Dockless Bike-sharing System Research Articles

Emergence of scaling in dockless bike-sharing systems for bike choice behavior

Emergence of scaling in dockless bike-sharing systems for bike choice behavior

A Locational Demand Model for Bike-Sharing

Problem definition: Micromobility systems (bike-sharing or scooter-sharing) have been widely adopted across the globe as a sustainable mode of urban transportation. To efficiently plan, operate, and monitor such systems, it is crucial to understand the underlying rider demand—where riders come from and the rates of arrivals into the service area. They serve as key inputs for downstream decisions, including capacity planning, location optimization, and rebalancing. Estimating rider demand is nontrivial as most systems only keep track of trip data, which are a biased representation of the underlying demand. Methodology/results: We develop a locational demand model to estimate rider demand only using trip and vehicle status data. We establish conditions under which our model is identifiable. In addition, we devise an expectation-maximization (EM) algorithm for efficient estimation with closed-form updates on location weights. To scale the estimation procedures, this EM algorithm is complemented with a location-discovery procedure that gradually adds new locations in the service region with large improvements to the likelihood. Experiments using both synthetic data and real data from a dockless bike-sharing system in the Seattle area demonstrate the accuracy and scalability of the model and its estimation algorithm. Managerial implications: Our theoretical results shed light on the quality of the estimates and guide the practical usage of this locational demand model. The model and its estimation algorithm equip municipal agencies and fleet operators with tools to effectively monitor service levels using daily operational data and assess demand shifts because of capacity changes at specific locations. Funding: This work was supported by The Pacific Northwest Transportation Consortium (PacTrans) [Grant 69A3551747110]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0306 .

Relationship between POI configurations and environmental benefits of dockless bike-sharing system: A case study of Shenzhen

Relationship between POI configurations and environmental benefits of dockless bike-sharing system: A case study of Shenzhen

Unveiling Disparities in Bicycling Mobility Patterns Across Socioeconomic Statuses: A Framework for Identifying User Profiles in Dockless Bike-Sharing Systems

Cycling, as a healthy and low-carbon nonmotorized form of mobility, plays a crucial role in promoting the sustainable development of urban transportation. Thus, fostering more equitable cycling for residents has become a critical topic in current transportation research. The relationship of individual socioeconomic status (SES) in cycling mobility patterns has not been sufficiently explored, however. To address this, we introduce a framework for identifying individual social and activity portraits by fusing DBS trip data with user ID and housing price data. Leveraging this framework, we systematically investigate disparities in daily mobility patterns among cycling groups of varying SES. Specifically, we extract thirteen individual mobility indicators across four dimensions—trip purpose, time, extent, and intensity — to comprehensively characterize individual cycling patterns. Meanwhile, we use housing prices as a proxy to infer users’ SES, categorizing them into nine distinct groups. By aggregating the mobility indicators for each group, we conduct a comparative analysis of SES and daily cycling patterns. Taking Shenzhen, China, as a case study, we find that SES significantly influences the daily activity spaces of DBS users, with urban villages and older communities in the central city serving as notable exceptions. Additionally, lower SES cyclists tend to face greater daily travel burdens and shorter personal disposable time, as reflected in higher travel costs, a larger share of commuting trips, and even longer working hours. In contrast, upper SES users demonstrate higher noncommuting trip demand, characterized by a lower proportion of commuting trips and higher shares of weekend trips. These findings enhance our understanding of equity issues with nonmotorized transportation in megacities and offer valuable insights for optimizing active transport planning.

Mining individual daily commuting patterns of dockless bike-sharing users: A two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees

Mining individual daily commuting patterns of dockless bike-sharing users: A two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees

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

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

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

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

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

The environmental benefits of dockless bike sharing systems for commuting trips

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

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

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

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