Dockless Bike-sharing System Research Articles

A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction

While dockless bike-sharing systems are growing in popularity due to their convenience, indiscriminate parking creates disorder in cities. To address this problem, this paper proposes a novel prediction-based approach for planning dynamic electric fences. These fences can adapt to dynamic usage patterns and efficiently guide parking behavior. First, graded clustering and spatial point integration algorithms are used to determine the locations of electric fence candidates. Second, initial parking and scheduling simulations are developed to calculate the inventory of each candidate. Finally, a spatiotemporal graph neural network is utilized to predict inventory and generate real-time deployment plans for electric fences. Case studies are conducted in two regions in Shanghai, China. Compared to a non-electric fence scenario, extensive experiments show that our framework can satisfy 98.6% of the parking demand and reduce spatial entropy by 15% within a reasonable walking distance. The results contribute to improving urban orderliness and promoting the sustainability of bike-sharing systems.

Integration between Dockless Bike-Sharing and Buses: The Effect of Urban Road Network Characteristics

Globally, dockless bike-sharing (DBS) systems are acclaimed for their convenience and seamless integration with public transportation, such as buses and metros. While much research has focused on the connection between the built environment and the metro–DBS integration, the influence of urban road characteristics on DBS and bus integration remains underexplored. This study defined the parking area of DBS around bus stops by a rectangular buffer so as to extract the DBS–bus integration, followed by measuring the access and egress integration using real-time data on dockless bike locations. This indicated that the average trip distance for DBS–bus access and egress integration corresponded to 1028.47 m and 1052.33 m, respectively. A zero-inflated negative binomial (ZINB) regression model assessed how urban roads and other transportation facilities correlate with DBS–bus integration across various scenarios. The findings revealed that certain street patterns strongly correlate with frequent connection hotspots. Furthermore, high-grade roads and ‘dense loops on a stick’ street types may negatively influence DBS–bus integration. The increase in the proportion of three-legged intersections and culs-de-sac in the catchment makes it difficult for bus passengers to transfer by DBS. These insights offer valuable guidance for enhancing feeder services in public transit systems.

Retraction Note: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system

Retraction Note: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system

Understanding the Competition and Cooperation between Dockless Bike-Sharing and Metro Systems in View of Mobility

The advent of dockless bike-sharing (DBS) represents an effective solution to enhance public transportation usage. However, despite growing interest in integrating DBS with metro systems, comprehensive studies on their competitive and cooperative relationships remain limited. This study aims to analyze the spatial, temporal, and mobility characteristics of metro-related DBS to explore integration opportunities. Initially, three modes of interaction between DBS and metros are identified: strong competition, weak competition, and feeder relationships. Subsequently, based on these relationships, the analysis focuses on distance, spatio-temporal patterns, and the scope of DBS activities. Results from Beijing indicate that metro-associated DBS primarily serves as “last-mile” solutions without significant short-range competition with metro systems. Strongly competitive relationships, on the other hand, are interaction patterns due to the dense overlay of metro stations and inconvenient transfer facilities and are mainly used for non-commuting purposes. Furthermore, weakly competing and feeder DBS systems exhibit similar commuting patterns, highlighting bicycling as a viable alternative to walking within metro catchment areas and that metro catchment areas should be adapted to bicycling. Mobility communities, identified as tightly integrated cycling hubs, are proposed as strategic dispatch zones to manage peak demands and reduce operational strain on DBS fleets. These findings deepen our understanding of DBS and metro system interactions, offering insights to optimize public transport operations and enhance urban mobility solutions.

Self-loop analysis based on dockless bike-sharing system via bike mobility chain: empirical evidence from Shanghai

Self-loop analysis based on dockless bike-sharing system via bike mobility chain: empirical evidence from Shanghai

Designing online learning algorithms for electric fence placement in dockless bike-sharing system utilizing limited random parking strategies

Dockless shared bicycles, parked and frequently misplaced throughout the city, infringe upon urban public spaces and incur public costs. To address this issue, existing electric fence technology has been employed; however, compelling users to park their bicycles in static electric fences with fixed locations undermines the convenience offered by the dockless bike-sharing system's flexible parking. In this study, we develop a novel, implementable online learning algorithm that dynamically updates the electric fence siting scheme based on users' parking needs. We introduce the concept of a dynamic no-parking area, termed a ‘limited random parking’ strategy, which significantly reduces the average walking distance for users to between 9% and 40% of the original distance. The dynamic electric fence introduced in this research enhances Shannon entropy reduction by 5–19% compared to static electric fences. Moreover, we discovered that deploying merely approximately 20% of the total electric fences as dynamic ones suffices to further regulate parking without notably extending users' average walking distance, while the remaining 80% can remain static. Additionally, the marginal benefit of Shannon entropy sharply declines once the number of electric fences reaches a certain threshold. Building on these findings, we also offer guidelines for determining the optimal number of specific dynamic electric fences, suggesting that establishing approximately 500 dynamic electric fences in Nanjing represents an optimal strategy.

The heterogeneous effects of dockless bike-sharing usage intensity on house prices near subway stations

Dockless bike-sharing (DBS) systems have emerged as a popular mode of transportation in urban areas. While existing literature has explored the potential effects of DBS on urban systems, there is limited research on its impact on housing markets. This study addresses this gap by investigating the heterogeneous effects of DBS usage intensity on house prices at various distances from subway stations in Shanghai. Utilizing Mobike trip data and a dataset of 50,837 second-hand houses sold between May 2016 and December 2018, the analysis reveals that DBS usage intensity positively impacts house prices in areas outside 800 m and within 3000 m from subway stations, resulting in a 1.4 % increase in house prices for every 1,000 DBS rides within a 500 m radius. The study also finds that the marginal effect of DBS usage intensity on house prices is contingent on the distance from subway stations. For distances shorter than 2.33 km, the marginal effect rises with increasing distance. Conversely, for distances exceeding 2.33 km, the marginal effect declines and turns insignificant beyond 3 km. These findings imply that the positive influence of DBS on house prices is more pronounced in areas that are neither too close nor too far from subway stations, where people are more likely to use DBS to connect to subway networks. The findings of this study contribute to a better understanding of the complex relationship between DBS and real estate markets.

Identification Dockless Bike-Sharing and Metro Transfer Travelers through Mobility Chain

The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.

A Robust RF-Based Wireless Charging System for Dockless Bike-Sharing

In the past few years, dockless bike-sharing has become a popular means of public transportation and brought significant convenience to millions of citizens. As one of the key components of a shared bike, the smart locking/unlocking module has proposed a new challenge of how to provide robust power supplement for them. Current charging solutions for shared bikes are mainly based on mechanical power and solar power, and rarely take user experience and charging delay into consideration. In this paper, we design a robust RF-based wireless charging system for dockless bike-sharing. Our system utilizes radio frequency (RF) power to provide stable charging service while preserving the quality of service. In our system, an RF wireless charging sensing node is integrated on the bike's basket, so that the mutual interference during charging process and space occupation can be reduced. In order to reduce charging delay, we first design an efficient charging direction scheduling algorithm for a single charger. Then, we extend the solution to multiple-charger scenarios via dynamic programming. Our system has been successfully implemented on a dockless bike-sharing system. The experimental results verify that our design can satisfy the charging demands of shared-bikes and achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$85\%$</tex-math></inline-formula> of the optimal solution.

Nonlinear Influence and Interaction Effect on the Imbalance of Metro-Oriented Dockless Bike-Sharing System

Dockless Bike-Sharing (DBS) is an eco-friendly, convenient, and popular form of ride-sharing. Metro-oriented DBS systems have the potential to promote sustainable transportation. However, the availability of DBS near metro stations often suffers from either scarcity or overabundance. To investigate the factors contributing to this imbalance, this paper examines the nonlinear influences and interactions that impact the DBS system near metro stations, with Shenzhen, China serving as a case study. An ensemble learning approach is employed to predict the imbalance state. Then, the machine learning interpretation method (i.e., SHapley Additive exPlanations) is used to quantify the contribution of effects, discover the strength of interactions between factors and uncover their underlying interactive connections. The results indicate the influence of external factors and the relations between pairwise variables (e.g., road density and the day of the week) for each imbalanced state. Provide two quantized sets of factors that can result in the supply-demand imbalance and support future transport planning decisions to enhance the accessibility and sustainability of Metro-oriented DBS systems.

The environmental benefits of dockless bike sharing systems for commuting trips

Using Shanghai, China as an example, we develop a new approach for assessing the environmental benefits of dockless bike sharing system (DLBS), which combines both big data and small data and consider both substitutive modal shifts and complementary modal shifts related to DLBS. With a GPS-based big dataset on DLBS trips and a survey of DLBS commuters, we examine the impacts of the built environment on DLBS usage and modal shift behavior of DLBS commuters. We estimate the environmental benefits of DLBS as the difference in carbon emissions between DLBS trips and their counterfactuals assuming DLBS was unavailable. We find that a DLBS commuting trip on average reduces carbon emission by 185.48 g, much higher than the previous estimates based solely on substitutive modal shifts. The research findings could support policy formation to achieve carbon neutrality by promoting DLBS.

How Many Are Too Many? Analyzing Dockless Bike-Sharing Systems with a Parsimonious Model

Using a parsimonious model, this paper analyzes a dockless bike-sharing (DLB) service that competes with walking and a generic motorized mode. The DLB operator chooses a fleet size and a fare schedule that dictate the level of service (LOS) as measured by the access time or the walking time taken to reach the nearest bike location. The market equilibrium is formulated as a solution to a nonlinear equation system over which three counterfactual design problems are defined to maximize (i) profit, (ii) ridership, or (iii) social welfare. The model is calibrated with empirical data collected in Chengdu, China, and all three counterfactual designs are tested against the status quo. We show the LOS of a DLB system is subject to rapidly diminishing returns to the investment on the fleet. Thus, under the monopoly setting considered herein, the current fleet cap set by Chengdu can be cut by up to three quarters even when the DLB operator aims to maximize ridership. This indicates the city’s fleet cap decision might have been misguided by the prevailing conditions of a competitive yet highly inefficient market. For a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator’s objective. When the operator focuses on profit, limiting fare is much more effective than limiting fleet size. If, instead, it aims to grow market share, then setting a limit on fleet size becomes a dominant strategy. We also show, both analytically and numerically, that the ability to achieve a stable LOS with a low rebalancing frequency is critical to profitability. A lower rebalancing frequency always rewards users with cheaper fares and better LOS even for a profit-maximizing operator. Funding: This research was partially supported by the U.S. National Science Foundation [Grant CMMI 1922665] and the National Natural Science Foundation of China [Grant 71971044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0304 .

The spatial-temporal evolution of public bike-sharing systems in China: The disruption of dockless bike-sharing emergence

Many Chinese cities have implemented the station-based public bike-sharing systems (PBSs). However, the recent spread of the dockless bike-sharing systems (DBSs) has significantly impacted PBSs, which has not been thoroughly investigated in previous research. This study bridges this research gap by examining the spatiotemporal evolution of China’s PBSs, with a particular focus on the changes brought by the entry of DBSs, by analyzing data on the start and end dates of PBSs and DBSs across the country. We utilized logistic regression to identify factors that may influence the decision to discontinue PBS operations, and examined selected representative cities to uncover the underlying structural and political factors affecting PBSs’ persistency. Findings reveal that the entry of DBSs disrupts PBS operations in many cities. PBSs are less likely to cease operation in cities with subways and in coastal areas, but more likely to do so in densely populated cities. Furthermore, technical advancements, operational consistency, adherence to government guidelines, and a well-planned station layout contribute to the continued viability of PBSs amidst the challenges posed by DBSs. Based on these results, we suggest that cities should improve the competitiveness of PBSs through better integration with public transit and optimization of PBS operations.

Unraveling the mode substitution of dockless bike-sharing systems and its determinants: A trip level data-driven interpretation

Understanding the mode substitution of shared micro-mobility systems is essential for assessing their societal and environmental impact and developing improvement planning instruments. This study carries out a fine-grained analysis of the mode substitution of dockless bike sharing (DLBS) in relation to other transport modes at the trip level, leveraging multi-modal route planning techniques, transaction data of bike-sharing, and travel behavior modeling. More importantly, the study leverages interpretable machine learning to reveal the complex effects of built environment factors on the mode substitution patterns of DLBS based on multiple data sources. The results indicate that the probabilities of DLBS replacing other transport modes present large heterogeneity among different trips and in different urban contexts, which can be successfully quantified by the proposed approach at the trip level. The average substitution rates of bike-sharing to bus, metro, walking and ride-hailing in Shanghai are estimated to be 0.356, 0.116, 0.347 and 0.181, respectively. Built environment factors such as presence of transit systems can explain the variations in the substitution rates of DLBS to a certain transport mode in different urban contexts. Especially, the effects of some built environment factors show complex nonlinear and threshold patterns revealed by the data-driven method. The effects of key built environment factors are quantitatively interpreted and their practical implications discussed.

Understanding dockless bike-sharing spatiotemporal travel patterns: Evidence from ten cities in China

Dockless bike-sharing (DBS) has gained popularity in many cities in recent years. However, little attention has been given to the commonalities and differences in DBS travel patterns from multiple cities. This study aims to fill this gap by comparing DBS travel patterns in ten Chinese cities of varying scales. It collected data from these cities and analysed them from two perspectives: service usage characteristics and complex network properties. The results reveal a coexistence of similarities and differences in DBS travel patterns across different cities. The ten major cities show significant similarities in temporal distribution, with a similarity coefficient exceeding 0.7. Southern cities have a higher percentage of night riding compared to their northern counterparts. Moreover, over 85% of the travel distances are within 2 km. Concerning spatial distribution, all ten cities demonstrate an imbalanced demand distribution during peak hours, particularly in downtown areas, exhibiting evident tidal patterns. Network analysis outcomes demonstrate that the DBS networks in all ten cities possess small-world and scale-free properties. Furthermore, nodes with high degree centrality and closeness centrality are predominantly concentrated in downtown areas, displaying a diminishing trend from the city centre to the outskirts and showcasing strong global spatial autocorrelation. The betweenness centrality exhibits a property of random distribution. Nodes with high PageRank values are mainly concentrated in the city centre and around metro stations. The findings offer valuable insights for transportation planners and managers seeking to enhance their understanding of DBS systems.

Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS.

Column-and-constraint-generation-based approach to a robust reverse logistic network design for bike sharing

Dockless bike-sharing systems have grown rapidly in China in recent years. A large number of shared bikes have been put into use, which results in a serious problem of uncollected faulty bikes. However, since the exact number of faulty shared bikes cannot be known a priori, a reverse logistic network must be designed strategically to reduce the total costs of the collection process. This paper studies a robust reverse logistic network design problem of shared bikes that integrates the locations of recycling centres and the planning of collection routes in uncertain collection demand situations. A two-stage robust location routing model is presented to capture the uncertainty of collection demands, and then a set-partition reformulation is presented to address the computationally intractable model. We develop a column-and-constraint-generation-based algorithm combining a tailored column generation algorithm to efficiently solve the proposed model. Numerical experiments and parameter analyses, which are based on real data from bike-sharing companies, are conducted to evaluate the performance of the proposed algorithm. A case study is presented to further analyse the spatial features of the reverse logistic network of shared bikes. Benefits of applying robust optimization are demonstrated experimentally in terms of total cost under uncertain collection demand.

Comparing the Travel Behavior of Older Users Between Station-Based and Dockless Bike Sharing Systems

As the population worldwide continues to age, providing improved mobility services for older people becomes increasingly crucial. In recent years, the maturation of bike sharing has led older people to progressively incorporate it into their everyday lives. In this study, station-based bike sharing (SBBS) and dockless bike sharing (DBS) are investigated in relation to older population mobility. This research relies on the fusion of multiple data sources, including SBBS and DBS trip records, point of interest (POI), and meteorological conditions in Nanjing, China. A zero-inflated negative binomial regression model is used to compare the travel behaviors among various age groups with the exposure variable. SBBS outperforms DBS in relation to age equity, while diverse travel behavior patterns are exhibited between the age groups. Although younger users are the majority of users, older users travel more frequently than younger users in bike sharing services. Also, older users are more likely to use shared bikes near the religious and health POIs, as well as in higher population density areas. This study provides beneficial insights into the transport equity and service improvement of bike sharing systems.

Exploring spatio-temporal pattern heterogeneity of dockless bike-sharing system: Links with cycling environment

Exploring spatio-temporal pattern heterogeneity of dockless bike-sharing system: Links with cycling environment

Electric Fences for Dockless Bike-Sharing Systems: An Electric Fence-Planning Framework for a Dockless Bike-Sharing System Based on a Land Parcel Subdivision and Regional Coverage Maximization

Dockless bike sharing (DBS) is convenient for bike users because it can solve their last-mile problems. However, dockless bikes that are disorderly parked in public spaces can disturb normal traffic. In planning electric fences for DBS systems, the maximum coverage location problem (MCLP) identifies locations suitable for electric fences. However, the MCLP model ignores the regional boundaries of bike use and causes substantial errors in coverage assessments. In this article, we present a novel framework for planning electric fences, accounting for both trip distribution and area shapes. We first introduce a dynamic land parcel subdivision algorithm, which can reasonably divide the city into fine-scale regions concerning the distributions of spatial parking demands. Then, we formulate the electric fence-planning problem as a regional coverage maximization problem. We develop an accelerated maximum coverage model with complementary coverage (MCMCC) to locate electric fence areas, which can improve solution accuracy and reliability. The DBS data of Shenzhen, China, are used to validate the framework. The study findings indicate that MCMCC produces more robust and accurate results than the MCLP model because the planning solutions are not affected by changes in the shapes and sizes of the regions. The framework can also automatically and accurately determine electric fence capacities and locations.