load balancing trucks to move bikes between stations by considering the current inventory, demand rates, and proximity of all stations and trucks. The strength of this controller is that it achieves optimality over a specified planning horizon and reacts quickly to random inventory changes which update the planning horizon on a rolling basis. Thus, the optimal routes found for a planning horizon are updated at the next decision point, i.e., an intersection. This event-driven RHC decreases the complexity of finding optimal routes so that it may be used in real time. Insights obtained from the application of this approach to the Boston BSS are that extending the length of the receding horizon provides marginal benefits beyond a certain value and that the controller is robust with respect to the stochastic behavior of the user demand and the truck travel time processes. The approach is amenable to extensions that can include incentivizing users so as to enhance load balancing beyond external truck-based interventions. [ABSTRACT FROM AUTHOR]

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though it provides convenient, low-cost, and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems for both users and operators. The primary issue is the uneven distribution of bikes caused by ever-changing usage and (available) supply. This imbalance necessitates efficient bike rebalancing strategies, which depends highly on bike mobility modeling and prediction. In this paper, a trace-driven simulation-based prediction approach is proposed by simultaneously taking user mobility demand and real-time status of stations into consideration. We extensively evaluate the performance of our design with the dataset from one of the world’s largest public bike-sharing systems located in Hangzhou, China, which owns more than 2800 stations. The evaluation results show an 85 percentile relative error of 0.6 for checkout and 0.4 for checkin prediction. The preliminary results on how the predictions can be used for bike rebalancing are also provided. We believe that this new mobility modeling and prediction approach can improve the bike-sharing system operation algorithm design and pave the way for rapid deployment and adoption of bike-sharing systems across the globe. [ABSTRACT FROM AUTHOR]

The rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility gives rise to problems for both users and operators. For users, dynamic distribution of shared bikes caused by uneven user demand often leads to the check in or check out service unavailable at some stations. For operators, unbalanced bike usage comes with more bike broken and growing maintenance cost. In this paper, we consider enhancing user experiences and rebalance bicycle utilization by directing users to different stations with a higher success rate of rental and return. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. To ensure service quality, we firstly predict the user demand of each station to obtain the success rate of rental and return in the future. Experiments indicate that the precision of our method is as much as 0.826, which has raised by 25.9 percent as compared with that of the historical average method. To rebalance bike usage, from historical data, we identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequently used bikes decreases by 33.6 percent on usage number and 28.6 percent on usage time. [ABSTRACT FROM AUTHOR]