Search Results (42)

Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, it is meaningful to explore the relationship between the built environment and bike-sharing ridership. This study proposes a novel framework integrated with the extreme gradient boosting tree model to evaluate the impacts and threshold effects of the built environment on the origin–destination bike-sharing ridership. The results show that most built environment features have strong nonlinear effects on the bike-sharing ridership. The bus density, the industrial ratio, the local population density, and the subway density are the key explanatory variables impacting the bike-sharing ridership. The threshold effects of the built environment are explored based on partial dependence plots, which could improve the bike-sharing system and provide policy implications for green travel and sustainable transportation. Full article

Existing studies have limited evidence about the complex nonlinear impact mechanism of road network topology and built environment on bike-sharing systems’ greenhouse gas (GHG) emission reduction benefits. To fill this gap, we examine the nonlinear effects of road network topological attributes and built environment elements on the potential GHG emission reduction of dockless bike-sharing (DBS) trips in Shenzhen, China. Various methods are employed in the research framework of this study, including a GHG emission reduction estimation model, spatial design network analysis (sDNA), gradient boosting decision tree (GBDT), and partial dependence plots (PDPs). Results show that road network topological variables have the leading role in determining the potential GHG emission reduction of DBS trips, followed by land use variables and transit-related variables. Moreover, the nonlinear impacts of road network topological variables and built environment variables show certain threshold intervals for the potential GHG emission reduction of DBS trips. Furthermore, the impact of built environment on the potential GHG emission reduction of DBS trips is moderated by road network topological indicators (closeness and betweenness). Compared with betweenness, closeness has a greater moderating effect on built environment variables. These findings provide empirical evidence for guiding bike-sharing system planning, bike-sharing rebalancing strategy optimization, and low-carbon travel policy formulation. Full article

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. Full article

In the quest to enhance urban green mobility and promote sustainable leisure activities, this study presents a comprehensive analysis of the potential for cycling greenways within the urban fabric of Chengdu, China. Leveraging the built environment and cycling routes, simulated by dockless bike-sharing (DBS) big data on weekend afternoons, the cycling flow on existing networks reflects the preference for leisure cycling in surroundings, thus indicating the potential for future enhancements to cycling greenway infrastructure. Employing Multi-Scale Geographically Weighted Regression (MGWR), this research captures the spatial heterogeneity in environmental factors influencing leisure cycling behaviors. The findings highlight the significant roles of mixed land use, network diversity, public transit accessibility, human-scale urban design, road network thresholds, and the spatially variable impacts of architectural form in determining cycling greenway potential. This study culminates with the development of an evaluation model, offering a scientific approach for cities to identify and prioritize the expansion of cycling infrastructure. Contributing to urban planning efforts for more livable and sustainable environments, this research underscores the importance of data-driven decision-making in urban green mobility enhancement by accurately identifying and efficiently upgrading infrastructure guided by public preferences. Full article

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. Full article

Sustainable urban mobility is an imperative concern in contemporary cities, and shared micromobility systems, such as docked bike-sharing, dockless bike-sharing, and dockless e-scooter-sharing, are recognized as essential contributors to sustainable behaviors in cities, both complementing and enhancing public transport options. Most of the literature on this subject predominantly focuses on individual assessments of these systems, overlooking the comparative analysis necessary for a comprehensive understanding. This study aims to bridge this gap by conducting a spatiotemporal analysis of two different shared micromobility modes of transportation, docked bike-sharing systems and dockless e-scooter-sharing systems operating in the municipality of Lisbon. The analysis is further segmented into arrivals and departures on weekdays and weekends. Additionally, this study explores the impact of sociodemographic factors, the population’s commuting modes, and points of interest (POIs) on the demand for both docked bike-sharing and dockless e-scooter-sharing. Multiscale Geographically Weighted Regression (MGWR) models are employed to estimate the influence of these factors on system usage in different parishes in Lisbon. Comparative analysis reveals that the temporal distribution of trips is similar for both docked bike-sharing and dockless e-scooter-sharing systems on weekdays and weekends. However, differences in spatial distribution between the two systems were observed. The MGWR results indicate that the number of individuals commuting by bike in each parish has a positive effect on docked bike-sharing, while it exerts a negative influence on dockless e-scooter-sharing. Also, the number of commercial points of interest (POIs) for weekday arrivals positively affects the usage of both systems. This study contributes to a deeper understanding of shared micromobility patterns in urban environments and can aid cities in developing effective strategies that not only promote and increase the utilization of these shared micromobility systems but also contribute to sustainable urban mobility. Full article

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model. Full article

As shared micromobility (bikes and scooters) has proliferated throughout urban areas, there has been growing interest in how it facilitates connections with rail transit systems. This study explores the magnitude of interactions between shared micromobility and rail public transit systems using shared micromobility trip data and rail transit schedule data. We evaluate over one million trips from October 2019 to February 2020 in four California cities (San Francisco, Los Angeles, Sacramento, and San Jose) and develop criteria to identify trips connecting to rail transit. These include spatial and temporal rules, such as whether a trip starts/terminates close to public transit stations and whether a trip takes place when transit systems are operating. The criteria are examined via sensitivity analyses. The results indicate the degree of interaction between rail public transit and shared micromobility varies across cities and systems (i.e., docked/dockless). Most connections take place in the downtown or around public transit hubs. About 5–20% of all shared micromobility trips are identified as accessing or egressing from rail transit. These connecting trips exhibit commute-driven patterns and greater measured velocities. We conclude by examining the applicability of incorporating schedule information into the identification process of shared micromobility trips connecting to rail transit systems. Full article

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. Full article

Dockless bike-sharing has experienced explosive growth, establishing itself as an integral component of urban public transport systems. Challenges such as parking violations have spurred operators and users to pursue standardized management. While electronic parking spots are employed to promote standard parking, suboptimal parking layouts can lead to illegal parking. Inadequate post-violation penalties fail to achieve standard parking, while excessive punishment diminishes user engagement. This study combines parking spot density and penalties to incentivize standard parking, and Beijing, China, was selected as the research object. Using an SP questionnaire survey, a binary logistic model analyzes bike-sharing users’ standard parking behavior and willingness to adhere to different rules. Findings reveal that optimal walking distances range from 300 to 450 m for service levels and exceed 400 m for service efficiency. Influential factors include gender, age, occupation, usage behavior, and travel preferences. Users with high-frequency, low-convenience expectations, low travel costs, and flexible travel exhibit strong adherence. Additionally, user acceptance of the maximum distances without penalties follows an exponential distribution, with 80% accepting 400 m and 40% accepting 800 m. Enforcement has a visible effect within 300 m, but diminishes with longer distances. Excessive penalties result in significant user loss. Full article

Recently, the rapid development of the bike-sharing system (BSS) has dramatically influenced passengers’ travel modes. However, whether the relationship between the BSS and public transit is competitive or complementary remains unclear. In this paper, a difference-in-differences (DID) model is proposed to figure out the impact of the dockless BSS (DBSS) on bus ridership. The data was collected from Shanghai, China, which includes data from automatic fare collection (AFC) systems, automatic vehicle location (AVL) systems, DBSS transaction data, and point-of-interest (POI) data. The research is based on the route-level, and the results indicate that shared bikes have a substitution impact on bus ridership. Regarding all the travel distance, each shared bike along the route leads to a 0.39 decrease in daily bus ridership on the weekdays, and a 0.17 decrease in daily bus ridership on the weekends, respectively, indicating that dockless shared bikes lead to a stronger decrease in bus ridership on weekends compared to weekdays. Additionally, the substitution effects of shared bikes on bus ridership gradually decays from 0.104 to 0.016 in daily bus ridership on weekends, respectively, with the increase in the travel distance within 0–3 km. This paper reveals that the travel distance of passengers greatly influences the relationship between the DBSS and public transit on the route level. Full article

While a growing body of studies has investigated the collaborative governance (CG) of dockless bike-sharing schemes (DBSS) worldwide, few offer close descriptions and analyses of stakeholder interactions in specific social contexts. Our study fills this gap by examining the development of CG of DBSS in Sydney, Australia between 2017 and 2020. The methodology is guided by an Integrative Framework for CG, drawing on qualitative analysis of policy documentation and semi-structured interviews with key DBSS participants from the public and private sector. Our findings reveal context-specific drivers and dynamics that shaped the development of particular forms of CG within Sydney’s DBSS. Full article

Bike-sharing systems implemented in cities with good bike lane networks could potentiate a modal shift from short car trips, boosting sustainable mobility. Both passenger and last-mile goods transportation can benefit from such systems and, in fact, bike sharing (dockless or with docking stations) is increasing worldwide, especially in Europe. This research focused on a European city, Lisbon, and the e-bike sharing system GIRA, in its early deployment, in 2018, where it had about 409 bikes of which 30% were non-electric conventional bikes and 70% were e-bikes. The research aims at answering the main research questions: (1) What is the number of trips per day and travel time in conventional bikes and e-bikes?; (2) Do the daily usage peaks follow the trends of other modes of transport in terms of rush hours?; (3) Are there seasonality patterns in its use (weekdays and weekends, workdays and holiday periods)?; (4) How do climate conditions affect its use?; and finally, (5) What would be the impact on final energy consumption and GHG emissions? The dataset for 2018 regarding GIRA trips (distance, time, conventional or e-bike, docking station origin and destination) and weather (temperature, wind speed, relative humidity, precipitation) was available from Lisbon City Hall by means of the program “Lisboa aberta”. Data regarding the profile of the users (which trips GIRA replaces?) and data regarding electricity consumption were not available. The latter was estimated by means of literature e-bike data and electric motor specifications combined with powertrain efficiency. Greenhouse gas (GHG) emissions were estimated by using the latest Intergovernmental Panel on Climate Change (IPCC) CO₂ equivalents and a spreadsheet simulator for the Portuguese electricity GHG intensity, which was adaptable to other countries/locations. In a private car fleet dominated by fossil fuels and internal combustion engines, the e-bike sharing system is potentially avoiding 36 Ton GHG/year and reducing the energy consumption by 451 GJ/year. If the modal shift occurs from walking or urban bus to an e-bike sharing system, the impact will be detrimental for the environment. Full article

With a new generation of bike sharing services emerging, the development of dockless bike sharing services results in considerable socioeconomic and environmental benefits but also creates new issues, such as inappropriate parking behaviors and bike imbalances. To solve the inappropriate parking problem, electric fences have been introduced to guide users to park bikes in designated zones. Considering the role of electric fences in restricting user parking behaviors, an electric fence-based intelligent scheduling method for rebalancing dockless bike sharing systems is proposed in this paper. As a dynamic method that considers the real-time usage of bike sharing systems, an electric fence adjusts its capacity based on real-time information, which guides users to return bikes to electric fences with greater urgency. Because existing approaches require prespecified models and are unable to consider all the intricacies in the dynamic optimization problem, a model-free intelligent scheduling approach based on deep Q-learning that can adapt to the changing distributions of customer arrivals, available bikes, bike locations, and user travel times is used to solve the problem. Finally, a case study involving Beihang University is employed, which shows that the method performs well in rebalancing the bike sharing system and improving the mean utilization (MU) and customer satisfaction (CS). Full article

Bike-sharing is known as a sustainable form of transportation. This travel mode is able to tackle the “last mile” transit issue and deliver financial, well-being, and low-carbon lifestyle advantages to users. To date, many studies have analysed the influence of various factors, including built environments, on bike-sharing ridership. However, no study has exclusively synthesised these findings regarding the association between built-environment attributes and bike-sharing ridership. Thus, in this study, a systematic literature review was conducted on 39 eligible studies. These studies were assessed with respect to (1) bike-sharing usage, (2) studies’ geographical distribution, (3) data collection and analysis method, and (4) built environment factor type. Most studies were carried out in the US and Chinese cities. Variables associated with diversity, density, and distance to public transport stations and public transport infrastructure were frequently employed by the studies reviewed. It was found that BS stations with an average capacity of 24.63 docks and street network systems with an average length of 12.57 km of cycling lanes had a significant impact on the bike-sharing ridership. The findings of these studies were combined, and a series of recommendations were proposed based on them for bike-sharing service providers and researchers in academia. The findings of this evaluation can help practitioners and scholars understand the important built environment elements that influence bike-sharing ridership. Knowledge in this field will enable bike-sharing service providers to direct their resources sufficiently to enhance the more essential aspects of bike-sharing users’ satisfaction. Full article