Search Results (16)

As cities worldwide strive to promote healthy and sustainable non-motorized transport, the equity of dockless bike-sharing has become a central issue in urban transport planning. This study investigates the relationship between human-scale urban environmental perceptions and the equity of bike-sharing usage within 15-minute cycling catchments of metro stations. Using Shenzhen, China, as a case study, we integrated bike-share trip records from August 2021 (around 43 million trips), population grid data, and Baidu Street View images analyzed with deep learning models. The study first quantified the spatial inequality of bike-sharing usage within each metro catchment area using a per capita trip Gini coefficient. Subsequently, we assessed the correlation between these equity metrics and human-scale urban qualities quantified from street-level imagery. The findings reveal significant intra-catchment usage disparities, with some central urban station areas showing relatively equitable bike-sharing distribution (Gini as low as 0.37), while others, particularly on the urban fringe, exhibit highly inequitable patterns (Gini as high as 0.93). Spearman correlation analysis showed that catchments perceived as “livelier” and more “interesting” had significantly lower Gini coefficients, whereas other perceptual factors such as safety, beauty and wealth showed no significant linear relationship with equity. A Random Forest model further indicated that “liveliness” and “lack of boredom” are the strongest predictors of usage equity, highlighting the critical role of vibrant street environments in promoting equitable access. These findings bridge the fields of transportation equity and urban governance, suggesting that improving the human-scale environment around transit hubs, thereby making streets more engaging, safe, and pleasant, could foster more inclusive and equitable use of bike-sharing. Full article

Multimodal transportation systems, particularly those combining metro and bikesharing, have become central to addressing the first- and last-mile connectivity challenges in urban environments. This study presents a comprehensive data-driven framework to analyze the spatiotemporal interplay between metro and dockless bikesharing usage using real-world data from Tianjin, China. Two primary methods are employed: K-means clustering is used to categorize metro stations and bike usage zones based on temporal demand features, and non-negative Tucker decomposition is applied to a three-way tensor (day, hour, station) to extract latent mobility modes. These modes capture recurrent commuting and leisure behaviors, and their alignment across modes is assessed using Jaccard similarity indices. Our findings reveal distinct usage typologies, including mismatched (misalignment of jobs and residences), employment-oriented, and comprehensive zones, and highlight strong temporal coordination between metro and bikesharing during peak hours, contrasted by spatial divergence during off-peak periods. The analysis also uncovers asymmetries in peripheral stations, suggesting differentiated planning needs. This framework offers a scalable and interpretable approach to mining multimodal travel patterns and provides practical implications for station-area design, dynamic bike rebalancing, and integrated mobility governance. The methodology and insights contribute to the broader effort of data-driven smart city planning, especially in rapidly urbanizing contexts. Full article

Bike-sharing has been widely recognized for addressing the “last-mile” problem and improving commuting efficiency. While prior studies emphasize how the built environment shapes feeder trips, the effects of station types and spatial heterogeneity on bike-sharing and metro integration remain insufficiently explored. Taking the urban core area of Shenzhen as a case study, this paper examines how the built environment influences such integration during morning peak hours and how these impacts differ across station types. First, we proposed a “3Cs” (convenience, comfort, and caution) framework to capture key built environment factors. Metro stations were classified into commercial, residential, and office types via K-means clustering. Subsequently, the ordinary least squares (OLS) regression model and the multiscale geographically weighted regression (MGWR) model were employed to identify significant factors and explore the spatial heterogeneity of these effects. Results reveal that factors influencing bike-sharing–metro integration vary by station type. While land-use mix and enclosure affect bike-sharing usage across all stations, employment and intersection density are only significant for commercial stations. Furthermore, these influences exhibit spatial heterogeneity. For instance, at office-oriented stations, population shows both positive and negative effects across areas, while residential density has a generally negative impact. These findings enhance our understanding of how the built environment shapes bike-sharing–metro integration patterns and support more targeted planning interventions. Full article

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R² compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R² values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability. Full article

Dockless bike-sharing (DBS) plays a crucial role in solving the “last-mile” problem for metro trips. However, bike–metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike–metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike–metro transfer; (2) the built environment’s impact on bike–metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike–metro transfer, with residential and employment areas displaying different transfer patterns by time and transfer flow. Our findings underscore the importance of considering both built environment factors and spatial dependence in urban transportation planning to achieve sustainable and efficient transportation systems. 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

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

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

An essential function of dockless bikesharing (DBs) is to serve as a feeder mode to the metro. Optimizing the integration between DBs and the metro is of great significance for improving metro travel efficiency. However, the research on DBs–Metro Integration Cycling (DBsMIC) faces challenges such as insufficient methods for identification and low identification accuracy. In this study, we improve the enhanced two-step floating catchment area and incorporate Bayes’ rule to propose a method to identify DBsMIC by considering the parameters of time, distance, environmental competition ratio, and POI service power index. Furthermore, an empirical study is conducted in Shenzhen to verify the higher accuracy of the proposed method. Their spatiotemporal behavior pattern is also explored with the help of the kernel density estimation method. The research results will help managers improve the effective redistribution of bicycles, promote the coupling efficiency between transportation modes, and achieve sustainable development of urban transportation. Full article

Dockless bike-sharing systems have become one of the important transport methods for urban residents as they can effectively expand the metro’s service area. We applied the ordinary least square (OLS) model, the geographically weighted regression (GWR) model and the multiscale geographically weighted regression (MGWR) model to capture the spatial relationship between the urban built environment and the usage of bike-sharing connected to the metro. A case study in Beijing, China, was conducted. The empirical result demonstrates that the MGWR model can explain the varieties of spatial relationship more precisely than the OLS model and the GWR model. The result also shows that, among the proposed built environment factors, the integrated usage of bike-sharing and metro is mainly affected by the distance to central business district (CBD), the Hotels-Residences points of interest (POI) density, and the road density. It is noteworthy that the effect of population density on dockless bike-sharing usage is only significant at weekends. In addition, the effects of the built environment variables on dockless bike-sharing usage also vary across space. A common feature is that most of the built environment factors have a more obvious impact on the metro-oriented dockless bike-sharing usage in the eastern part of the study area. This finding can provide support for governments and urban planners to efficiently develop a bike-sharing-friendly built environment that promotes the integration of bike-sharing and metro. Full article

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics. Full article

Like many other transit modes, the metro provides stop-to-stop services rather than door-to-door services, so its use undeniably involves first- and last-mile issues. Understanding the determinants of the first- and last-mile mode choice is essential. Existing literature, however, mostly overlooks the mode choice effects of traffic safety perception and attitudes toward the mode. To this end, based on a face-to-face questionnaire survey in Shenzhen, China, this study uses the two-sample t-test to confirm the systematic differences in traffic safety perception and attitudes between different subgroups and develops a series of multinomial logistic (MNL) models to identify the determinants of first- and last-mile mode choice for metro commuters. The results of this study show that: (1) Walking is the most frequently used travel mode, followed by dockless bike-sharing (DBS) and buses; (2) Variances in traffic safety perception and attitude exist across gender and location; (3) Vehicle-related crash risks discourage metro commuters from walking to/from the metro station but encourage them to use DBS and buses as feeder modes; (4) DBS–metro integration is encouraged by the attitude that DBS is quicker than buses and walking, and positive attitudes toward the bus and DBS availability are decisive for the bus–metro and DBS–metro integration, respectively; and (5) Substantial differences exist in the mode choice effects of traffic safety perception and attitudes for access and egress trips. This study provides a valuable reference for metro commuters’ first- and last-mile travel mode choice, contributing to developing a sustainable urban transport system. Full article

Promoting a transition in individuals’ travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro–bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro–bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro–bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers’ use frequency of integrated metro–bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems. Full article

Metro–bikeshare integration is considered a green and efficient travel model. To better develop such integration, it is necessary to monitor and analyze metro–bikeshare transfer characteristics. This paper measures access and egress transferring distances and catchment areas based on smartcard data. A cubic regression model is conducted for the exploration of the 85th access and egress network-based transferring distance around metro stations. Then, the independent samples t-test and one-way analysis of variance (ANOVA) are used to explore access and egress transfer characteristics in demographic groups and spatial and temporal dimension. Additionally, the catchment area is delineated by applying both the network-based distance method and Euclidean distance method. The result reveals that males outcompete females both in access and egress distances and urban dwellers ride a shorter distance than those in suburban areas. Access and egress distances are both shorter in morning peak hours than those in evening peak hours and access distance on weekdays is longer than that on weekends. In addition, network-based catchment area accounts for over 90% of Euclidean catchment area in urban areas, while most of the ratios are less than 85% in suburban. The paper uses data from Nanjing, China as a case study. This study serves as a scientific basis for policy makers and bikeshare companies to improve metro–bikeshare integration. Full article

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable. Full article