Search Results (398)

Urban congestion is simultaneously influenced by heterogeneous spatio-temporal travel demands, the topology and spatial characteristics of road networks, and the interplay between multiple travel modes. As a critical component of solutions towards a greener and more sustainable transportation, bike-sharing systems have great potential in reducing carbon emissions, improving public health, and alleviating congestion by substituting short-distance motorized trips. Benefiting from flexible accessibility and usage, dockless bike-sharing has gained wide popularity and revived the fashion of cycling in cities. In this study, we reveal that the widely adopted detour ratio alone cannot effectively reflect congestion levels at the route level. Using large-scale dockless bike-sharing data and taxi trajectory data in Beijing, we quantitatively examine the relationships between cycling flow, motor vehicle traffic and road network structure. In addition, the proposed cycling-traffic-weighted detour ratio can prescreen potentially inefficient cycling routes, which can assist targeted infrastructure optimization and evidence-based urban planning. Full article

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities. Full article

Predicting origin–destination flows in high-density bike-sharing systems remains challenging due to the lack of models that jointly capture temporal dynamics and behavioral variability in urban mobility. In this study, we introduce a spatio-temporal optimal transport framework with dynamically calibrated behavioral regularization that integrates physical network costs with historical mobility priors to infer latent behavioral structure in trip patterns. Unlike static or purely predictive approaches, the proposed framework captures temporal spillovers across hourly intervals, allowing for the continuous evolution of mobility flows. We reinterpret the regularization parameter as a behavioral persistence indicator governing the trade-off between cost minimization and prior adherence. This parameter is dynamically calibrated over a 12-month period using Kullback–Leibler divergence from historical priors, enabling a behavioral diagnostic perspective on mobility regimes. Empirically, we uncover statistically significant regime shifts: weekday mobility is dominated by cost-efficient flows, whereas weekend behavior exhibits stronger adherence to historical mobility patterns and greater variability. We further identify systematic weather-related modulation, with adverse conditions associated with reduced behavioral persistence and patterns consistent with a contraction of discretionary mobility. These findings demonstrate that the proposed framework yields an interpretable behavioral metric for urban mobility systems. This has implications for adaptive mobility management, enabling data-driven rebalancing strategies that respond to temporal variation in behavioral regimes. Full article

High-resolution GPS trajectories pose a geospatial processing challenge: transforming temporally ordered observations into structured spatial representations that retain intra-trip state transitions at metropolitan scale. This study develops and validates a scalable geospatial transformation workflow for detecting and structuring recurrent mid-trip stops from large-scale trajectory data. Using approximately 97 million GPS observations from Seoul’s public bike-sharing system, stopping episodes are identified through speed-based segmentation and density-based spatial clustering (DBSCAN). Recurrent stopping hotspots are attributed with spatial context via a land-use overlay and proximity analysis to pedestrian crossings. Sequential transitions between recurrent hotspots are represented as directed and weighted hotspot-to-hotspot networks, whose structural properties are evaluated using connectivity, clustering, path length, and modularity metrics under degree-preserving randomization. The workflow emphasizes explicit parameterization and modular processing, aligning with reproducible GIS-based spatial analytical frameworks. By converting fine-grained trajectory observations into validated mesoscopic connectivity representations, the framework provides a transferable geospatial processing pipeline for extracting structured connectivity information from high-resolution trajectory datasets. Full article

In the context of escalating global warming and the urban heat island effects, recurrent extreme heat events will increase the exposure risk of cyclists, which will have a detrimental effect on both health and the sustainability of active mobility. Nevertheless, this risk has not been given sufficient attention. To accurately quantify the levels of solar radiation exposure experienced by cyclists in high-temperature conditions and the impact of the built environment on these levels, this study focuses on central Shanghai as a case study. The integration of Mobike trajectories, street view imagery, and solar radiation data sets enabled the quantification of trip-level cumulative radiation exposure and per-minute exposure levels. Subsequently, the XGBoost–SHAP interpretability framework was employed to decipher the mechanisms of the built environment. The following key findings have been identified: (1) Spatiotemporally, the radiation exposure level of cyclists exhibited an inverted U-shaped pattern, peaking at midday (10:00–15:00), with per-minute values of 862–943 W/m². This intensity significantly exceeded that observed during the morning (407 W/m²) and evening (253 W/m²). (2) It was determined that geometric factors dominated the radiative exposure level. The shading index demonstrated a critical influence (57% contribution), with exposure reduction intensifying beyond 0.41 yet exhibiting diminishing marginal effects after 0.6. The sky view factor and building height elevated exposure risk by amplifying direct solar radiation. (3) Socioeconomic factors had divergent effects on the radiation exposure level of cyclists: commercial/business densities reduced exposure through continuous building shade, whereas transportation facility density increased exposure due to low-shaded layouts. Consequently, this study proposes “shaded corridors” as a core mitigation strategy, establishing a tripartite intervention framework (spatial-facility-governance) for radiation exposure reduction. The present study provides scientific foundations for the targeted enhancement of heat resilience in active mobility. Full article

This paper examines the subjective annoyance associated with shared-bike electronic alert sounds (SBeASs), an emerging urban noise source. A study was conducted by employing extensive questionnaire surveys and psychoacoustic experiments. A preliminary survey (N = 1340) indicated that 90.6% of participants reported being impacted by SBeASs, with pronounced effects on nighttime rest and daytime work efficiency. In this study, SBeAS samples were taken from three prominent Chinese bike-sharing brands: Hello Bike, Meituan Bike, and DiDi Bike. Under laboratory conditions, subjective annoyance assessments (N = 28) for SBeASs were conducted at controlled sound pressure levels (SPLs) ranging from 45 to 65 dBA, with occurrence frequencies of 1, 3, and 5 s. Simultaneously, annoyance assessments were also conducted for two reference noise types: traffic noise and street noise. The results indicated a notable increase in annoyance levels related to SBeASs with rising SPL and increased occurrence frequency. Minor variations in annoyance were identified among different bike-sharing brands, which can be attributed to their distinct acoustic features. When the SPL was above 55 dBA, the DiDi Bike SBeASs produced considerably higher annoyance than those of other brands. This can be attributed to its elevated low-frequency energy, loudness, and roughness. Moreover, individuals exhibiting increased sensitivity to noise reported notably higher annoyance ratings on the SBeAS scale (p = 0.019). Under low-SPL conditions (45–55 dBA), the annoyance attributed to frequent SBeASs can exceed that caused by traffic noise and street noise at comparable SPLs, highlighting the distinct disruptive impact of abrupt sound sources. Full article

As a key link between short-distance urban mobility and public transport, dockless bike-sharing (DBS) systems have expanded rapidly in recent years. However, existing studies are limited by insufficient factor coverage, incomplete temporal analysis, and inadequate assessment of spatial-scale effects. To address these gaps, this study uses Shenzhen as a case study, integrating 40 million DBS trip records from August 2021 with multi-source geospatial data to develop a spatiotemporal analytical framework. First, it examines differences in riding patterns between weekdays and weekends, further segmenting trips into six time periods to capture intra-day temporal variations. Through multicollinearity and spatial autocorrelation tests, a 700-m grid was identified as the optimal analysis unit. Subsequently, a Multi-scale Geographically Weighted Regression (MGWR) model quantified how multiple sources of factors collectively shape DBS usage behavior. Results indicate that higher frequency, faster speeds, and longer distances during peak periods characterize weekday trips. Office POIs and transit accessibility positively affect DBS usage during weekday peaks, whereas Residential POIs and Convenience Service POIs have a greater influence on weekend trips. Population density and land-use mix consistently promote DBS use across all periods. Younger residents (<30 years) were the main users, especially during weekday peak and weekend no-peak periods, whereas gender and education had limited impact. These findings provide empirical evidence to optimize bike-sharing deployment, enhance multimodal transport integration, and support sustainable urban mobility planning. Full article

Bike sharing is an established component of urban mobility infrastructure, offering a low-emission alternative to motorized transport for short trips in cities worldwide. Accurate demand forecasting is essential for efficient system operation: it enables better bike redistribution, reduces user wait times, and lowers the operational costs associated with rebalancing. This study evaluated multiple ensemble strategies for hourly bike-sharing demand prediction, comparing bagging methods (Random Forest, Extra Trees), boosting methods (AdaBoost, Gradient Boosting Regressor, Histogram-based Gradient Boosting Regressor), and a Voting ensemble, while systematically investigating the impact of hyperparameter optimization. A repeated hold-out protocol was used, in which the dataset was randomly divided into 80% training and 20% test subsets across 10 random splits; 5-fold cross-validation was applied within each training fold exclusively for hyperparameter tuning, ensuring the test set remained unseen during model selection. Random Search and Bayesian Optimization were compared under identical budgets of 60 configurations per model. Results show that optimization substantially improves all models, with the most pronounced gains for AdaBoost (58% RMSE reduction) and Gradient Boosting Regressor (45% RMSE reduction). A Voting ensemble combining a Random Search-tuned Gradient Boosting Regressor and a Bayesian-optimized Histogram-based Gradient Boosting Regressor achieves the best overall performance (RMSE of 38.48, R² of 0.955) with the lowest variance among all repeated splits. Feature importance analysis confirms that hour of day and temperature are the dominant demand drivers, consistent with the operational patterns of urban bike-sharing systems. The performance difference between Random Search and Bayesian Optimization is negligible for most models, suggesting that well-designed search spaces allow simpler strategies to achieve competitive results. A controlled comparison conducted under identical experimental conditions shows that the Voting ensemble is statistically equivalent to XGBoost and nominally better than LightGBM, while CatBoost achieves a statistically significant advantage, highlighting it as a strong individual alternative. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

The rise of shared e-bike systems presents a promising solution for sustainable urban mobility, yet their operational efficiency is often hampered by unpredictable user demands. This challenge directly impacts the achievement of SDG 11 by creating service inconsistencies that can deter users. To address this, we propose a data-driven methodology for optimizing resource allocation in shared e-bike systems. Based on large-scale trip data from Ningbo, China, our analysis reveals significant spatiotemporal demand regularities at a fine-grained, cell-based level, including pronounced commuting peaks and clear spatial heterogeneity between high- and low-demand zones. Building upon these findings, we implement a SARIMAX model to generate accurate, hourly, day-ahead demand forecasts that incorporate key contextual information. Our results indicate that the SARIMAX model provides substantial improvements in predictive accuracy while offering superior interpretability and practical computational efficiency. The resulting forecasts enable data-informed decision-making for critical operations such as fleet rebalancing, battery swapping, and parking zone management. This study provides a robust and routine transparent tool for shared mobility operators, demonstrating how industrial engineering principles and statistical modeling can directly enhance the sustainability and user experience of urban transportation systems. Full article

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, ToB ignores network flow effects while bicycles departing from the same location may reach destinations with vastly different ToB values. To overcome this, we propose a flow-integrated ToB (FwToB) index that incorporates the idle time at both the trip origin and destination. Applying this index to central Beijing reveals significant spatial heterogeneity while maintaining the original core-periphery pattern, indicating that most bicycles flow to areas with similar efficiency. Geographically weighted regression further shows that factors like population density, healthcare, shopping facilities, and distance to metro stations influence efficiency with substantial spatial non-stationarity. These findings advance the understanding of bike-sharing efficiency and offer insights for operators and urban planners. Full article

Bike-sharing use has been growing because it improves personal mobility, offers an alternative to walking, and strengthens connections to transit. Demand forecasting is crucial for bike-sharing services because it enables operators to anticipate empty stations and full docks, improve vehicle rebalancing and staffing, and deliver more reliable service at lower operating cost. In this paper, we propose a cluster-based, hour-ahead demand forecasting methodology that (1) groups stations into geographically coherent areas using K-means clustering method, (2) constructs hourly arrival and departure demand time series for each cluster while explicitly preserving zero-demand hours, and (3) incorporates exogenous factors such as temperature and weather-event type. We analyze multi-year trip records from Chicago’s Divvy bike-sharing system (2014–2017) to characterize network expansion and assess spatial stability over time. We then use the period (1 August 2016–31 December 2017), during which the number of active stations is stable, to conduct our predictive modeling. We compare three machine learning-based predictive models—linear regression (LR), time series (TS), and random forest (RF)—and assess their out-of-sample performance using the root mean squared error (RMSE). Results show that TS and RF models consistently outperform LR, achieving up to 80% $R^2$ values and substantially lower RMSE across all 10 clusters, with particular improvements in high-variability central areas. By forecasting net demand (arrivals minus departures) at the cluster level, the approach supports practical identification of likely surplus/deficit areas to guide rebalancing decisions. Full article

Rebalancing shared bikes poses a significant challenge for dockless bike-sharing (DLBS) operators, as inevitable spatiotemporal mismatches between demand and supply lead to high redistribution costs. Despite its operational significance, empirical research on the spatiotemporal imbalance of DLBS usage and its underlying drivers remain limited. Utilizing one month’s extensive trajectories of shared bikes in Shanghai, China, this study quantifies DLBS net flows at fine-grained grid level by hour to capture demand–supply imbalances across both spatial and temporal dimensions. To uncover dominant patterns in DLBS imbalance, we employ non-negative matrix factorization, a matrix decomposition technique, to extract latent structure of DLBS net flows. Four distinct patterns are identified: self-sustained balance, morning peak outflow, morning peak inflow, and metro-driven imbalance. We further apply multinomial logit models (MNL) to examine how these patterns are associated with different built environment characteristics. The results show that higher population density, greater diversity of points of interest, and proximity to city centers promote more balanced DLBS flows, whereas high road network density and concentrations of subway stations, residential communities, and firms intensify imbalances. These findings provide valuable insights for enhancing the operational efficiency of DLBS systems and supporting informed transportation management and urban planning practices. Full article

The use of light electric vehicles (LEVs), such as electric bikes and electric scooters, is being increasingly adopted as a sustainable transportation solution in urban areas. This is driven by the need for cleaner, faster, and space-efficient mobility solutions in urban areas. Although research on LEVs has grown over time, it remains fragmented across disciplines, creating a need for an integrated study on how LEVs contribute to sustainable transport in urban areas. This study conducted a bibliometric review to identify key themes in LEVs and sustainable transport in urban areas, and proposed future research agendas based on conceptual patterns and research gaps. The Scopus database was utilised, with a focus on 552 publications covering the period from 2000 to 2025, retrieved on 30 September 2025. The Biblioshiny application (version 5.0) was used to perform bibliometric performance analysis and science mapping techniques. Results revealed that the publication trend steadily rose from 2015, with a significant upsurge after 2020, with an annual growth rate of 18.69%. Three dominant themes were identified, namely sustainability, integration with public transport, and technological innovations, alongside underexplored areas such as shared electric micromobility, freight delivery, and policy and governance. Research gaps remain in lifecycle impacts, social equity, and governance frameworks, highlighting the need for inclusive and sustainable LEV adoption. Future research should capture full lifecycle impacts, expand access to LEVs beyond current user groups, and align rapid technological advances with inclusive governance frameworks. Full article

Identifying area functions around urban rail transit (URT) stations is crucial for optimizing urban planning and infrastructure allocation. Traditional methods relying on static land-use data fail to capture dynamic human–environment interactions, while emerging mobility datasets suffer from spatial granularity limitations. This study bridges this gap by integrating spatiotemporal patterns of dockless bike sharing (DBS) with Point of Interest (POI) configurations to characterize station functions. Taking Beijing as a case study, we develop a cluster analysis framework that synthesizes DBS density fluctuations, parking distribution shifts between day/night periods, and POI features. Cluster results reveal functionally distinct station groups with statistically significant differences in both DBS usage patterns and POI distributions. Critically, high-density urban cores exhibit concentrated bicycle usage aligned with mixed POI agglomerations, while suburban zones demonstrate commuter-oriented fluctuations with evening residential surges. This alignment between DBS-derived activity signatures and POI-based land-use features provides actionable insights: planners can optimize bicycle parking in residential clusters, calibrate last-mile connections in employment cores, and adapt infrastructure to localized functional transitions—ultimately enhancing URT-integrated sustainable development. Full article