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As the popularity of electric bicycles (e-bikes) continues to surge, the number of accidents involving them has commensurately increased. A significant factor contributing to the high fatality rate in these accidents is the low usage of helmets among e-bike riders. Helmets have been proven to reduce the severity of injuries, yet their usage remains unexpectedly low. This issue is particularly pronounced among college students, the primary buyer group for e-bikes. Regrettably, there is a lack of research exploring their intentions to wear helmets. Understanding determinants of their intentions to wear helmets is crucial in promoting safe e-bike travel. Therefore, the present study aims to develop an integrated theoretical model that combines the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM) to examine the factors influencing e-bike riders' helmet-wearing intentions among college students. Additionally, two variables—descriptive norms and law enforcement—are incorporated. The results indicate that the integrated model accounts for 76% of the variance in helmet-wearing intention, surpassing single-theory models. Specifically, the TPB accounts for 65%, while the HBM explains 53%. Notably, law enforcement emerges as the most influential factor, highlighting the crucial role of enforcing regulations and promoting awareness. Other significant factors include subjective and descriptive norms, attitudes, perceived benefits, perceived susceptibility, perceived barriers, and perceived severity. These findings provide valuable insights for policy development and targeted interventions aimed at improving helmet wear rates among e-bike riders, especially among the college student population. [ABSTRACT FROM AUTHOR]

This study explores the readiness of eight large Hungarian cities to adopt electric bike sharing systems as a sustainable transportation mode. By assessing 25 indicators related to infrastructure, safety, demographics, legislation, and transportation systems, we comprehensively understand each city's current situation and readiness level. By engaging experts, we derived weighted scores for key indicators to provide a comprehensive analysis of each city's potential. The results reveal varied readiness across cities, offering targeted insights for policymakers to enhance urban mobility sustainably. Our analysis reveals that certain cities, such as Budapest and Gyor, are better prepared for sustainable transportation than others. However, every city has positive and negative aspects that must be considered. Establishing infrastructure for cycling and connectivity to public transportation systems should be prioritized in Hungarian cities. Additionally, safety action plans should be implemented to address road safety concerns. Promoting cycling culture and electric vehicles is also essential to encourage the adoption of sustainable transportation modes. The findings of this research offer valuable insights to policymakers, urban planners, and researchers interested in promoting sustainable mobility. [ABSTRACT FROM AUTHOR]

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users' electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA's lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments. [ABSTRACT FROM AUTHOR]

In these times of efforts to develop high-efficiency hydrogen-adopted or hydrogen-fueled internal combustion engines (ICEs), and concerns related to electric vehicles such as battery cost, drawbacks in harsh winter climates, low range, etc., it is important to generate original ideas in line with the manufacturing of ICEs with improved efficacy. Accordingly, this study presents a comprehensive experimental investigation to observe the cooperated effects of pulse width modulation (PWM)-controlled hydroxy (HHO) gas introduction and improvement in tribological performance of piston ring-cylinder liner mechanism on performance and emission characteristics of a spark-ignition (SI) engine (co-system). The variation of compression ratio (CR), influence of two electrolyzer types of tube electrolyzer (TE) and plate electrolyzer (PE) for production of HHO gas, and zeta potential (ZP)-based dynamic light scattering (DLS) analysis to specify the optimal catalyst concentration in de-ionized water were also observed. Electron cyclotron resonance-chemical vapor deposition (ECR-CVD) method was utilized to bombard the piston ring substrates with diamond-like carbon (DLC) atoms under high energy plasma to improve the mechanical strength of the friction surface. Using linear tribometer, the uncoated (UPR) and coated piston rings (CPR) underwent friction tests to determine the wear rate (WR), and coefficient of friction (COF) which have substantial contribution to frictional losses. The surfaces of the samples were visualized via scanning electron microscopy (SEM) and atomic force microscopy (AFM) before and after abrasion tests to analyze carbon coating and its effects on tribological performance. The observations depicted that HHO flowrate needs to be varied as the engine load and CR change, and based on these observations, PWM control unit was designed, manufactured and reprogrammed so as to adjust electrical power consumption of HHO system when needed. The aforementioned analyses ensured optimization of the overall system so as to maximize the efficiency of the test engine. The co-system with optimized parameters (CPR + HHO + PWM) yielded an increase in average brake power (P e) up to 31%, and average reductions in specific fuel consumption (β), carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxide (NO x) emissions by 17%, 25%, 19%, and 14%, respectively at engine load range from 20% to 100%. It is expected that this study will be a good guide in terms of developing high-efficient ICEs due to promising results provided by the co-system. • Electrolyzers. • KOH catalyst. • HHO flow control. • Variable compression ratio. • Tribology. [ABSTRACT FROM AUTHOR]

Mobile measurement techniques are increasingly utilized to monitor urban emissions, regional air quality, and air pollutant exposure assessments. This study employed a bicycle measurement method to obtain the detailed distribution of air pollutant concentrations in roadside, commercial, residential, and recreational areas. The study area is located in Chuncheon, South Korea, with approximately 280,000 residents. Black carbon (BC), PM2.5, and NO2 were monitored using portable devices equipped on an electric bicycle. Results showed that in the evening (6–8 p.m.), the concentrations were higher in both commercial and residential areas compared to the background location, while concentrations were notably elevated only in roadside areas in the morning (8–10 a.m.). Spatial mapping of measured concentrations revealed that the highest concentrations corresponded to areas with densely operated charbroiling meat-cooking restaurants. Additionally, it was confirmed that BC and PM2.5 emitted from the commercial areas influenced nearby recreational areas (e.g., streamside roads). In conclusion, this study demonstrated that air pollutant hotspots resulting from human activities, such as dining at commercial restaurants, significantly worsen the local air quality on a small scale. Efforts to reduce the uncontrolled emissions of air pollutants from charbroiling meat-cooking restaurants are necessary. [ABSTRACT FROM AUTHOR]

Darkness is a barrier to cycling because the risk of an accident is higher and the feeling of social safety is lower. Factors influencing the choice to cycle (e.g., socio-demographic factors) have already been studied extensively, but rarely in relation to darkness. To implement strategies for increasing bicycle use (also when it is dark), we need to know what decision factors play a role for people to cycle or not when it is dark. A mixed method approach is adopted; applying a logistic regression using data of 842 surveyed cyclists in the province of East Flanders (Belgium), supplemented a qualitative analysis using 26 in-depth semi-structured interviews. Results show that knowing and using a route frequently has a positive effect on the likelihood of cycling in the dark. Elderly (and retirees) are less likely to cycle in the dark compared to youth and young adults. The latter group (ranging between 11 and 17 years old) often cycle to school while it is still dark (either or not accompanied by a parent). Whether or not one cycles alone or in group has a lower or higher propensity to cycle during the dark. People with a racing bike often cycle in groups and are less likely to cycle in the dark. Recreative and sportive cycle tours are mostly done during the day. Cyclists with an e-bike and speed pedelec have a higher likelihood to cycle in the dark compared to those who cycle on a regular bike. Women have 84% lower odds to cycle in the dark compared to men because they perceive darkness as less socially safe. Lastly, living in a rural environment has a positive impact on the odds to cycle in the dark compared to more urban environments. Establishing a cycling culture with inclusive infrastructure is essential for addressing the diverse needs of vulnerable groups and ensuring the provision of safe routes. A holistic approach is critical to effectively integrate these elements into urban planning and transport policies. • Darkness is a barrier to cycling due to a lower (sense of) safety. • More insights are required to organize effective strategies for increasing bike use. • Knowing and using a route frequently has a positive impact on cycling in the dark. • Young adults, men and electric bike owners have higher odds of cycling in the dark. • Policy interventions should focus on the actual needs of the intended target groups. [ABSTRACT FROM AUTHOR]

AbstractThis study investigates the impacts of various factors on e-bike riders’ injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders’ illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers’ speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders’ injury. Moreover, both drivers and riders’ illegal behaviour leads to an increased injury, while their avoidance behaviour before crashes can protect riders. In addition, types of visual obstacles, accidents occurring at night, large vehicles’ involvement, and the application of sunshade canopies by riders increased the probability of severe injury, while helmet use can protect riders in accidents with motor vehicles. [ABSTRACT FROM AUTHOR]

Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security. [ABSTRACT FROM AUTHOR]

Purpose: Electric bikes (EB) are a form of active transportation with demonstrated health benefits. The purpose of this study was to determine the influence of riding an EB for one week on indices of cardiometabolic health in middle-aged adults. Methods: Adults (n = 22; age = 57.1 ± 11.3 year; BMI = 27.7 ± 4.9) participated in a 2 week study. During Week 1, participants were instructed to continue regular activities. Starting Week 2 participants were provided an EB to ride at least 3 days for a minimum of 30 min·day−1. Physical activity (PA) and glucose were measured continuously. Body composition, blood lipids, glucose, insulin, hemoglobin A1c (HbA1c), plasma endothelin-1 (ET-1), and carotid-femoral pulse wave velocity (cf-PWV) were measured on days 1 and 14.Data and Statistical analyses or Statistics. Each participant served as their own control. Paired t-tests compared dependent variables between week 1 (without EB) and week 2 (with EB). Results: When provided an EB for one week, moderate to vigorous PA increased by 6–9 min·day−1 (P < 0.05) and sedentary time decreased by ~ 77 min·day−1 (P < 0.05). Data from 24 h continuous glucose monitoring showed the percentage of time in healthy range (70–120 mg·dl−1 glucose) increased (P < 0.05) from week 1 to week 2. Compared to day 1, cf-PWV was lower at day 14 (P < 0.05) following one week of riding an EB. Conclusion: Moderately-active, middleaged adults showed improved continuous glucose regulation and lower central arterial stiffness following one week of riding an EB. [ABSTRACT FROM AUTHOR]

Shared bikes, shared e-scooters, and public transit make up most public transportation modes in big cities. Their combination can provide a convenient, efficient, and flexible multi-modal transportation service. Despite the obvious similarity among them, differences exist in the roles that they play in a multi-modal transportation system. A case study in the City of Austin, where shared bikes, shared e-scooters, and public transit coexist, is used to explore their unique characteristics and how they spatially complement or compete with each other. The results show that public transit has more pronounced characteristics related to commuting than shared micromobility modes do, and that shared bikes are more likely to be used for commuting compared to shared e-scooters. Interestingly, the results suggest that there is spatial segregation between where shared bikes complement public transit and shared e-scooters complement public transit, i.e., only one shared mode complements public transit at a given area. [ABSTRACT FROM AUTHOR]

Wearing helmets while riding electric bicycles can significantly reduce head injuries resulting from traffic accidents. To effectively monitor compliance, the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles. However, manual enforcement by traffic police is time-consuming and labor-intensive. Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques. This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles, addressing these challenges. The proposed model improves upon YOLOv8n by deepening the network structure, incorporating weighted connections, and introducing lightweight convolutional modules. These modifications aim to enhance the precision of small target recognition while reducing the model's parameters, making it suitable for deployment on low-performance devices in real traffic scenarios. Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%, showing an 11.5% improvement over the baseline model, with a 16.2% reduction in parameters. Additionally, the model achieves a frames per second (FPS) rate of 58, meeting the accuracy and speed requirements for detection in actual traffic scenarios. [ABSTRACT FROM AUTHOR]

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved. [ABSTRACT FROM AUTHOR]

PURPOSE: Electric bicycles (e-bikes) achieve higher speeds than pedal bicycles, but few studies have investigated the impact on injury rates specific to the pediatric population. Utilizing the National Electronic Injury Surveillance System (NEISS), we compared rates of pediatric injury for e-bikes, bicycles, and gas-engine bicycles (mopeds) from 2011 to 2020. METHODS: Descriptive and bivariate inferential analyses were performed upon NEISS estimates of e-bike, bicycle, and moped injuries in children aged 2-18 years. Analyses were stratified by patient age and helmet usage. The Mann-Kendall test of trends was used. RESULTS: We identified 3945 e-bike, 23,389 moped, and 2.05 million bicycle injuries. Over time, the incidence of injury increased for e-bikes (Kendalls τ=0.73, p = 0.004), decreased for pedal bicycles (Kendalls τ= - 0.91, p = 0.0003), and did not change for mopeds (Kendalls τ = 0.06, p = 0.85). Males accounted for 82.5 % of e-bike injuries. The age group most commonly affected by e-bike injury (44.3 %) was 10-13 years old. The proportion of injuries requiring hospitalization was significantly higher for e-bikes (11.5 %), compared to moped and bicycle (7.0 and 4.8 %, respectively, p

Local and state electric bike (e-bike) incentive programs offering point-of-sale or post-sale monetary discounts to consumers have been implemented across the United States since 2018. As yet, however, little is known about their effectiveness in changing travel behavior. To understand the outcomes of these incentive programs, UC Davis researchers analyzed survey data from rebate recipients in Northern California two months and one year after they acquired e-bikes. The rebate programs were evaluated for effects of e-bike ownership on travel behavior, including changes in bicycling, driving, and use of transit, and on greenhouse gas emissions. The team also suggest areas for future research. View the NCST Project Webpage

The range of an electrically assisted bicycle, which is constrained by the rider's cycling ability and the battery capacity, is heavily influenced by rolling resistance. Furthermore, the magnitude of rolling resistance affects commuters' motivation to decide whether to cycle or to choose another way to commute. This paper presents a way to simulate the transient rolling resistance of bicycle tyres as a function of ambient temperature. The significance of the change in driving resistance at different ambient temperatures is demonstrated through the range simulation of an electrically assisted bicycle at varying ambient temperatures. A representative driving cycle for bicycle commuters was created, enabling comparison of dynamic behaviour in a standardised set, to evaluate the effect of ambient temperature on the battery capacity and the increase in driving resistances. To the authors' knowledge, this kind of model has not previously been created for bicycles. The model calculates tyre temperature based on the heat transfer, considering the heating—i. e., rolling resistance—and cooling effects—i. e., convective and radiative cooling. The decrease in tyre temperature results in an increase in rolling resistance and a decrease in the battery capacity, which was considered in the simulations. The results show significantly increased energy demand at a very low ambient temperature (down to −30°C) compared to + 20°C. The novelty of this article is simulating energy expenditure of bicycle dynamically as a function of ambient temperature. This model includes a temperature-dependent transient bicycle rolling resistance model as well as a battery capacity model. The findings provide researchers with a better comprehension of parameters affecting energy expenditure of bicycles at different ambient or tyre temperatures. The models can be used as a tool during the design process of bicycles to quantify the required battery capacities at different climates. In addition, traffic planners can use the model to assess the effect of changes in infrastructure on motivation to utilise bicycles. [ABSTRACT FROM AUTHOR]

Over the last few years, electrically assisted cycling, e-biking, has increased substantially worldwide. Replacing car driving for individual journeys, especially commuting, is highlighted as important to mitigate climate change, improve public health, and reduce congestion and other unwanted consequences connected to the car. Car driving, however, is still the overwhelmingly dominant mode of personal transport globally and the 'system of automobility' permeates the whole of society. Flexibility and autonomy are considered the main reasons for the car's dominance (Urry 2004). By analysing interviews and diaries kept by e-bikers, collected in semi-urban and urban settings in Sweden, this article aims to contribute to knowledge about emerging micromobility practices such as e-biking in relation to a transport system where flexibility is the norm. The results show that e-biking encompasses elements that give the practice potential to both recruit and retain practitioners. By successfully combining elements of conventional cycling and car driving, it offers reliability, convenience, and flexibility. E-biking facilitates transforming a dull commute into leisure as the rider can enjoy the sensuous and reflective aspects of the journey. At the same time, through that squeezing of time, it does not challenge prevailing structures but rather maintains the time-space of automobility. [ABSTRACT FROM AUTHOR]

• 52 participants reached 4 predefined destinations via shared e-scooter or bicycle. • E-scooter riders prioritize road surface, safety, and rule compliance. • GPS riding data is comparable for both user groups. • E-scooter riders tend to take longer routes for routes with multiple turns. • Traffic planning should prioritize high-quality cycling infrastructure. Problem : The surge in popularity of electric kick scooters (e-scooters) poses new challenges for traffic planning, demanding a comprehensive understanding of route choice behavior to see how e-scooters are used, how they affect traffic flow, and where improvements can be made to the road infrastructure. Therefore, this study aimed to analyze route choices and preferences of e-scooter riders and cyclists in a quasi-experimental setup with both user groups having the same trip destinations. Method : Two groups of participants (n = 52) completed a ride with either a shared e-scooter or bicycle to reach four predefined destinations in Dresden, Germany. The riders were supposed to choose their routes and subsequently reported the difficulty of decision-making and the importance of several route choice factors related to the ride. Results : E-scooter riders rated road surface and safety as significantly more important for route choice than cyclists and tended to perceive the decision-making as more difficult. Riding data revealed broad comparability between the groups, with e-scooter riders tendentially having longer routes for complex decisions (unknown destinations, scenic routes, more turns required). Discussion : The study suggests that the route preferences of e-scooter riders may be influenced by a combination of road surface and safety considerations, highlighting the need for high-quality cycling infrastructure. Limitations exist regarding the naturally occurring differences in riding experience in e-scooter riding versus cycling. Practical implications indicate that planning for e-scooter riders can benefit from insights drawn from activities designed for cyclists. The provision of real-time road quality information is proposed, considering its potential impact on overall road safety. Summary : This study contributes to a better understanding of how e-scooter riders navigate through cities and delivers a valuable foundation for transport planners and engineers considering the rise in cycling and micro-mobility use. [ABSTRACT FROM AUTHOR]

• Social categorization among e-scooter riders and cyclists was studied. • Higher dispositional attribution ratings for ingroups than outgroups emerged. • Fine severity was higher for the ingroup than the outgroup. • Situational attribution ratings were higher for e-scooter riders than cyclists. • Results imply considerate coexistence among cyclists and e-scooter riders. Problem: In many countries, a new road user group, e-scooter riders, share the existing cycling infrastructure. The study aimed to investigate if an individual's status as a cyclist or e-scooter rider affects their social identity and whether it results in ingroup favoritism or outgroup discrimination. Method: An online experiment involving 179 cyclists and 64 e–scooter riders was conducted, where they rated the behavior of ingroup or outgroup members in six traffic scenarios. Results: Participants rated dispositional attributions as more causally relevant than situational ones across all traffic scenarios. Cyclists and e–scooter riders were inclined to judge ingroup members' rule violations more harshly than those of outgroup members in terms of dispositional attribution ratings and punishment severity. For situational attributions, few indications of ingroup favoritism were observed for the e–scooter rider group. Summary: Findings suggest initial indications of considerate coexistence, from the perspective of social identity theory, between the two modes of transport, supporting current regulations on the use of cycling infrastructure by e–scooter riders. Indications of ingroup discrimination, however, suggest that safety campaigning may target to promote courtesy within the cyclist and e-scooter rider groups. [ABSTRACT FROM AUTHOR]

The occupational health burden and mechanisms that link gig work to health are understudied. We described injury and assault prevalence among food delivery gig workers in New York City (NYC) and assessed the effect of job dependence on injury and assault through work-related mechanisms and across transportation modes (electric bike and moped versus car). Data were collected through a 2022 survey commissioned by the NYC Department of Consumer and Worker Protection among delivery gig workers between October and December 2021 in NYC. We used modified Poisson regression models to estimate the adjusted prevalence rate ratio associations between job dependence and injury and assault. Of 1650 respondents, 66.9% reported that food delivery gig work was their main or only job (i.e., fully dependent). About 21.9% and 20.8% of respondents reported being injured and assaulted, respectively. Injury and assault were more than twice as prevalent among two-wheeled drivers, in comparison to car users. Fully dependent respondents had a 1.61 (95% confidence interval (CI) 1.20, 2.16) and a 1.36 (95% CI 1.03, 1.80) times greater prevalence of injury and assault, respectively, than partially dependent respondents after adjusting for age, sex, race and ethnicity, language, employment length, transportation mode, and weekly work hours. These findings suggest that fully dependent food delivery gig workers, especially two-wheeled riders, are highly vulnerable to the negative consequences of working conditions under algorithmic management by the platforms. Improvements to food delivery gig worker health and safety are urgently needed, and company narratives surrounding worker autonomy and flexibility need to be revisited. [ABSTRACT FROM AUTHOR]

Delivery riders are more vulnerable than other traffic participants, especially in vehicle-involved delivery crashes. This study aims at identifying the unobserved heterogeneities in different factors, based on 4251 vehicle-scooter-style electric bicycle (SSEB) crashes. First, some potential factors are selected from seven perspectives, and the spatiotemporal characteristics are analysed. Second, a latent class clustering method is proposed to clarify the optimal number of clusters by maximizing the heterogeneities across clusters. Third, partial proportional odds (PPO) models for the whole dataset and sub-datasets are developed to explore the heterogeneities across various clusters. Besides, marginal effects are implemented to quantify the heterogeneities. The results evidence that there are remarkable heterogeneities across different clusters, especially in riding behaviours and road conditions. Several factors only significantly affect particular clusters but not the whole dataset. The PPO models for the sub-datasets perform better in identifying the underlying heterogeneities. The results also highlight the greater roles of riding behaviours and road conditions in delivery SSEB-vehicle crashes. The top five influencing factors are running red light, using cell phones, vehicle type, reverse riding and bike lane (their maximum marginal effects exceeding +35%). The findings could support to mitigate the related crash losses. [ABSTRACT FROM AUTHOR]

As a trending mobility choice, e-scooters have become popular in many cities. A number of authorities have initiated shared e-scooter trial schemes to assess the feasibility of the vehicles prior to enacting official legalisation. This paper aims to provide an evidence review of shared e-scooters and investigate how existing evidence may inform long-term policies. This carries significant relevance for jurisdictions that are in a conflicting position with e-scooters, such as New South Wales (NSW), Australia whose context motivates this study. The evidence review focuses on three themes derived from experience with shared e-scooters within the broader micromobility landscape, namely: safety; where shared e-scooters fit into the modal landscape; and the environmental impacts. Findings confirm that ensuring the safety of shared e-scooters requires complex solutions, which may include a clear regulatory framework for e-scooters, safety education and skill training, innovative data collection and analysis methods, and an approach to safety management that is user-based, location-based, and time-based. In terms of modal fit policymakers should encourage first and last-mile combinations with public transport, with consideration of user characteristics; while climate impact is strongly correlated to the mode replaced by e-scooter trips. The paper provides insights for policymakers on the regulation and positioning of shared e-scooters. • Many jurisdictions are struggling to determine policies for e-scooters. • Existing e-scooter evidence sheds light on possible long-term policies. • Safety management should consider different users, locations, and times. • First and last-mile combinations with public transport should be encouraged. • Climate impact is strongly correlated to the mode replaced by e-scooter trips. [ABSTRACT FROM AUTHOR]

This paper focuses on computing the electromagnetic parameters of inner rotor-configuration Surface-mounted Permanent Magnet Synchronous Motors (SPMSMs) via the combination of analytical and finite element methods with an application to electric bicycles. The analytical method is, first, developed in detail to calculate the essential parameters of the inner rotor-configuration SPMSM. Then, a Finite Element Method (FEM) is introduced to verify the analytical parameters found. Further, a simulation of magnetic flux density, torque, cogging torque, torque ripple, back Electromagnetic Force (EMF), linkage flux, and temperature is performed. The end result is validated with a real 2.2 kW inner rotor-configuration SPMSM. [ABSTRACT FROM AUTHOR]

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In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer's operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory. [ABSTRACT FROM AUTHOR]

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor's degree, and 92% possess a driver's license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility. [ABSTRACT FROM AUTHOR]

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment. [ABSTRACT FROM AUTHOR]

This article describes a tangible interface for an e-coach, co-designed in four countries to meet older adults' needs and expectations. The aim of this device is to coach the user by giving recommendations, personalized tasks and to build empathy through vocal, visual, and physical interaction. Through our co-design process, we collected insights that helped identifying requirements for the physical design, the interaction design and the privacy and data control. In the first phase, we collected users' needs and expectations through several workshops. Requirements were then transformed into three design concepts that were rated and commented by our target users. The final design was implemented and tested in three countries. We discussed the results and the open challenges for the design of physical e-coaches for older adults. To encourage further developments in this field, we released the research outputs of this design process in an open-source repository. [ABSTRACT FROM AUTHOR]

This study investigates the design an electric motor propelling hubless wheel mounted on an electric bicycle (e-bike) by numerical analysis. The motor is part of an in-wheel system that uses a permanent magnet synchronous motor (PMSM) to drive the hubless wheel. This paper presents an optimized PMSM design and compares various torque-related parameters, such as cogging torque, cogging torque total harmonic distortion (THD), and ripple torque. The comparison is based on a motor design that examines the effect of different factors, including the number of windings, slot openings, permanent magnet span, and magnet thickness. The motor was modeled and analyzed with variations in the number of windings (21, 23, 25), slot openings (2.4 mm, 3.0 mm, 3.6 mm), permanent magnet span (23 mm, 25 mm, 27 mm), and permanent magnet thickness (3.5 mm, 4.0 mm, 4.5 mm). Our findings suggest that increasing the permanent magnet's thickness leads to a higher magnetic flux density. Additionally, when the permanent magnet span exceeds 25 mm, there is a sharp increase in cogging torque THD. Based on these results, a slot opening of 3.0 mm and the use of 25 windings are considered suitable for a hubless e-bike. [ABSTRACT FROM AUTHOR]

Among the global carbon emissions in different fields, the carbon emissions in the transportation sector account for more than 1/4. Only the development of passenger car technology makes it difficult to achieve the climate goals. E-bike is considered to be one of the effective solutions. Hydrogen energy, as one of the representatives of new energy, has the advantages of cleanliness, high efficiency, and high energy density. Therefore, in this paper, a long-range hydrogen-electric hybrid bike and its energy management strategy are designed to achieve optimal performance. Herein, two energy management strategies, including the threshold-based and fuzzy logic optimized threshold-based strategy, are designed and compared by evaluating the parameters of equivalent hydrogen consumption, state of charge fluctuation, and the final state of the control system. The results show that with an initial SOC of 50% for Li-ion battery, the fuzzy logic optimized threshold-based strategy reduces equivalent hydrogen consumption by up to 16% compared to the threshold-based strategy. Besides, the fluctuation of the Li-ion battery's state of charge could be reduced by up to 54.7%, indicating that the fuzzy logic optimized threshold-based strategy is a promising energy management strategy for fuel cell hybrid E-bike. [Display omitted] • A long-range hydrogen-electric hybrid E-bike with metal hydride hydrogen storage is designed. • A fuzzy logic optimized threshold-based energy management strategy (EMS) is proposed. • Li-ion battery SOC fluctuations could be reduced by up to 54.7% via fuzzy logic optimized EMS. • Fuzzy logic optimized EMS shows self-adaptability and economic H 2 consumption for hybrid E-bike. [ABSTRACT FROM AUTHOR]

Promoting the shift from private cars to micromobility (e.g., bike, e-bike, scooter) can represent a valuable action to improve city sustainability and liveability. Micromobility can help to replace trips by individual private cars (e.g., daily short round trips) as well as to improve coverage and accessibility of transit services, and, subsequently, to reduce the traffic impacts (e.g., pollutant emissions). It can be seen as a potential solution to move people more efficiently in urban areas, as well as to push people towards a more active mobility behaviour, contributing to the well-being goals. In this context, the paper, rather than inferring the users' propensity to change their travel mode, proposes a methodology to identify car trips that can be considered the most compatible with micromobility. Estimation of the potential demand (e.g., the upper level of car trips that could be replaced by micromobility) is carried out by exploiting the opportunity offered by floating car data (FCD) for characterising car trips. Its goodness is therefore evaluated through an application to a real case study (i.e., the city of Trani, Apulia Region, Southern Italy), divided into seventy traffic zones, and where a FCD dataset of about 5,200 trips was available. The FCD allowed the car trips to be characterised (e.g., origin and destination, path features) instead of using the traditional surveys. The results indicate that a significant share of daily car trips can be substituted (i.e., the most compatible) by micromobility (31% of car round trips in the case study), with considerable potential environmental gains (traffic emission reduction; less than 21% of total emissions from private cars). Results can be of interest to local authorities in integrating micromobility in urban mobility planning and promoting new sustainable transport alternatives, as well as to transport companies for designing new appeal services. The developed methodology is parametric and uses easy-to-obtain data available worldwide; thus, it can be easily transferred to other city contexts. [ABSTRACT FROM AUTHOR]

Kansei Engineering (KE) is a product design method that aims to develop products to meet users' emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user's multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user's multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design. [ABSTRACT FROM AUTHOR]

Remarkable characteristics like simple structure, high starting torque, simple maintenance, good efficiency at all speeds and cost-effectiveness have made switched reluctance machines (SRMs) suitable candidates for electric cars, electric bicycles and variable speed applications (VSAs) such as wind turbines. In this paper, a three-phase Outer-Rotor SRM (ORSRM) with 6 stator poles and 10 rotor poles (6/10) for VSA has been investigated. Unlike conventional SRM structures such as 6/4 and 12/8, an SRM model with higher number of rotor poles can reduce torque ripple much more effective than conventional SRMs. First, the SRM design is done accurately, then, by changing some parameters, the sensitivity analysis is performed on torque ripple and average torque. Finally, the optimisation process on the geometry and structural dimensions of the SRM like rotor pole arc, stator pole arc, rotor yoke thickness, rotor pole height, rotor diameter and the number of turns per phase are executed to minimise the torque ripple and maximise the average torque via genetic algorithm (GA). The initial design, optimisation and performance of the proposed model are validated via a 2-D finite element method (FEM) technique by ANSYS-Maxwell. [ABSTRACT FROM AUTHOR]

Cities face major challenges when it comes to sustainability and mobility. Transport's contribution to climate change is well-established, and people need to move in the most sustainable way to reach the 2030 emissions targets set by the Paris Agreement. One possible pathway towards more sustainable mobility practices is electromobility. The electrification of micro-mobility is happening rapidly, and one of the most popular is the e-bike. For years, electric bikes were relegated to niche status, but they are now experiencing explosive growth in sales in many countries. In this article, we draw on an experiment with new users of e-bikes to study the integration of e-bikes into existing mobility practices and to explore their sustainability potential. Through the lens of domestication theory, we zoom in on the relations that formed between users, technology, and environments in the course of the experiment. Our analysis highlights how emotional and sensory experiences play crucial roles in the adaption of new mobility technologies. Based on our findings, we argue that to reach the sustainability potential of e-bikes, a set of support mechanisms must be developed according to a holistic and relational understanding of mobility that also takes emotions and sensory experiences into consideration. [ABSTRACT FROM AUTHOR]