Search Results (240)

While there are studies on bicycle crashes, no study has investigated the spatial analysis of fatal and injury bicycle crashes in the state of Ohio. This study fills this gap in the literature by mapping and investigating the bicycle crash-prone areas in the state. It analyzes fatal and injury bicycle crashes from 2014 to 2023 by utilizing four exploratory spatial data analysis techniques: nearest neighbor index, global Moran’s I index, hotspot and cold spot analysis, and local Moran’s I index at the state, county, census tract, and block group levels. Results vary slightly across techniques and spatial scales but consistently show that bicycle crash locations are clustered statewide, particularly in the state’s major metropolitan areas such as Columbus, Cincinnati, Toledo, Cleveland, and Akron. These urban centers have emerged as hotspots, indicating a higher vulnerability to bicycle crashes. While global Moran’s I analysis at the county level does not reveal significant spatial autocorrelation, a strong positive autocorrelation is observed at both the census tract (p = 0.01) and block group (p = 0.00) levels, indicating significant high clustering, signifying that finer geographical units yield more robust results. Identifying specific hotspots and vulnerable areas provides valuable insights for policymakers and urban planners to implement effective safety measures and improve conditions for non-motorized road users in Ohio. The study highlights the need for targeted mitigation strategies in high-risk areas, including comprehensive safety measures, infrastructure improvements, policy changes, and community-focused initiatives to reduce crash risk and create safer environments for cyclists throughout Ohio’s urban fabric. Full article

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Road irregularities generate vibrations that are transmitted to cyclists’ hands. This paper describes a purpose-designed laboratory setup and data processing method to assess vibration-induced numbness and discomfort. The rear wheel of a road bike was coupled with a smart trainer for indoor cycling, while the front wheel was supported by a vibrating platform to simulate road–bike interaction. The vibrotactile perception threshold (VPT) is measured in the fingers, and a questionnaire was used to assess the discomfort in different parts of the hand using a unipolar scale. To validate the method, ten male volunteers underwent two one-hour cycling sessions, one for each of the two handlebar designs tested. VPT was measured in the index and little fingers of the right hand at 8 and 31.5 Hz before and after each session, while the discomfort questionnaire was completed at the end of each session. The discomfort scores showed a strong inter-subject variability, indicating the necessity to combine them with the objective measurements of the VPT, which is shown to be sensitive in identifying the perception shift due to vibration exposure and the differences between the fingers. This study demonstrates the effectiveness of the proposed method for assessing hand numbness and discomfort in cyclists. Full article

This study aimed to describe the training leading to the Zwift Academy (ZA) Finals of a world-class road cyclist who earned a professional contract after winning the contest. Four years of daily power meter data were analyzed (male, 25 years old, 68 kg, VO₂max: 85 mL·min⁻¹·kg⁻¹, and 20-min power: 6.37 W·kg⁻¹), focusing on load, volume, intensity, and strategies. Early training alternated between long, moderate-intensity sessions and shorter high-intensity sessions, with easy days in between. Gradually, the structure was progressively modified by increasing the duration of moderate-intensity (MIT) and high-intensity (HIT) and, subsequently, moving them to “high-volume days”, creating a sort of “all-in days” with low-intensity (LIT), MIT, and HIT. Moderate use of indoor training and a few double low-volume, low-intensity sessions were noted. These data provide a deep view of a 4-year preparation period of ZA, providing suggestions for talent identification and training, thereby highlighting the importance of gradual progression in MIT and HIT. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

In the present era, achieving sustainability requires the development of planning strategies to develop a safer urban infrastructure. This study examines the realistic aspects of cyclist safety by analysing cyclists’ fields of view, using Geographic Information Systems (GIS) and spatial data analysis. The research introduces novel geoprocessing tools-based GIS techniques that mathematically simulate cyclists’ angles of view and the distances to nearby environmental features. It provides precise insights into some potential hazards and infrastructure challenges encountered while cycling. This research focuses on managing and analysing the data collected, utilising OpenStreetMap (OSM) as vector-based supporting data. It integrates cyclists’ behavioural data with the urban environmental features encountered, such as intersections, road design, and traffic controls. The analysis is categorised into specific classes to evaluate the impacts of these aspects of the environment on cyclists’ behaviours. The current investigation highlights the importance of integrating the objective environmental elements surrounding the route with subjective perceptions and then determining the influence of these environmental elements on cyclists’ behaviours. Unlike previous studies that ignore cyclists’ visual perspectives in the context of real-world data, this work integrates objective GIS data with cyclists’ field of view-based modelling to identify high-risk areas and highlight the need for enhanced safety measures. The proposed approach equips urban planners and designers with data-informed strategies for creating safer cycling infrastructure, fostering sustainable mobility, and mitigating urban congestion. Full article

This study aimed to (1) determine and compare the magnitude and direction of asymmetry in lower limbs neuromuscular properties, range of motion, strength and muscle electrical activity (EMG) in well-trained male road cyclist across categories (elite, under-23 and junior); (2) establish test- and age-specific asymmetry thresholds for these variables to enable individualized classification; and (3) examine the relationship between these lateral asymmetries and performance in a maximal incremental cycle ergometer test. Fifty-five well-trained road cyclists were assessed through tensiomyography (TMG), active knee extension test (AKE), leg press and EMG of vastus lateralis (VL-EMG) during a maximal incremental cycling test. Junior cyclists showed lower asymmetry in VM than elite cyclists, but greater asymmetry in AKE. No significant differences were found in strength or VL-EMG during the maximal incremental cycle ergometer test. The magnitude and direction of lateral asymmetry differs between tests (TMG: 11.3–21.3%; AKE: 2.3%; leg-press: 9.8–31.9%; VL-EMG: 20.8–22.7%). Multiple linear regression revealed a significant predictive model for maximal incremental cycling ergometer performance based on lateral asymmetry in AKE, leg press and VL and rectus femoris contraction time (R²_a = 0.23). These reference data can support trainers in monitoring and managing lateral asymmetry throughout the cyclists’ season. Full article

This study presents a systematic bibliometric review of wearable technologies aimed at vulnerable road user (VRU) safety, covering publications from 2000 to 2025. Guided by PRISMA procedures and a PICo-based search strategy, 58 records were extracted and analyzed in CiteSpace, yielding visualizations of collaboration networks, publication trajectories, and intellectual structures. The results indicate a clear evolution from single-purpose, stand-alone devices to integrated ecosystem solutions that address the needs of diverse VRU groups. Six dominant knowledge clusters emerged—street-crossing assistance, obstacle avoidance, human–computer interaction, cyclist safety, blind navigation, and smart glasses. Comparative analysis across pedestrians, cyclists and motorcyclists, and persons with disabilities shows three parallel transitions: single- to multisensory interfaces, reactive to predictive systems, and isolated devices to V2X-enabled ecosystems. Contemporary research emphasizes context-adaptive interfaces, seamless V2X integration, and user-centered design, and future work should focus on lightweight communication protocols, adaptive sensory algorithms, and personalized safety profiles. The review provides a consolidated knowledge map to inform researchers, practitioners, and policy-makers striving for inclusive and proactive road safety solutions. 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

Object detection in complex road scenes is vital for autonomous driving, facing challenges such as object occlusion, small target sizes, and irregularly shaped targets. To address these issues, this paper introduces DGSS-YOLOv8s, a model designed to enhance detection accuracy and high-FPS performance within the You Only Look Once version 8 small (YOLOv8s) framework. The key innovation lies in the synergistic integration of several architectural enhancements: the DCNv3_LKA_C2f module, leveraging Deformable Convolution v3 (DCNv3) and Large Kernel Attention (LKA) for better the capture of complex object shapes; an Optimized Feature Pyramid Network structure (Optimized-GFPN) for improved multi-scale feature fusion; the Detect_SA module, incorporating spatial Self-Attention (SA) at the detection head for broader context awareness; and an Inner-Shape Intersection over Union (IoU) loss function to improve bounding box regression accuracy. These components collectively target the aforementioned challenges in road environments. Evaluations on the Berkeley DeepDrive 100K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets demonstrate the model’s effectiveness. Compared to baseline YOLOv8s, DGSS-YOLOv8s achieves mean Average Precision (mAP)@50 improvements of 2.4% (BDD100K) and 4.6% (KITTI). Significant gains were observed for challenging categories, notably 87.3% mAP@50 for cyclists on KITTI, and small object detection (AP-small) improved by up to 9.7% on KITTI. Crucially, DGSS-YOLOv8s achieved high processing speeds suitable for autonomous driving, operating at 103.1 FPS (BDD100K) and 102.5 FPS (KITTI) on an NVIDIA GeForce RTX 4090 GPU. These results highlight that DGSS-YOLOv8s effectively balances enhanced detection accuracy for complex scenarios with high processing speed, demonstrating its potential for demanding autonomous driving applications. Full article

Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort. Full article

Traffic congestions due to construction and maintenance works of road infrastructure cause travel delays, unpredictability and less tolerant road users. Bicyclists are more flexible with road closures, shifting to alternative routes, public transport and other active transport depending on the infrastructure, quality and transport services. However, the mixed traffic environment near road closures increases the safety risks for bicyclists. Traditional traffic monitoring systems rely on costly and demanding intrusive sensors. The application of wireless sensors and machine learning algorithms can enhance the analysis and prediction ability of traffic distribution and characteristics in the proximity of road closures. This paper applies artificial neural networks (ANNs) coupled with a Generalised Delta Rule (GDR) algorithm to analyse the sensor traffic data before, during and after the closure of the Mill Road Bridge in Cambridge City in the United Kingdom. The ANN models show that the traffic volume of motorbikes (44%) and buses (34%) and the proximity of Mill Road Bridge (39%) are significant factors affecting bicycle traffic before the closure. During the bridge closure, the proximity of the bridge (99%) and traffic volume of large rigid vehicles (51%) are the most important factors of bicycle distribution in nearby streets leading cyclists to unsafe detours. After the reopening of the Mill Road Bridge, unclear signage caused continued traffic impact, with motorbikes (17%) and large vehicles (24%) playing the most significant role in the spatial distribution of bicycle traffic. This paper emphasises safety concerns from mixed traffic and highlights the importance of cost-effective sensor-based traffic monitoring and analysis of the sensor data using neural networks. Full article

This study analyzes and compares sports supplement (SS) consumption among federated road cyclists, considering sex and competition category. The aim is to identify key factors influencing SS use and provide insights for developing nutritional strategies in cycling. A cross-sectional, descriptive study was conducted, involving 1503 cyclists (1231 men and 272 women). Data were collected through a validated questionnaire assessing anthropometric data, training habits, SS consumption patterns, and sources of information. Results indicate that 64.3% of cyclists currently use SS. Women reported a significantly higher consumption rate (88.2%) compared to men (59.1%), although men had a higher average SS intake than women (8.28 ± 9.36 vs. 6.76 ± 5.96). Additionally, SS use decreased with age and competition level, with elite cyclists showing the highest prevalence (76.3%) and master 50 the lowest (58.4%). Group A supplements (scientifically supported) were the most frequently used, while Group C supplements (limited evidence) and Group D substances (prohibited) were more commonly consumed by men. Findings highlight significant differences in SS consumption based on sex and competition level, with elite cyclists and women reporting higher prevalence. However, men reported a higher average number of SS consumed. The study underscores the need for targeted nutritional education, particularly among master cyclists, to promote evidence-based SS use and minimize the risks of ineffective or unsafe supplementation. Future research should explore the long-term effects of SS consumption in cycling and the effectiveness of educational interventions for safe and optimized supplementation practices. Full article

In recent years, the safety of vulnerable road users (VRUs), such as pedestrians, cyclists, and micro-mobility users, has become an increasingly significant concern in urban transportation systems worldwide. Reliable and accurate detection of VRUs is essential for effective safety protection. This survey explores the techniques and methodologies used to detect VRUs, ranging from conventional methods to state-of-the-art (SOTA) approaches, with a primary focus on infrastructure-based detection. This study synthesizes findings from recent research papers and technical reports, emphasizing sensor modalities such as cameras, LiDAR, and RADAR. Furthermore, the survey examines benchmark datasets used to train and evaluate VRU detection models. Alongside innovative detection models and sufficient datasets, key challenges and emerging trends in algorithm development and dataset collection are also discussed. This comprehensive overview aims to provide insights into current advancements and inform the development of robust and reliable roadside detection systems to enhance the safety and efficiency of VRUs in modern transportation systems. Full article

Road safety for vulnerable road users, particularly cyclists, remains a critical global issue. This study explores the potential of multimodal visual and haptic interaction technologies to improve cyclists’ perception of and responsiveness to their surroundings. Through a systematic evaluation of various visual displays and Haptic Feedback mechanisms, this research aims to identify effective strategies for recognizing and localizing potential traffic hazards. Study 1 examines the design and effectiveness of Visual Feedback, focusing on factors such as feedback type, traffic scenarios, and target locations. Study 2 investigates the integration of Haptic Feedback through wearable vests to enhance cyclists’ awareness of peripheral vehicular activities. By conducting experiments in realistic traffic conditions, this research seeks to develop safety systems that are intuitive, cognitively efficient, and tailored to the needs of diverse user groups. This work advances multimodal interaction design for road safety and aims to contribute to a global reduction in traffic incidents involving vulnerable road users. The findings offer empirical insights for designing effective assistance systems for cyclists and other non-motorized vehicle users, thereby ensuring their safety within complex traffic environments. Full article