Investigating Different Types of Red-Light Running Behaviors among Urban e-Bike Rider Mixed Groups

Zhang, Fan; Kuai, Chenchen; Lv, Huitao; Li, Wenhao

doi:10.1155/2021/1977388

Cited by 7 publications

(3 citation statements)

References 33 publications

(104 reference statements)

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“…With the aim of enhancing the enforcement of wearing helmets for bicyclists and e-bike riders for some trips, helmet laws and related regulations have been supported by previous studies [ 19 , 54 , 55 , 56 ]. For instance, a study by Dong et al (2021) [ 52 ] found that traffic enforcement has a more powerful inhibiting effect on the aggressive driving behaviors of food delivery riders.…”

Section: Literature Reviewmentioning

confidence: 79%

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China

Zhou

Tao

Dong

et al. 2022

IJERPH

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At present, Chinese authorities are launching a campaign to convince riders of electric bicycles (e-bikes) and scooters to wear helmets. To explore the effectiveness of this new helmet policy on e-bike cycling behavior and improve existing e-bike management, this study investigates the related statistical distribution characteristics, such as demographic information, travel information, cycling behavior information and riders’ subjective attitude information. The behavioral data of 1048 e-bike riders related to helmet policy were collected by a questionnaire survey in Ningbo, China. A bivariate ordered probit (BOP) model was employed to account for the unobserved heterogeneity. The marginal effects of contributory factors were calculated to quantify their impacts, and the results show that the BOP model can explain the common unobserved features in the helmet policy and cycling behavior of e-bike riders, and that good safety habits stem from long-term safety education and training. The BOP model results show that whether wearing a helmet, using an e-bike after 19:00, and sunny days are factors that affect the helmet wearing rate. Helmet wearing, evenings during rush hour, and picking up children are some of the factors that affect e-bike accident rates. Furthermore, there is a remarkable negative correlation between the helmet wearing rate and e-bike accident rate. Based on these results, some interventions are discussed to increase the helmet usage of e-bike riders in Ningbo, China.

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Section: Literature Reviewmentioning

confidence: 79%

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China

Zhou

Tao

Dong

et al. 2022

IJERPH

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“…The biggest risk of running red lights is colliding with the intersecting motor vehicles, leading to angle collisions, which are extremely destructive. The red-light-running behaviors of e-bikes, including both food delivery e-bikes and ordinary e-bikes, have become the main cause of traffic accidents at signalized intersections in China [32]. Zhang et al [33] collected 3335 samples at four signalized intersections in Xi'an, China, and found that FDRs were more likely to run red lights than ordinary e-bike riders.…”

Section: Riding Safety Attitudesmentioning

confidence: 99%

Exploration of Riding Behaviors of Food Delivery Riders: A Naturalistic Cycling Study in Changsha, China

Zhang,

Liu

2023

Sustainability

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Aimed at the riding safety issue of food delivery riders in China who mainly travel by electric bikes, a naturalistic cycling study was conducted by collecting the naturalistic cycling data of dozens of food delivery riders in Changsha, China, to identify their riding characteristics. It was found that the participating food delivery riders are mainly undereducated young male adults, and the primary reason for them to take the job is the flexible working hours. Furthermore, they frequently work overtime and admit to often committing risky riding behaviors to deliver food on time. The analysis of their riding trajectories indicates that they delivered orders all day long, rather than just at mealtimes. They mainly work within 3 km of the delivery station, and the average riding radius was 2.39 km. Male riders, riders working less than one year, and riders with high school education had a relatively fast riding speed. These findings provide valuable new insights for agencies to understand the riding characteristics of food delivery riders and to formulate the appropriate countermeasures to improve their occupational safety.

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“…However, this could also worsen traffic safety risks on mixed-use roads. Incidents such as running red lights, speeding, and hit-and-run occurrences have become increasingly frequent [2][3][4]. Data from the National Bureau of Statistics indicates that in 2021 there were a total of 35,141 traffic accidents involving TNVs nationwide [5].…”

Section: Introductionmentioning

confidence: 99%

Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx

Zhou,

Zhao,

Yang

et al. 2024

Sensors

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Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model’s feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.

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Investigating Different Types of Red-Light Running Behaviors among Urban e-Bike Rider Mixed Groups

Cited by 7 publications

References 33 publications

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China

Exploration of Riding Behaviors of Food Delivery Riders: A Naturalistic Cycling Study in Changsha, China

Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx

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