Search Results (427)

E-cargo bikes have emerged as a promising solution for sustainable urban mobility and last-mile logistics. However, their structural design must ensure durability and safety under demanding cargo transport and daily operating conditions. This study evaluates the structural performance of three single-track E-cargo bike frame typologies, Urban, Long John and Long Tail, using finite element analysis under fatigue and impact loading conditions derived from EN 15194:2020 and EN 17860-2:2024. Numerical models of aluminum 6061-T6 frames were developed to simulate cyclic pedaling, horizontal, seat-post and vertical cargo loading forces, together with falling-frame and falling-mass impact tests. Structural performance was assessed through fatigue life, stress distribution, damage initiation, plastic strain and permanent wheelbase deformation. The Urban and Long John frames satisfied the adopted fatigue-life requirements, whereas the Long Tail frame failed the vertical loading-area fatigue test with a predicted fatigue life of 5.22 × 10⁴ cycles, below the required 2 × 10⁵ cycles. The maximum von Mises stresses during the falling-frame impact test reached 384 MPa, 326 MPa and 356 MPa for the Urban, Long John and Long Tail frames, respectively, while the corresponding permanent wheelbase deformations remained limited to 2.07 mm, 1.97 mm, and 1.43 mm, all below the acceptance criterion. These results highlight the influence of frame geometry and cargo location on structural behavior and support future frame optimization. Full article

Electric scooters (e-scooters) and electric bicycles (e-bikes) are established urban micromobility modes, and children and adolescents form a growing share of riders. This narrative review synthesises peer-reviewed evidence on e-scooter and e-bike injuries in patients aged 18 years or younger, covering epidemiology, mechanisms, injury patterns, clinical outcomes, comparisons with conventional devices, and prevention. Each source was classified as dedicated paediatric evidence, mixed-age evidence with extractable paediatric results, adult or mixed-age evidence used only for context, or an adult-dominated systematic review, so that the basis of every claim is explicit. The reported burden has risen across national surveillance data and single-centre series, and one population-adjusted analysis found injury rates increasing by 293% for e-bikes and by 88% for powered scooters between 2019 and 2022. Most studies lack exposure denominators, so exposure-adjusted paediatric risk remains poorly quantified and rising counts cannot be equated with rising risk. Injuries affect adolescent boys disproportionately and peak at ages 11 to 14 years. Extremity and soft-tissue injuries predominate, while a clinically important minority sustain traumatic brain injury, craniofacial and dental trauma, or severe multisystem injury. Low helmet use and higher device speeds are consistently associated with more severe outcomes, although observational designs cannot establish causal effects and device type is confounded with rider age and road exposure in every available paediatric comparison. Powered devices are associated with greater severity than non-powered counterparts. Speed limitation and helmet promotion appear promising, but paediatric-specific effectiveness evidence remains limited. Prospective paediatric research incorporating exposure denominators is the priority. Full article

Accurate short-horizon demand forecasting is essential for efficient bike-sharing rebalancing operations, yet most existing approaches validate on a single city and predict pickup demand only, leaving open questions about generalizability and the joint modeling of departure, arrival, and net supply flows. This paper proposes a CNN-LSTM architecture augmented with two components: a dynamic weather attention gate that conditions model output on forecast-horizon weather conditions and a community graph integration module that diffuses spatial context through a trip-volume-weighted adjacency matrix derived from Leiden community detection. A four-variant ablation study isolates the contribution of each component across two geographically and climatically distinct bike-sharing systems, BIXI Montreal and Capital Bikeshare Washington DC, using 15 min resolution trip data from two consecutive riding seasons. A single-seed evaluation initially suggested that community graph integration consistently reduces pickup prediction error in both cities and that the weather attention gate improves pickup prediction in Washington DC but not Montreal. However, a subsequent multi-seed check (three random seeds) found that these improvements do not hold up as consistently as the single-seed result suggested: The community graph variant outperforms the base CNN-LSTM in only 25–47% of seed-community combinations across the two cities and the weather attention variant in only 27–60%, indicating that small percentage improvements reported from a single training run in this class of model are frequently within the range of ordinary seed-to-seed variations rather than reliable architectural effects. We report this directly as a methodological finding in its own right: Ablation studies at the scale typically reported in this literature, including our own initial single-seed results, may substantially overstate the reliability of small reported improvements. For net supply change, the quantity most directly relevant to rebalancing decisions, all deep learning variants significantly outperform Random Forest in Montreal (paired Wilcoxon $p=0.0005$), a pattern that trends similarly but does not reach significance in Washington DC at the available sample size ($p\approx 0.06$–$0.07$); no significant difference is observed between the base CNN-LSTM and the full proposed model in either city, indicating that the added architectural components do not provide a demonstrable further benefit specifically for this derived, signed target. Taken together with the multi-seed instability observed for pickup prediction, these results caution against over-interpreting small single-seed ablation margins in this modeling setting more broadly and point to multi-seed evaluation as necessary practice for this class of architecture comparison. Full article

Bike-sharing systems (BSS) are increasingly adopted as sustainable urban mobility solutions; however, the station location problem has been tackled through isolated approaches—mathematical optimization, GIS-based topological inspection, or multi-criteria analysis—without a unified framework for data-scarce cities. This study presents a three-stage integrated methodology for optimal BSS network design, applied to the Municipality of Athens, a city with no pre-existing BSS. Stage 1 generates candidate bike stations through GIS analysis, with demand estimated via a transit-population composite proxy combining fixed-track transit ridership and population density, and road safety quantified via kernel density estimation of traffic accidents. Stage 2 formulates a Maximal Coverage Location Problem (MCLP) as a mixed-integer linear program (MILP) solved via the ε-constraint method, generating Pareto fronts across twelve scenarios defined by four temporal periods and three walking thresholds. Stage 3 applies the AHP-TOPSIS methodology to rank candidate Pareto-optimal stations across six criteria. The selected optimal configuration of 124 stations at a 300 m walking threshold achieves 94% weighted demand coverage with a stable year-round network. Road safety is the dominant AHP criterion (weight = 43.4%), reflecting Athens’ critical infrastructure gap. These results validate the framework’s applicability in data-scarce contexts and offer a transferable methodology for BSS planning. Full article

Shared bikes are frequently parked in disorder, resulting in low efficiency of manual collection and transfer and heavy workload for maintenance staff. Random parking across various areas forces shared bikes to occupy sidewalks and fire exits, damaging urban landscapes and disrupting traffic order. To tackle these industrial pain points, this paper develops an integrated intelligent robot system equipped with functions of multi-pose grasping, automatic transfer and fixed-point delivery of shared bikes, which can effectively address the drawbacks of low efficiency and high labor costs in traditional manual maintenance. This paper focuses on the completion of the robot’s overall mechanical structure design, stiffness–precision collaborative optimization model construction, finite-element static simulation verification, 1:7 scaled prototype development and performance testing. Firstly, the overall layout design of the multi-posture adaptive floating clamping mechanism, transfer-bearing frame, and Mecanum wheel omnidirectional mobile chassis is completed, and the structural parameters and assembly benchmarks of the core components are clarified. Secondly, a stiffness–precision coupling optimization model is established, and the static analysis under extreme load conditions is carried out through Abaqus finite-element software, which verifies the rationality of 45# carbon steel material selection and the safety of structural strength. Subsequently, a 1:7 scaled principle prototype is developed, and repetitive grabbing and transfer tests are carried out to verify the system operation feasibility, stability and grabbing accuracy. Finally, the statistical analysis of the test data and the horizontal comparison of similar schemes are completed. The test and simulation results show that the maximum stress of the system under extreme working conditions is 131.21 MPa, which is far lower than the allowable stress of 355 MPa of 45# steel, and the safety factor reaches 2.71. The maximum total deformation is 4.0552 mm, which is concentrated at the end of the front-end clamping mechanism, and is within the allowable stiffness deviation range of the transfer system. The average value of the single clamping positioning error of the scaled prototype is 0.476 mm, with a 95% confidence interval of 0.457–0.495 mm, which is converted to a positioning error of ≤3.4 mm for the full-scale prototype, which is far better than similar industry solutions. The average time of a single complete grabbing and transfer operation is 12.38 s, which is more than 45% higher than the traditional manual mode. The structural design, grabbing accuracy and operation stability of the robot designed in this paper all meet the requirements of actual working conditions of urban sidewalks, which can effectively reduce the intensity of manual labor and improve the operation and maintenance efficiency of shared bicycles. It has strong engineering application value and can provide reference for the design and manufacturing of intelligent collection and transfer systems for shared two-wheelers. Full article

This study examines the context-dependent association between eye-level motor traffic and metro-integrated cycling in Shenzhen, China. To address the limitations of static street-view imagery, we constructed a traffic-informed AIGC-enhanced analytical framework to approximate peak-hour visual motor-traffic conditions. The resulting eye-level motor-traffic measure was incorporated into OLS, GWR, and MGWR models together with land-use, road-network, development-intensity, and streetscape variables. The results show that this measure was positively associated with metro-integrated cycling volume primarily during the weekday morning peak, while the association weakened or became statistically insignificant during evening and weekend periods. We describe this pattern as a commuter’s paradox-like association: visible motor traffic may co-occur with high first-/last-mile cycling demand in high-intensity commuting environments, rather than necessarily deterring cycling. The analysis further suggests a threshold-like land-use pattern in which residential density may act as a background precondition rather than a linear driver during peak hours. This study illustrates the methodological applicability of Geospatial Artificial Intelligence (GeoAI) for addressing static-data limitations and provides planning implications for evaluating station-area feeder cycling environments. Full article

Bike-sharing systems (BSS) are a key component of sustainable urban mobility. However, their performance is strongly influenced by urban topography and socio-economic conditions. This study analyzes Barcelona’s public BSS, Bicing, to examine how altitude and neighborhood income affect bicycle availability, departures, and electric bicycle adoption. The main objective is to determine whether the observed “electric divide” is driven by income or by topographical necessity. The analysis uses 2023 data from 511 Bicing stations and income information from 62 neighborhoods obtained from Open Data Barcelona and the Spanish National Statistics Institute. Three indicators were constructed: bike availability ratio, departures ratio, and electric bicycle ratio. Results show a strong negative correlation between altitude and bike availability (r = −0.71) and a strong positive correlation between altitude and electric bicycle use (r = 0.78). High-altitude stations show lower availability and fewer departures, while electric bicycles dominate uphill trips. Although high-income neighborhoods initially appear to use more electric bicycles, regression results show that income becomes insignificant once altitude is controlled for. Therefore, electric bicycle adoption is driven mainly by physical necessity rather than socio-economic preference. Full article

Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer. Full article

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