Bike Supply Research Articles

Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach

Bike-sharing systems are important components of urban transportation systems that facilitate short-distance travel. In recent years, free-floating bike-sharing systems have become popular. However, a major problem in such systems is the increasing number of idle bikes due to their spatial dispersion, which leads to a loss of demand and difficulty in bike rebalancing. To overcome these drawbacks, operators reserve parking areas, and laborers are incorporated into the bike rebalancing operation. We propose a novel dynamic bike rebalancing strategy based on trucks and laborers for free-floating bike-sharing systems. This strategy combines bike collection by laborers and bike transportation by trucks for rebalancing operations. In addition, we consider broken bike detection by laborers and user dynamics in two extension models. Because of the demand uncertainty in bike-sharing systems, we employ an enhanced distributionally robust optimization (EDRO) approach to protect against uncertainty. A target-level constraint is added to the general distributionally robust optimization (DRO) model to further improve the out-of-sample performance. An adaptive robust strategy is also employed to avoid overly conservative solutions. Furthermore, a linear decision rule is adopted to reformulate the EDRO model into a mixed-integer programming model, which guarantees computational tractability. The results of numerical experiments show that the EDRO model outperforms other benchmark models on a series of measures. Moreover, we verify the value of laborers in dynamic rebalancing operations and explore the effect of different bike supply levels and several other parameters on the value of laborers. We also test how the target-level parameter affects the model performance.

Evaluating the bikeability of urban streets using dockless shared bike trajectory data

Cycling, recognized as a sustainable and convenient mode of urban transportation, provides a solution to traffic congestion and last-mile connectivity. Despite its benefits, current urban planning often neglects cycling infrastructure, leading to inadequate support for the diverse cycling behaviors observed in many cities. To enhance the cycling environment, assessing bikeability at the urban street level is essential, involving the identification of relevant indicators and their associated weights. Traditional methods for determining these indicators and weights often rely on expert or resident opinions through surveys or various questionnaire-based techniques, lacking validation of their impact on cycling behavior. In response, we proposed a comprehensive framework to link the bikeability evaluation model with regression model of built environment on cycling behavior based on dockless shared bike trajectory data considering both demand and supply factors. The framework utilizes the regression model to derived indicators and their weights to build a four-fold bikeability evaluation model incorporating cycling demand, shared bike supply, bikeability, and behavior. An empirical study conducted in Longgang, Shenzhen, China demonstrates the effectiveness of the proposed method. The findings indicate its superiority over existing methods, providing a more reasonable and insightful approach to support the planning and construction of cycling systems in urban areas.

Spatiotemporal analysis of bike-share demand using DTW-based clustering and predictive analytics

This paper investigates bike-share activities and explores their relationships with neighborhood features, advancing our current knowledge for integrating cycle facilities into urban space to support first/last-mile mobility. To identify distinct demand patterns, bike stations are clustered based on time-series ridership. Measuring the similarity between time-series data in the transportation field should take into account the influence of phase difference because similar demands happening in the morning and in the afternoon should be considered dissimilar. This study uses a weighted Dynamic Time Warping to address this issue by assigning a larger weight to data points with a larger time difference, enabling a more realistic measure to compare ridership. Using bike-share trip data from Citi Bike, we identified eight station clusters, each of which exhibits unique temporal activities. We further integrated the locations of various points of interest to explore the profiles of the clusters. Bike stations that are closer to public transport, commercial buildings and food service establishments are generally more popular, suggesting that incorporating spatial contexts can develop a richer understanding of bike-share usage. In addition, this study goes beyond descriptive analytics by investigating the role of neighborhood features in predicting cluster memberships of bike stations. Our results show that non-tree-based models, such as Support Vector Machines and K-Nearest Neighbors, outperform tree-based models. This study provides valuable insights for both urban planners and bike-share operators. When assessing the potential of new bike-share infrastructure, urban planners can deploy our models to identify the cluster-specific demand pattern based on neighborhood features. Bike-share operators can also utilize our findings to identify neighborhoods that require strategic supply of bikes and parking space that may vary within a day.

A censored semi-bandit model for resource allocation in bike sharing systems

Resource allocation is an essential problem in the application of bike sharing systems. Demand estimation from historical data plays an important role in bike resource allocation. However, as the observed demand is always lower than the available bike supply, the historical pickup data is a supply-censored version of true user demand, which may lead to the degradation of allocation policies designed directly from historical data in actual online use. Therefore, the exploration of latent user demand is also necessary for the bike-sharing system. In this paper, we study the following problem: whether we can optimize the allocation policy with observed historical demand (exploitation) and consider exploring the latent demand (exploration) during the allocation process simultaneously. We model this problem as a censored semi-bandit problem, which aims to maximize the cumulative number of successful pickups during the multi-round allocation process when the real user demand is unknown at the beginning. We adopt a nonparametric estimator to estimate the user demand from the censored pickup feedback and propose an upper confidence bound based allocation policy to achieve a trade-off between the exploitation and exploration of user demand. The convergence property of the proposed policy is proved theoretically in this paper. Computational results of ablation experiments based on real-world data sets demonstrate the significance of considering exploring latent user demands and the proposed policy can well reduce the lost demands.

Destination Prediction Based on Virtual POI Docks in Dockless Bike-Sharing System

As the sharing economy develops and bike-sharing apps emerge, the dockless bike-sharing system (DLBS) has become a competitive alternative to the docked bike-sharing system because of its convenience of finding and parking without physical docks. Meanwhile, new demands are rapidly increasing as DLBS expands, e.g., crowd-sourced re-balancing and pre-ordering during rush hours. A more fine-grained destination prediction is required to tackle these issues. In this paper, we propose a probabilistic-trip-based destination prediction method named P³M. To overcome the uncertainty due to docks’ absence, we introduce the virtual docks derived from POIs and convert a single trip recorded in GPS into several probabilistic trips among POIs using an innovative user behavior model Walking-Riding-Walking Probabilistic Trip. To deal with sparsity, P³M adapts a trip-wised parameter share strategy together with a statistical-based history-feature extractor for better performance without overfitting. Compared with the baseline method, P³M reduces the mean absolute errors measured with distance by 31.55% (from 1.1036 km to 0.7554 km) and is less sensitive to the sparsity of user’s records. Further, we analyze the application of P³M in different types of DLBS and use two simulations to prove its efficiency under insufficient bike supply circumstances.

DISCOVERY OF TEMPORAL VARIATIONS OF PUBLIC BIKE RIDERSHIP USING INTERNET OF THINGS FOR BUILDING SMART CITY

This paper analyzes the temporal variations of public bike riding over the hours, the days and the months. A whole year of usage data (34,098,787 trips) are collected using Internet of Things(IoT) for the bikesharing system in Ningbo, China. Data Mining shows significant seasonal variations of bike rental amounts. The seasonal reductions of bike riders are explained mainly by three factors: nonpermanent residents leaving the city during the Chinese Spring Festival, uncomfortable high/low outdoor temperature, and rainy/snowy weather. Weather impact analysis suggests that rainfall decreases rental amounts significantly, with 1mm of daily rainfall expected to reduce daily rental amount by about 10%. The results also show significant day-of-the-week variations, with rental amounts in weekends 20-30% lower than in weekdays, indicating a clear commuter pattern of usage. The commuter pattern is further proven by the time of day variation with the double rental peaks of morning and afternoon. It should be noted that the total usage in either morning or afternoon rush hours only accounts for 60% of the total bike supply, which indicates that some bikes in the system may have been positioned in the wrong place at peak times. It is further shown that peak hour usage accounts for only 10∼12% of AADT (annual average daily traffic), much lower than the peak hour percentages for other modes, indicating insufficient bike supply and improper positioning of infrequently used bikes. These temporal characteristics imply that the bike turnover rate could increase further if the efficiency of the bike supply is improved with proper location and relocation of bikes. Understanding these temporal characteristics of bikesharing ridership could help the service provider and policy maker to improve the bikesharing services for building Smart City.

A two-stage robust approach to integrated station location and rebalancing vehicle service design in bike-sharing systems

A bike-sharing system is a shared mobility mechanism that provides an alternative transportation mode for short trips with almost no added travel speed loss. However, this model’s low usage ratio and high depreciation rate pose a risk to the sustainable development of the bike-sharing industry. Our study proposes a new integrated station location and rebalancing vehicle service design model. This model aims to maximize daily revenue under a given total investment for station locations and bike acquisition. To address demand ambiguity due to possible bias and loss of data, we present a two-stage robust optimization model with a demand-related uncertainty set. The first stage of our model determines the station locations, initial bike inventory, and service areas of rebalancing vehicles. In contrast to the literature, which either simplifies the rebalancing process as an inventory transshipment problem or formulates it as a complex dynamic bike rebalancing problem, we assign each rebalancing vehicle to a service area composed of several specified stations. An approximate maximum travel distance for each rebalancing vehicle is also designed and constrained to ensure that the rebalancing operation can be performed within each period. In the second stage, our model optimizes the daily fleet operation and maximizes the total revenue minus the rebalancing cost. To solve our model, we design a customized row generation approach. Our numerical studies demonstrate that our algorithm can efficiently obtain exact solutions in small instances. For a real-size problem, the nearly optimal solutions of our model also reveal a high-quality worst-case performance with a small loss in mean performance, particularly when the value of the budget ratio (that is, the average number of bikes per station) is at a medium level. Moreover, the distribution of service areas depends on the bike supply and demand level at each station. The optimal fleet rebalancing operation does not have to be confined to one geographical area. Furthermore, our robust model can achieve larger mean and worst-case revenues and a higher revenue stability than a stochastic model with a small data set.

Where are public bikes? The decline of dockless bike-sharing supply in Singapore and its resulting impact on ridership activities

Active mobility modes have become a significant alternative mode of transport within all major urban cities. This could be partly attributed to the rise and fall of dockless bike-sharing services in the last three years which significantly changed the transport ecosystem in these cities. However, the various complex relationships between the supply of bike-sharing services, the generated demand as well as the cycling dynamics and behavior of subscribers is not well understood. In this article, we examine how the changes in bike supply impacted the cycling activities of a dockless bike-sharing system in Singapore. We employed a clustering algorithm and modelled the network community detection tool into a three-month cycling GPS dataset and compared the spatiotemporal pattern and network structures of biking journeys for two major time periods (1) January 2019 and (2) March 2019 between which a significant number of bike-sharing companies exited the industry. This resulted in a drastic drop in the number of available shared bikes for the latter period. Our results show that prior to the decline of the bike supply, we identified three types of bike journeys that shared similar temporal patterns, indicating different riders, namely casual, moderate, or enthusiastic users. Over 90 percentage of enthusiastic riders disappeared after the supply of public bikes decreased. Our network analysis further showed that nine out of ten observed communities either contracted or collapsed after the supply downsize. We therefore hope that urban and transportation planners and scientists would be able to use these findings to further understand the dynamics of supply demand of bike-sharing systems and help improve planning considerations in the future.

The Bike-Sharing Rebalancing Problem Considering Multi-Energy Mixed Fleets and Traffic Restrictions

Nowadays, as a low-carbon and sustainable transport mode bike-sharing systems are increasingly popular all over the world, as they can reduce road congestion and decrease greenhouse gas emissions. Aiming at the problem of the mismatch of bike supply and user demand, the operators have to transfer bikes from surplus stations to deficiency stations to redistribute them among stations by vehicles. In this paper, we consider a mixed fleet of electric vehicles and internal combustion vehicles as well as the traffic restrictions to the traditional vehicles in some metropolises. The mixed integer programming model is firstly established with the objective of minimizing the total rebalancing cost of the mixed fleet. Then, a simulated annealing algorithm enhanced with variable neighborhood structures is designed and applied to a set of randomly generated test instances. The computational results and sensitivity analysis indicate that the proposed algorithm can effectively reduce the total cost of rebalancing.

Linguistic summarization to support supply network decisions

A supply chain network architecture is a key element of designing and modeling a supply chain to better understand the cost and time associated with the distribution of products with available resources and market locations. Due to the large size of combinations for product design and supplier choices; descriptive, predictive and prescriptive analytics are needed to design, control and then improve a supply chain network. Current study is the first instance in the supply network management field using linguistic summarization (LS), a descriptive analytics tool generating natural language-based summaries of raw data with the help of fuzzy sets. This study has developed a LS method for revealing information from a realistic complex network of a bike supply chain, and it produces network description phrases by using fuzzy set theory to model linguistic/textual terms. The truth degree of generated summaries is calculated by fuzzy cardinality-based methods instead of scalar cardinality-based methods to overcome inherent disadvantages. The results of the study are interpreted in two ways: word clouds are used for single objective cases, and list of sentences that exceed a threshold value are used for bi-objective cases. LS-based findings, explanations and strategic decisions are directed at decision support to increase supply network performance, efficiency and sustainability.

The Biobjective Bike-Sharing Rebalancing Problem with Balance Intervals: A Multistart Multiobjective Particle Swarm Optimization Algorithm

The bike-sharing system (BSS), as a sustainable way to deal with the “last mile” problem of mass transit systems, is increasingly popular in recent years. Despite its success, the BSS tends to suffer from the mismatch of bike supply and user demand. BSS operators have to transfer bikes from surplus stations to deficit stations to redistribute them among stations by means of trucks. In this paper, we deal with the bike-sharing rebalancing problem with balance intervals (BRP-BIs), which is a variant of the static bike-sharing rebalancing problem. In this problem, the equilibrium of station is characterized by a balance interval instead of a balance point in the literature. We formulate the BRP-BI as a biobjective mixed-integer programming model with the aim of determining both the minimum cost route for a single capacitated vehicle and the maximum average rebalance utility, an index for the balanced degree of station. Then, a multistart multiobjective particle swarm optimization (MS-MOPSO) algorithm is proposed to solve the model such that the Pareto optimal solutions can be derived. The proposed algorithm is extended with crossover operator and variable neighbourhood search to enhance its exploratory capability. Compared with Hybrid NSGA-II and MOPSO, the computational experimental results demonstrate that our MS-MOPSO can obtain Pareto optimal solutions with higher quality.

Mechanism Design with Predicted Task Revenue for Bike Sharing Systems

Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called TruPreTar to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. TruPreTar possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that even when the payment budget is tight, the total revenue still exceeds or equals the budget. Otherwise, TruPreTar achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least √2 that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that TruPreTar is effective in rebalancing bike supply and demand and, as a result, generates high revenue that outperforms several benchmark mechanisms.

Hardness of and approximate mechanism design for the bike rebalancing problem

Recently arose in the flourishing sharing economy, the bike rebalancing problem is a new challenge that concerns how to incentivize users to park bikes at system-desired locations that better meet bike demands. It can also be generalized to other location-based vehicle or tool sharing problems such as car, truck, drone, and trolley sharing. In this paper, we address this problem using an auction model under a crowdsourcing framework, where users report their original destinations and the bike sharing platform assigns proper relocation tasks to them in order to better balance the bike supply and demand. We first prove two impossibility results: (1) finding an optimal solution to the bike rebalancing problem is NP-hard, and (2) there is no approximate mechanism with bounded approximation ratio that is both truthful and budget-feasible. To overcome this barrier, we introduce two practical constraints and design a two-stage approximate mechanism that satisfies location truthfulness, budget feasibility, individual rationality, and achieves constant approximation ratio. To the best of our knowledge, we are the first to address two dimensional location truthfulness in the regime of mechanism design. In addition, our extensive experiments based on real-world dataset demonstrate that our proposed mechanism can effectively redress the imbalance of bike distribution.

Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China

Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic bike optimization algorithm (HBOA) to determine the optimal supply and distribution of bikes considering the effect of bicycle cycling. In this approach, the different bike trips with separate bikes can be connected in space and time and converted into a continuous trip chain for a single bike. To improve this cycling efficiency, it is important to properly design the bicycle distribution. Taking Shenzhen as an example, we implement the algorithm with OD matrix data from Mobike and Ofo, the two large bike sharing companies which account for 80% of the shared bike market in Shenzhen, over two days. The HBOA results are as follows. 1) Only one-fifth of the bike supply is needed to meet the current usage demand if the bikes are used efficiently, which means a large number of shared bikes in Shenzhen remain in an idle state for long periods. 2) Although the cycling demand is high in many areas, it does not mean that large numbers of bikes are needed because the continuous inflow caused by the cycling effect of bikes will meet most of the demand by itself. 3) The areas with the highest demands for optimal bikes are residential, followed by industrial, public transportation, official and commercial areas, on both working and non-working days. This algorithm can be an objective basis for city related departments to manage station-free BSSs and be applied to design the layout of bikes in small-scale spatial units to help station-free BSSs operate efficiently and minimize the need to relocate the bikes without reducing the level of user satisfaction.

Examining and optimizing the BCycle bike-sharing system – A pilot study in Colorado, US

Many cities around the world have integrated bike-sharing programs into their public transit systems to promise sustainable, affordable transportation and reduce environmental pollution in urban areas. Investigating the usage patterns of shared bikes is of key importance to understand cyclist’s behaviors and subsequently optimize bike-sharing programs. Based on the historical trip records of bike users and station empty/full status data, this paper evaluated and optimized the bike-sharing program BCycle in the city of Boulder, Colorado, the United States, using a combination of different methods including the Potential Path Area (PPA) and the Capacitated Maximal Covering Location Problem (CMCLP). Results showed significantly different usage patterns between membership groups, revealed diverse imbalance patterns of bike supply and demand across stations in the city and provided three system upgrading strategies about maximizing the service coverage. This case study is committed to future energy conservation and sustainable energy systems nationwide and ultimately worldwide, by holding immerse potential to adapt the resulting optimization strategies to the cities with a similar urban context across the United States, as well as more emerging bike-sharing programs in other countries, such as China.

Static green repositioning in bike sharing systems with broken bikes

Bike-Sharing Systems (BSSs) and environmental concerns have been receiving increasing popularity in transportation operations. In BSSs, the distribution of bike demand often mismatches with bike supply and there are broken bikes. Usable bikes are needed to redistribute between stations to satisfy the demand and all broken bikes need to be carried back to the depot for repairs. Both types of bikes are often transported by fossil-fueled vehicles but using these vehicles for the operation may damage the environmental creditability of BSSs. A methodology is needed to mitigate the environmental impact of this operation.This study aims to propose a methodology to reposition both good and broken bikes in a bike-sharing network in order to achieve a perfect balance between bike demand and supply at each station and make sure that all broken bikes are moved back to the depot. The objective of this repositioning operation is to minimize the total CO2 emissions of all repositioning vehicles. A Mixed Integer Linear Program (MILP) model is presented to formulate the problem mentioned above and a commercial solver is used to solve it for small applications. Using example applications, problem characteristics and the factors that affect the CO2 emissions are discussed. The results indicate that allowing multiple visits can reduce vehicle emissions. Moreover, when the percentage of broken bikes in the system increases, the CO2 emissions increase. Furthermore, if there is a tolerance for meeting the demand target, when this tolerance increases, the CO2 emissions decrease. In addition, when the distance of a link in an optimal route increases, the resultant emissions may remain unchanged. Besides, when the vehicle capacity increases, the CO2 emissions decrease. The real world instances of Citybike Vienna are used to compare emission and distance minimization solutions and investigate the runtime complexity of the proposed model. The results demonstrate that a shorter distance may not necessarily lead to lower emissions. The results also show that as the number of vehicles increases, the total emissions and runtime increase. A clustering method based on the nearest neighbor heuristic together with a commercial solver is used to solve a large real-world instance. This result confirms the possibility of using the clustering approach to reduce the running time for large network instances with multiple vehicles.

A hybrid large neighborhood search for the static multi-vehicle bike-repositioning problem

This paper addresses the multi-vehicle bike-repositioning problem, a pick-up and delivery vehicle routing problem that arises in connection with bike-sharing systems. Bike-sharing is a green transportation mode that makes it possible for people to use shared bikes for travel. Bikes are retrieved and parked at any of the stations within the bike-sharing network. One major challenge is that the demand for and supply of bikes are not always matched. Hence, vehicles are used to pick up bikes from surplus stations and transport them to deficit stations to satisfy a particular service level. This operation is called a bike-repositioning problem. In this paper, we propose a hybrid large neighborhood search for solving the problem. Several removal and insertion operators are proposed to diversify and intensify the search. A simple tabu search is further applied to the most promising solutions. The heuristic is evaluated on three sets of instances with up to 518 stations and five vehicles. The results of computational experiments indicate that the heuristic outperforms both CPLEX and the math heuristic proposed by Forma et al. (2015) [Transportation Research Part B 71: 230–247]. The average improvement of our heuristic over the math heuristic is 1.06%, and it requires only a small fraction of the computation time.