Search Results (33)

In recent years, electric cargo (e-cargo) bikes have been increasingly adopted as a sustainable alternative for urban logistics and last-mile delivery, particularly in densely populated areas where traditional vehicles face traffic congestion and access limitations. This study aims to develop a representative driving cycle for e-cargo bikes based on real-world cycling data. An instrumented Long John-type e-cargo bike was used to collect naturalistic data from four different riders covering a total of 50 km along a predefined route in the city center of Florence, selected in collaboration with the Italian postal service provider (i.e., Poste Italiane) to reflect typical delivery operations. The driving cycle was generated using a Markov chain Monte Carlo (MCMC) method, modeling the stochastic transitions of vehicle speed and acceleration values. The resulting driving cycle, defined as the Florence cargo bike driving cycle (FCBDC), achieved an error of 2.1% on the Speed Acceleration Probability Distribution (SAPD) root sum square difference; although minor losses in peak acceleration values were observed due to data smoothing and discretization, the synthesized driving cycle effectively reproduces the dynamic characteristics of e-cargo bike riding. While the study is limited to a single route and is equivalent to simulated postman behavior, it provides valuable insights to guide the future development and optimization of e-cargo bikes for sustainable mobility operations. Full article

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 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

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

The increasing adoption of electric bicycles (e-bikes) has led to a growing need for advanced active safety systems, such as anti-lock braking systems (ABSs), to enhance rider safety. In recent years, both hydraulic and electromechanical ABSs were researched. To support the development and validation of these systems, this paper presents a longitudinal vehicle dynamics model of an electric bicycle. The model captures key physical interactions, including drivetrain, transmission, braking, and tire–road contact, to accurately simulate longitudinal motion. By leveraging this model, future studies can perform virtual validation of active safety components in a controlled and repeatable environment, reducing the dependency on costly and time-intensive physical testing. The proposed model lays the foundation for a model-based design approach, enabling early-stage performance assessment and optimization of safety-critical functions in electric bicycles. Full article

In road cycling, developing bike handling skills can prevent crashes and falls. Nevertheless, bike handling remains largely unexplored in the world of road cycling. The goal of this research was to develop a methodology to assess bike handling during races and training by estimating the rider–bicycle roll angle and road-plane accelerations from global positioning system (GPS) data only. A multi-dimensional bike-rider mathematical model was included in an optimal control framework to follow a reference trajectory generated from GPS data points. Estimated variables and experimental data collected with a cost-effective setup showed good agreement, i.e., root mean square error (RMSE) of 12° and 0.1 g for roll angle and both longitudinal and lateral accelerations, respectively, in the worst-case scenarios. This methodology might allow for the estimation of key bike handling variables during fast segments with cost-effective instrumentation. It can therefore constitute a tool for objectively assessing bike handling in road cycling training and racing. Full article

The purpose of this study was to investigate how environmental judgments and psychological factors jointly influence self-reported violation riding behaviors among e-bike riders in China, with attention to sociodemographic heterogeneity. To achieve this, the e-bike violation riding behavior questionnaire was designed. Additionally, a hybrid approach integrating the Structural Equation Model (SEM) with the Random Parameters Logit (RP-Logit) model was constructed to reveal the impact factors of e-bike riders’ violation riding behaviors, in which demographic information and latent variables were comprehensively considered. This methodology simultaneously analyzed the complex relationships among latent variables (measured by SEM) and captured the heterogeneous effects of demographic factors on discrete violation tendencies (modeled by RP-Logit). The following two main findings emerged: (1) Experienced riders and those who use e-bikes as operating tools tend to exhibit a higher tendency to engage in violation riding. (2) Perceived Risk has the greatest impact on the performance of high-violation tendencies. Specifically, the probability of choosing high-violation riding behaviors decreases by 0.18 for each unit increase in the rider’s Perceived Risk. (3) Similarly, for each unit increase in riders’ Perceived Law Enforcement, the probability of choosing high-violation riding behaviors decreases by 0.15. The findings suggest that relevant authorities should address e-bike violation behaviors through enhanced safety education and strengthened enforcement measures, particularly targeting high-risk rider groups. Full article

As cities globally prioritize sustainable transportation, understanding gender-differentiated responses to the urban built environment is critical for equitable mobility planning. This study combined the Social Ecological Model (SEM) with the theoretical perspective of Gendered Spatial Experience to explore the differentiated impacts of the Perceived Street Built Environment (PSBE) on the cycling behavior of men and women. Questionnaire data from 285 e-bike and traditional bicycle riders (236 e-bike riders and 49 traditional cyclists, 138 males and 147 females) from Gulou District, Nanjing, between May and October 2023, were used to investigate gender differences in cycling behavior and PSBE using the Mann–Whitney U-test and crossover analysis. Linear regression and logistic regression analyses examined the PSBE impact on gender differences in cycling probability and route choice. The cycling frequency of women was significantly higher than that of men, and their cycling behavior was obviously driven by family responsibilities. Greater gender differences were observed in the PSBE among e-bike riders. Women rated facility accessibility, road accessibility, sense of safety, and spatial comfort significantly lower than men. Clear traffic signals and zebra crossings positively influenced women’s cycling probability. Women were more sensitive to the width of bicycle lanes and street noise, while men’s detours were mainly driven by the convenience of bus connections. We recommend constructing a gender-inclusive cycling environment through intersection optimization, family-friendly routes, lane widening, and noise reduction. This study advances urban science by identifying gendered barriers in cycling infrastructure, providing actionable strategies for equitable transport planning and urban design. Full article

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion. Full article

This study focuses on developing an advanced anti-lock braking system (ABS) for motorcycles, specifically targeting the challenges associated with cornering. Significant roll angles during motorcycle turns can often lead to slipping and the loss of control, increasing the risk of accidents. Existing ABSs primarily address longitudinal dynamics and fail to provide optimal braking control during cornering. To address this gap, this study utilizes BikeSim and MATLAB/Simulink for simulations and experiments to design an ABS that adapts to varying roll angles by analyzing motorcycle dynamics during cornering. A tire model is constructed using the Magic Formula to examine both longitudinal and lateral characteristics under different conditions, which helps determine the current tire slip set-point. The controller, designed with a finite-state machine combined with bang-off-bang control, uses tire slip as the control variable. It adjusts the slip set-point based on changes in roll angle and sends control signals to the hydraulic actuator to regulate braking pressure, ensuring optimal braking performance without the loss of control. Finally, hardware-in-the-loop experiments are conducted, with real-time control commands sent to the hardware platform’s actuator via BikeSim RT. These experiments validate the effectiveness of the designed controller, significantly enhancing braking stability during cornering and improving safety for motorcycle riders. Full article

Leisure activities are known to be especially important for the health of people with disabilities. In Belém, PA, an Amazonian city in Brazil, a nonprofitable organization has promoted leisure ridings in bicycles for those people in Utinga State Park, a large green area for physical and leisure activities. The handcrafted bikes have a sidecar attached for users with disabilities which are ridden by trained volunteers. Since such bikes have been empirically manufactured, they require some minor improvements in safety, comfort, and handling, and verification of structural strength. Therefore, ergonomic, modal, and forced vibration analyses assessed the user’s comfort and safety and a structural analysis with the use of strain gauges evaluated the bicycle’s structural strength. Initially, a numerical modal analysis was performed using the finite element method, and the modal model obtained was validated by an experimental modal analysis employing shaker excitation. ISO-2631-based evaluations of forced vibration and human body comfort were conducted regarding whole-body vibration in vehicles and mechanical equipment. Vibration measurements at the position of the rider and sidecar occupant were obtained during rides on the bicycle and, according to the results, in general, when subjected to loads, the bicycle showed low stress levels far from the yield stress of the material, promoting an excellent safety factor in relation to its structural integrity. The modal, comfort, and forced vibration analyses revealed a mode of vibration in the sidecar that caused discomfort to the back of the users. Ergonomics analysis pointed out changes in the handlebars, the bicycle seat, the coupling between the sidecar and the bike, and the dimensions of the sidecar will provide greater comfort and safety. This paper presents and discusses the proposed modifications to both bicycle and sidecar. Full article

China has become the largest country for e-bikes in the past decade, leading to a surge in road-related accidents. Chinese provinces and cities have successfully formulated local regulations, which set fines for not wearing helmets levying on e-bike riders. Without considering psychological resistance which is universal under the premise of compulsory legislation, the legislation and enforcement activities cannot continuously promote electric bikers’ helmet use. This study aims to investigate the predictors that influence e-bike riders’ intention to wear helmets by designing a research methodology that incorporates the theory of planned behavior (TPB), the protection motivation theory (PMT), and the psychological reactance theory (PRT). A multi-method analytical approach, including structural equation modeling (SEM), fuzzy-set qualitative comparative analysis (fsQCA), and a Bayesian Network (BN) with a sample dataset of 846 respondents. SEM and fsQCA explored the intentions to wear helmets from linear and nonlinear perspectives. BN verifies the degree of influence between different configurations resulting from fsQCA, to predict which mental configurations could get the most significant impact on helmet use. A total of 11 configurations lead to high helmet-wearing intention, and 4 lead to low helmet-wearing intention. Policymakers could design safety policies on our research, such as driving licenses and special lectures for e-bike riders. Full article

Sustainable modes of transport are being promoted to achieve global climate goals. The perceived user experience is decisive for the choice of transport mode. To increase the share of sustainable transport in total traffic, the user experience is placed into the spotlight, raising the need for appropriate exploration methods. Machine learning (ML) techniques have become increasingly popular in the transport domain, but the black-box nature of ML models poses significant challenges in interpreting the relationship between model input and output. Explainable AI methods (XAI) can fill this gap by providing post hoc interpretation methods for black-box models. The aim of the present work was therefore to assess the potential of XAI to explore user experience in transport. The introduced method was based on a popular XAI method named SHAP (SHapley Additive exPlanations). Applied to the use case of e-bikes, we aimed to explore factors influencing the riding experience on e-bikes. We applied Gaussian process regression to data collected in a cycling study from 55 e-bike riders including rider behaviour, motor power and riding dynamics. Applying SHAP, we compared the riding experience of four rider types identified by hierarchical cluster analysis. The results provide insights into the riding experience on e-bikes: motor power, rider behaviour and riding dynamics were found to be meaningful predictors differing in their impact between rider types. Our results can be regarded as a proof of concept and demonstrate the potential of XAI to enhance the understanding of user experience in transport. Full article

To study the internal formation mechanisms of risky riding behaviors of takeaway e-bikers at urban intersections, we designed a takeaway riding risky behavior questionnaire and obtained 605 valid samples. An exploratory factor analysis was then conducted to extract five scales: individual characteristics, safety attitude, riding confidence, risk perception, and risky riding behavior. On this basis, a structural equation model was constructed to explore the intrinsic causal relationships among the variables that affect the risky riding behaviors of takeaway e-bikers. The results show that the influence of incentive compensation driven by the takeaway platform was the greatest one. Takeaway riders tend to fight against time to improve punctuality and income by red-light running and speeding. They usually need to pay attention to order information and the delivery routes and communicate with customers to pick up meals in real-time, which inevitably lead to the use of cell phone while riding. Road factors such as “no turnaround at the intersection” and “no non-isolation facilities between on-motorized and motorized lane” lead riders to riding against the traffic, riding on the motor lane, and parking outside the stop line. In addition, lax traffic regulations lead to frequent loopholes for takeaway riders. It means that improving the takeaway platform system, strengthening traffic safety education, and adopting mandatory restraint measures are extremely important. The empirical results provide theoretical support for the benign and healthy development of the takeaway industry, which is significant for preventing and reducing risky behaviors of takeaway riders and improving safety at urban intersections. Full article

The integrated fast detection technology for electric bikes, riders, helmets, and license plates is of great significance for maintaining traffic safety. YOLOv5 is one of the most advanced single-stage object detection algorithms. However, it is difficult to deploy on embedded systems, such as unmanned aerial vehicles (UAV), with limited memory and computing resources because of high computational load and high memory requirements. In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5. Firstly, the YOLOv5s backbone network and the Neck part are lightened by combining the two lightweight networks, ShuffleNetv2 and GhostNet, included. Secondly, by adopting an Add-based feature fusion method, the number of parameters and the floating-point operations ($FLOPs$) are effectively reduced. On this basis, a scene-based non-truth suppression method is proposed to eliminate the interference of pedestrian heads and license plates on parked vehicles, and then the license plates of the riders without helmets can be located through the inclusion relation of the target boxes and can be extracted. To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate. The results show that, the SG-YOLOv5 has the same mean average precision ($mAP0.5$) as the original; the number of model parameters, the $FLOPs$, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively. Additionally, the number of frames per second ($FPS$) is 2.7 times higher than that of the original. Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy. Full article