Search Results (305)

The implementation of the new national electric bike (e-bike) standard has imposed stringent compliance requirements on equipment and e-bikes in the instant delivery sector, which directly affects the delivery efficiency and the work adaptability of food delivery riders. This study aims to investigate food delivery riders’ continued usage intention of e-bikes under China’s new e-bike regulation. Based on valid data collected from food delivery riders in Nanjing, this study employs ordered logit regression to examine the primary factors influencing their continued usage intention of e-bikes. The findings reveal that: (1) Male riders’ willingness to continue using e-bikes is comparatively lower, whereas older riders show a stronger intention. (2) Food delivery riders with higher incomes and those who need to replace their e-bikes show a stronger inclination to continue using them. (3) Limited e-bike options have a significant negative effect on riders’ continued usage intention, while speed limits exert no significant influence. Based on these empirical findings, corresponding policy recommendations are proposed to promote riders’ continued use of e-bikes, such as developing age-friendly delivery models, establishing an income guarantee mechanism for riders, and optimizing platform delivery time allocation. The findings could provide a theoretical basis and practical insights for policymakers and food delivery platforms to improve e-bike management policies. Full article

This paper presents a mobile application to encourage sustainable travel in urban areas as a proof-of-concept for user-centred sustainable urban transport. The app provides real-time route evaluation based on the environmental impact of different transport modes and local sensor monitoring feedback. Its core feature is an ecological route assessment using life cycle assessment calculations. Users receive quantitative feedback on their carbon footprint and a mobility score ranging from one (worst, red) to five (best, green). Providing both ecological and time-based navigation assessments, the app generates a comprehensive ecological footprint based on individual behaviour, raising awareness of United Nations climate targets. To increase its appeal, the app integrates a quest model offering vouchers from local partners (e.g., half-priced coffee) and competitions (e.g., complete the most journeys under 5 km by bike or on foot per week). A user-centred development process involving multiple test groups and a physical mock-up has been used to optimize the user interface, concept, and gamification elements. The app will be extended to include location-based quests and interactive chat quizzes. The project addresses key aspects of sustainable individual mobility and could be adapted for other cities, universities, or regions. 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

Urban freight and service operations represent a critical challenge for cities, contributing to greenhouse gas emissions, congestion, and competition for curb space. In addition to parcel deliveries, many service trips combine transport with installation, maintenance, or packaging recovery, generating long vehicle dwell times and inefficient use of public space. This paper investigates alternative operational scenarios for such activities, evaluating technological and organizational options that can reduce their environmental and spatial impacts. The study compares a diesel LCV baseline with four zero-emission configurations: battery electric LCVs; battery electric LCVs integrated with micro-hubs and cargo e-bikes; hydrogen fuel cell LCVs for long-range operations, and hydrogen fuel cell LCVs combined with cargo e-bikes via micro-hubs. The methodological framework is based on a vehicle routing problem (VRP) formulation supported by empirical data from Rome. It integrates indicators of energy use, carbon emissions, and curb-side occupation, and it includes the spatial representation of routes on urban and inter-urban maps to highlight operational differences across the five scenarios. Results indicate that zero-emission vehicles can eliminate tailpipe emissions, while logistics reorganization through decoupling improves the use of public space and enables the recovery of packaging materials. Battery solutions appear best suited to short and medium distances, whereas hydrogen is advantageous for longer routes. Overall, the study shows that combining technological and organizational measures provides a robust pathway toward sustainable logistics and more efficient service operations in metropolitan contexts. Full article

Background: Knee osteoarthritis (KOA) often results in reduced physical activity. This exploratory study evaluated lower limb muscle activity across three e-cycling assistance levels and examined e-cycling’s influence on kinesiophobia, stress and exercise motivation in individuals with KOA. Methods: Ten participants cycled with no, low and high assistance on an e-bike. Muscle activity and knee kinematics were measured using surface electromyography and inertia measurement units. A subset of four participants completed questionnaires assessing kinesiophobia (TSK-17), perceived stress (PSS), and exercise motivation (BREQ-3). Muscle activity across the three levels of assistance was analysed using a linear mixed-effects model. Results: Peak and mean muscle activity of rectus femoris (p = 0.01; 0.039), vastus medialis oblique (p = 0.0002; 0.001) and biceps femoris (p = 0.002; 0.03) were lower during high-assistance compared to the no-assistance cycling. No significant differences were observed in mean muscle activity between no- and low-assistance cycling. Reported exertion and pain were low during e-cycling, with kinesiophobia (M = 35.8 ± 2.5 to 33.3 ± 3.5) decreasing following e-cycling, whereas perceived stress (M = 14 ± 5.7 to 14.5 ± 3.3) increased marginally in our small sample. In the small subgroup of participants, the Behaviour Regulation Exercise Questionnaire outcomes increased in four out of six subscores post-exercise. Conclusions: Considering the differences in muscle activity recorded, and given that this is a pilot study, we propose that e-cycling may be an ideal way of introducing graded exercise to KOA patients, potentially allowing them to maintain physical activity and self-management of their disease. Full article

Ensuring equitable and inclusive access to children’s extracurricular activity facilities represents a profound manifestation of educational equity and is crucial for promoting children’s holistic development and societal sustainability. However, the underlying spatial mechanisms shaping their equity remain insufficiently explored. Using Tianjin’s central urban area as a case study, this study examines the spatial accessibility and equity of such facilities under dual government–market provision systems. The multi-mode Huff two-step floating catchment area model (MM-Huff-2SFCA) was employed to assess accessibility across walking, e-bike, public transport, and private car modes, integrating facility quality, household preference, and time-based distance decay. Equity was further evaluated using Lorenz curves and Gini coefficients across multiple spatial scales, while geographically weighted regression (GWR) identified spatial heterogeneity in factors such as child population density, transport infrastructure, household economic status, and basic education coverage. Results indicate that macro-level spatial balance masks substantial micro-scale inequities, particularly among transport-disadvantaged groups. Government and market systems exhibit contrasting spatial logics, forming a compensation–complementarity pattern across urban space. These findings underscore the need for refined and differentiated governance in extracurricular activity facilities planning, integrating spatial planning, transport accessibility, and social equity to advance child-friendly urban development and equitable public service provision. Full article

This paper presents an artificial intelligence (AI)-assisted multi-objective topology optimization of a 48 V interior permanent magnet synchronous motor (PMSM) intended for mid-drive e-bike applications. The machine features a 48-slot, 8-pole stator–rotor combination with $\Delta$-shaped three buried magnets per pole, and is coupled to a multi-stage gearbox that adapts its high-speed, low-torque output to a human-scale crank speed. The design problem simultaneously maximizes average torque and efficiency while minimizing torque ripple by varying key stator slot dimensions and magnet geometries. A modular MATLAB–ANSYS Maxwell framework is developed in which finite element simulations are driven by a Bayesian optimization (BO) loop augmented by a large language model (LLM) with retrieval-augmented generation (RAG). The LLM acts as a memory-based agent that proposes candidates, shapes Gaussian Process priors, and incorporates natural language rules expressing qualitative design knowledge. Two AI-assisted trials are compared against a multi-objective Artificial Hummingbird Algorithm benchmark, RAG + BO with and without natural language input. All three methods converge to a similar Pareto region with average torque around 5.4–5.7 Nm, torque ripple of approximately 12.8–14.2%, and efficiency near 93.3–93.6%, suitable for geared e-bike drives. The LLM-guided trial achieves this performance with a 20.1% reduction in simulation expenses relative to the BO baseline and by about 48% compared to the Artificial Hummingbird Algorithm. The results demonstrate that integrating LLM guidance into Bayesian optimization improves sample efficiency while providing interpretable design trends for PMSM topologies tailored for light electric vehicles. Full article

In skeletal muscles fibers, cellular respiration, excitation–contraction (EC) coupling (the mechanism that translates action potentials in Ca²⁺ release), and store-operated Ca²⁺ entry (SOCE, a mechanism that allows recovery of external Ca²⁺ during fatigue) take place in organelles specifically dedicated to each function: (a) aerobic ATP production in mitochondria; (b) EC coupling in intracellular junctions formed by association between transverse tubules (TTs) and sarcoplasmic reticulum (SR) named triads; (c) SOCE in Ca²⁺ entry units (CEUs), SR-TT junctions that are in continuity with membranes of triads, but that contain a different molecular machinery (see Graphical Abstract). In the past 20 years, we have studied skeletal muscle fibers by collecting biopsies from humans and isolating muscles from animal models (mouse, rat, rabbit) under different conditions of muscle inactivity (sedentary aging, denervation, immobilization by casting) and after exercise, either after voluntary training in humans (running, biking, etc.) or in mice kept in wheel cages or after running protocols on a treadmill. In all these studies, we have assessed the ultrastructure of the mitochondrial network and of the sarcotubular system (i.e., SR plus TTs) by electron microscopy (EM) and then collected functional data correlating (i) the changes occurring with aging and inactivity with a loss-of-function, and (ii) the structural improvement/rescue after exercise with a gain-of-function. The picture that emerged from this long journey points to the importance of the internal architecture of muscle fibers for their capability to function properly. Indeed, we discovered how the intracellular organization of the mitochondrial network and of the membrane systems involved in controlling intracellular calcium concentration (i[Ca²⁺]) is finely controlled and remodeled by inactivity and exercise. In this manuscript, we give an integrated picture of changes caused by inactivity and exercise and how they may affect muscle function. Full article

This paper reports part of the research results carried out within the National Centre for Sustainable Mobility (MOST—CN4)—Spoke 5 “Light Vehicle and Active Mobility”—funded by the Italian National Recovery and Resilience Plan (PNRR). Specifically, it presents the design of a bicycle frame based on the pursuit of maximum modularity and adaptability. The research investigates multi-level modularity as a strategic design lever to improve flexibility, usability, and production efficiency in light vehicles. A design-driven and interdisciplinary methodology combined a systematic analysis of 76 patents with a qualitative cross-comparison of 15 case-study products and collaborative co-design activities involving domain experts. The resulting insights informed the development of a modular bike frame prototype, enabling evaluation of user-centered adaptability, production-oriented modularity, and functional transformations such as e-bike integration and cargo configurations. Findings provide evidence-based, qualitative insights and practical design exploration for modular light vehicle development, offering an operational framework and laying the groundwork for a structured, generalized framework to guide future research and industrial applications. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

This study aimed to explore the factors influencing right-turning vehicles’ yielding behavior for crossing e-bikes at signalized intersections to improve safety for crossing e-bikes. Videos of different intersections were obtained through manual video recording and drone aerial photography. Spatiotemporal information data for right-turning vehicles and straight-through e-bikes were extracted through Tracker 6.0 software. Right-turning vehicle yielding decisions were categorized into three types: no yielding, decelerating to yield, and stopping to yield. Five potential variables influencing yielding decisions were selected: personal attributes of e-bike riders, traffic characteristics of e-bikes, traffic characteristics of right-turning vehicles, road characteristics, and right-turning vehicle–e-bike interaction influence characteristics. A multiple ordered logistic regression model was established to predict right-turn vehicle yielding decisions. Simultaneously calculating the OR (Odds Ratio) value reveals the likelihood of increased yielding probability under varying factors. For every one-unit increase in the number of crossing e-bikes, the yielding probability increases to 1.002 times the original value; for every one-unit increase in the average speed of right-turning vehicles, the yielding probability decreases to 0.406 times the original value; for every one-unit increase in the average crossing speed of e-bikes, the yielding probability increases to 1.737 times the original value. Compared with the straight + right-turn lane, a dedicated right-turning lane increases the yielding probability of right-turning vehicles to 4.2 times, and compared with not occupying a crosswalk, illegally occupying a crosswalk decreases the yielding probability of right-turning vehicles to 0.356 times. These findings offer valuable insights for enhancing the safety of e-bikes crossing signal-controlled intersections. Full article

The increasing demand for active and light mobility (including bicycles, e-bikes and e-scooters) has become a key driver of sustainable urban transport, calling for a renewed approach to urban planning. A central challenge is redesigning infrastructure around users’ needs, inspired by the “15-min city” concept developed by Carlos Moreno. However, the existing literature on user preferences in this domain remains fragmented, both methodologically and thematically, and often lacks integration of user behaviour analysis. This paper presents a structured review of recent international studies on factors influencing route and infrastructure choices in active and light mobility. The findings are organized into an analytical framework based on five macro-criteria: external and infrastructural factors, transport mode, user typology, experimental methodology and infrastructure attributes. The synthesis tables aim to summarize the findings to guide planners, researchers and decision-makers towards more inclusive, adaptable and effective mobility systems, through the development of user-oriented planning tools, attractiveness indexes and strategies for cycling and micromobility networks. Moreover, the review contributes to an ongoing national research initiative and lays the groundwork for developing decision-making tools, attractiveness indexes and route recommendation systems. Full article

In recent years, e-bikes have rapidly gained popularity in China. However, riders frequently engage in aberrant behaviors, posing significant traffic safety concerns. Field observation combined with traffic conflict techniques offer an effective approach for identifying risky riding behaviors that significantly affect traffic safety. This study aims to address two major limitations in existing research that can lead to estimation biases: the unsystematic and incomplete inclusion of potential risky riding behaviors, and the insufficient consideration of unobserved heterogeneity in conflict data. Data on 437 e-bike–motor vehicle conflicts were collected at four signalized intersections in Hefei, covering 21 variables including illegal, negligent, and error-prone riding behaviors, as well as sociodemographic factors. Appropriate conflict risk indicators were selected for straight-line and angle conflicts, respectively. A random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV) was developed and compared against binary logistic and mixed logit models. The results indicate that the RPBL-HMV model provides a significantly better goodness-of-fit than the other two models. Six factors with fixed parameters are positively associated with high-risk conflicts, while two factors exhibit random parameters—one of which decreases in mean when riders fail to slow down before turning. The identified risky behaviors and the corresponding targeted countermeasures offer practical insights for regulating unsafe e-bike riding and improving intersection safety. Full article

E-bikes are emerging as a competitive alternative to private cars in both urban and suburban contexts, enhancing accessibility to daily amenities and aligning with the proximity-oriented principles of X-minute city development. However, empirical knowledge remains limited regarding e-bike adopter profiles, trip purposes, influencing factors, and modal substitution patterns, particularly in urban Global South contexts. This exploratory pilot study employs correlation analysis and exploratory factor analysis to examine the sociodemographic characteristics of e-bike users in Surabaya, identify trip behavior patterns, and uncover potential determinants associated with e-bike usage within the X-minute city framework. Based on a sample of 71 e-bike users, the preliminary findings reveal notable socioeconomic patterns in e-bike adoption, with lower-income inner-city residents, particularly women in informal employment, emerging as early adopters. Additionally, two potential influence dimensions are identified: utilitarian trip chaining and active mobility infrastructure. While these findings require validation through larger-scale studies, they suggest potential for e-bikes to expand feasible X-minute city catchments and support low-carbon mobility transitions in similar Global South contexts. Full article

The aim of this research is to present both a sensorless control and a torque derating algorithm in the overload region of a permanent magnet motor for e-bikes. First, the theoretical backgrounds and the field-oriented control are presented. Then, a sensorless control is designed based on the back-emf estimation with a second-order generalized integral flux observer for the permanent magnet motor. The second-order generalized integral flux observer is an adaptive filter which can eliminate the DC offset and strongly attenuate the harmonics of the estimated rotor flux. The algorithms have been simulated and then validated by means of tests on a permanent magnet motor for e-bikes. Full article