The road safety and risky behavior analysis of delivery vehicle drivers in China

Multi-scenario driving style research based on driving behavior pattern extraction

Accurately analyzing drivers' driving styles is crucial for road safety and enhancing intelligent driving systems. However, existing studies have not fully explored the hidden information in driving sequences or considered the influence of driving environments on driving styles. Based on natural driving data from electric vehicles in Wuhan, a framework for driving style analysis based on driving behavior pattern extraction was proposed. Driving sequences were extracted under free-driving and car-following scenarios, where the convergence of driving features was verified using kernel density estimation and relative entropy. A driving propensity indicator based on a dynamic threshold was constructed, and combined with the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM) and K-means clustering algorithm, 4 and 5 types of driving behavior pattern were extracted under free-driving and car-following scenarios, respectively. Energy consumption distribution was introduced to verify the validity of driving pattern extraction. Jensen-Shannon (JS) divergence was utilized to calculate the difference in the distribution of the driving propensity indicator among different drivers. By quantifying behavioral differences, drivers were categorized into aggressive, moderate, and conservative types. The results show that the statistical characteristics of driving patterns are consistent with the distribution of energy consumption, with the highest energy consumption occurs in aggressive acceleration and high-speed steady-state patterns, and the highest braking energy recovery occurs in aggressive deceleration pattern. Furthermore, the driving environment influences driving styles to certain degree while exhibiting consistent or diverse driving styles in different driving scenarios and patterns.

Investigation of non-fatal occupational injury and their causes among food delivery riders in China

The increasing popularity of online food delivery has provided multiple job opportunities. Although food delivery riders benefit from work flexibility, they face occupational risk and suffer accidents and injuries. This study attempts to provide a new perspective on protective measures for delivery riders by examining the causes of injuries at the individual level. A respondent-driven sampling method was used to control data bias, and a total of 1092 online food delivery riders in Beijing, Shanghai and Jinan participated in the survey. The results indicated that: good personal norms are negatively related to non-fatal occupational injury among riders; perceived risk mediates this relationship; and safety attitudes moderate the relationship. These results may help platform enterprises to voluntarily implement more effective and comprehensive measures to ensure the safety of riders, while also inspiring government when developing labor protection regulation for riders.

The effects of role stressors and smartphone interactions on delivery riders' unsafe behaviors during the delivery process in China

Objectives. Although some studies have shown role stressors can lead to unsafe behaviors, it is unclear how role stressors induce delivery riders' unsafe behaviors. We found that delivery riders suffered from tremendous role stressors during the delivery process and had to conduct frequent smartphone interactions. This study aimed to explore the effects of role stressors and smartphone interactions on delivery riders' unsafe behaviors. Methods. First, a questionnaire survey (N = 326) was used to collect data, and correlation and regression analyses were conducted to explore the relationship between role stressors, smartphone interaction frequency and delivery riders' unsafe behaviors. Second, a scenario survey (N = 35) was conducted, and comparative analysis was used to further explore how smartphone interactions affect delivery riders' unsafe behaviors. Results. The questionnaire survey revealed that role stressors, smartphone interaction frequency and delivery riders' unsafe behaviors were positively correlated. In addition, the role stressors forced delivery riders to conduct necessary and unnecessary smartphone interactions. The scenario survey found that smartphone interactions reduced delivery riders' motion speed and motion ability, and increased their psychology, so they had a risk-taking mentality, which led to an increase in unsafe behaviors.

Exploring the heterogeneous effects of riding behaviours and road conditions on delivery rider severities in scooter-style electric bicycle crashes involving vehicles

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.

Analyzing the impact of bicycle geometry and cargo loading on the rideability and safety of cargo bikes: An investigative study

Introduction

Electric cargo bikes have become popular for transporting goods and people due to their small size and strong carrying capacity. However, the way they perform, handle, and operate safely can be affected by the weight of the cargo, where it is placed on the bike, and the bike's design.

Method

This paper analyzes the rideability and safety of eight different cargo bikes representing three different design categories, Retrofitted, Long-john, and Long-tail bikes, also considering three different cargo loading locations. We quantitatively examined the rideability by computing the minimum speed for self-stability, the maximum possible acceleration and deceleration without losing wheel-ground contact, the handlebar torque for steady-state turning, and the force required to overcome obstacles. The effect of using powerful motorized wheels has also been discussed.

Results

Long-john cargo bikes are unstable for lightweight cargo loads, more sensitive to cargo loads, and therefore may not be suitable for riding in narrow, crowded spaces like footpaths. Moreover, retrofitted cargo bikes should only be used to carry lightweight cargo as a combination of heavy cargo load and a powerful rear wheel motor poses a potential risk of accidents. Long-tail cargo bikes are less affected by changes to the cargo load and are thus safer than retrofitted bikes. Their relatively compact length also makes for a smaller turning radius.

Conclusion

Rideability and safe handling of the cargo bikes strongly depend on the bike design and load and loading position. Retrofitted bikes are not suitable for carrying heavy loads and any load at the front has an adverse effect on the overall rideability and safety.

Practical application

The results highlight the benefits and limitations of different cargo bike designs and, therefore, could have implications for the cargo bike manufacturers, service providers, and policymakers.

Kalankesh L, Zeinalzadeh AH, Ghaffari A, Dastgiri S. Development, Validation, and Cross Cultural Adoption of Persian Version of Behavioral Risk Factor Tool

Background

One of the most crucial objectives of policymakers is to enhance the population's overall health. Establishing a surveillance system is a way to achieve this goal. The Behavioral Risk Factor Surveillance System (BRFSS) is a national system that collects data on the health-related behaviors of the United States residents using the Behavioral Risk Factor Questionnaire (BRFSSQ). This survey is aimed at reducing risk behaviors and their consequences. Regarding the fact that the cultural environment within each country may affect how behaviors are assessed, this study aimed to develop a Persian version, cross-cultural adaptation, and assess the validity and reliability of the PBRFSSQ.

Methods

In this cross-sectional study, 250 individuals were enrolled using the stratified sampling method between August 2022 and April 2023. Six steps of translation and test method proposed by Sousa et al was used. Content and face validity were calculated. Also, the Cronbach's alpha and test-retest were computed.

Results

Of all participants, 54.5% were male and aged 30 to 65 years old (69%). The Scale Content Validity Index was equal to 0.95. The Intra class Correlation Coefficient (ICC) was computed as 0.86, 0.88, and 0.87 for the core, optional, and total components, respectively. Furthermore, the Cronbach's alpha coefficient of 0.85 was obtained overall.

Conclusion

This tool was highly valid and reliable for assessing risky behaviors among the Iranian general population.