Influence of familiarity with traffic regulations on delivery riders' e-bike crashes and helmet use: Two mediator ordered logit models

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.

Analysis of road traffic accidents and casualties associated with electric bikes and bicycles in Guangzhou, China: A retrospective descriptive analysis

Introduction

Electric bicycles (e-bikes) and bicycles in large Chinese cities have recently witnessed substantial growth in ridership. According to related accident trends, this study analyzed characteristics and spatial distribution in the period when e-bike-related accidents rapidly increased to propose priority measures to reduce accident casualties.

Methods

For e-bike- and bicycle-related accident data from the Guangzhou Public Security Traffic Management Integrated System, linear regression was used to examine the trends in the number of accidents and age-adjusted road traffic casualties from 2011 to 2021. Then, for the period when e-bike-related accidents rapidly increased, descriptive statistics were computed regarding rider characteristics, illegal behaviors, road types, collision objects and their accident liability. One-way analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test. P < 0.05 was considered statistically significant. Finally, the density distribution of accidents was presented, and Moran's I (MI) was used for assessing spatial autocorrelation. Hotspots were identified based on an optimized hotspot analysis tool.

Results

Between 2011 and 2021, the number of accidents and casualty rate (per 100,000 population) increased for e-bikes but decreased for bicycles. After 2018, e-bike-related accidents increased rapidly, and bicycle-related accidents plateaued. Accident hotspots were concentrated in central city areas and suburban areas close to the former. Three-quarters of accidents occurred in motorized vehicle lanes. Most occurred on roads without physically segregated nonmotorized vehicle lanes. More than three-fifths of the accidents involved motor vehicles with at least four wheels. The prevalence (per 100 people) of casualties among e-bike rider victims and cyclist victims accounted for 92.0 % and 96.5 %, respectively. A total of 71.6 % of e-bike-related accidents involved migrant workers. Riding in motorized vehicle lanes was the most common illegal behavior.

Conclusions

Although e-bike-related and bicycle-related accidents presented similar characteristics, the sharp increase in e-bike-related accidents requires attention. To improve e-bike safety, governments should develop appropriate countermeasures to prevent riders from riding on motorways, such as improving road infrastructure, adjusting the driver's license system and addressing priority control areas.

Self-reported cycling behavior and previous history of traffic accidents of cyclists

BACKGROUND

Cyclists are vulnerable traffic users and studying the cycling behavior of professional and elite cyclists, their previous history of traffic accidents combined with the current knowledge on high-risk behaviors of this group can be a useful basis for further studies on ordinary cyclists. This study aimed to determine the relationship between cycling behavior and the previous history of traffic accidents among members of the Cycling Federation of Guilan province in 2022.

METHODS

A descriptive-analytical study was performed in which the Bicycle Rider Behavior Questionnaire (BRBQ) constructed in the Porsline platform was distributed using the WhatsApp social network. All participants were asked to self-report their cycling behavior. The final analysis was performed by using STATA software (version 14).

RESULTS

The study subjects included a total of 109 cyclists with a mean age of 38.62 ± 10.94 years and a mean cycling experience of 13.75 ± 11.08 years. Using the logistic regression model, the relationship between gender (P = 0.039), years of cycling experience (P = 0.000), and education level (P ≤ 0.00), with previous traffic accidents, was found significant. There was also a significant relationship between stunts and distractions (P = 0.005), signaling violation (P = 0.000), and control error (P = 0.011) with previous traffic accidents. A significant association existed between stunts and distractions (P = 0.001) and signaling violation (P = 0.001) with a previous history of traffic injury within the last 3 years.

CONCLUSIONS

The findings of this study can be used to establish cyclist safety and preventative planning in society. In behavior change intervention programs, it is best to target male cyclists with higher-level education. In addition, the behavior of the cyclists whose predominant term of signaling violations must be corrected should be targeted. It is necessary to shape information campaigns and educational programs aimed for cyclists with common high-risk behaviors, especially signaling violations.

Global burden and trends of three common road injuries from 1990 to 2019 and the implications for prevention and intervention

BACKGROUND

Analysis on the burden of specific types of road injuries (RIs) in the previous Global burden of disease (GBD) studies is lacking. The present work aimed to analyze the burden of three common RIs using the updated data of the GBD 2019, which would inform policy-making.

METHODS

Data on cyclist road injuries (CRIs), motorcyclist road injuries (MRIs), and motor vehicle road injuries (MVRIs) were extracted from the GBD 2019. Trends of age-standardized rate (ASR) were predicted using estimated annual percentage change (EAPC) from 1990 to 2019.

RESULTS

Over the past three decades, the global incident ASRs of CRIs and MRIs presented increasing trends, but that of MVRIs declined slightly. However, trends of death and disability adjusted life years (DALYs) caused by three common RIs decreased in most regions and countries. Particularly, trends in ASRs of years of life lost (YLLs) cuased by RIs decreased more pronouncedly than that of years of life lived with disability (YLDs). The burden of three common RIs showed significant social and demographic characteristics. Low-middle and middle socio-demographic index (SDI) areas had a heavy burden of RIs, particularly CRIs and MRIs. However, the high SDI area undertook a relatively low burden, and presented more pronounced downward trends in death and DALYs.

CONCLUSIONS

The burden and changing trends of three common RIs were geographically heterogeneous. The findings highlighted that increasing incident trends of RIs needed more cost-effective measures of prevention and intervention.

Non-fatal traffic crashes among food delivery riders in Vietnam: What is the problem?

OBJECTIVE

The rapid development of information technologies and the COVID-19 pandemic have resulted in the proliferation of online food shopping and food delivery motorcyclists. In contrast to the relatively ample literature on factors influencing fatalities and risky riding behaviors of food delivery motorcyclists, little is known about the determinants of non-fatal crashes involving online food delivery riders. The present study examines the prevalence and factors of non-fatal crashes among food delivery riders.

METHODS

The self-reported data of 393 online food delivery riders were collected in Hanoi and Hochiminh city, Vietnam. Binary logit regression was used to investigate the factors associated with non-fatal crashes.

RESULTS

The findings showed that more than half of riders (54%) reported being involved in at least one crash in the last 12 months. The most common risky riding behaviors associated with the crashes included using a mobile phone while riding, neglecting turn signals, red-light running, riding when tired/sleepy, and speeding. The riders who were national migrants, married, and worked on planned delivery routes mainly alone were more likely to experience crashes. At the same time, adequate perceived rewards for their work prevent crash involvement. Perceived risk was not a significant predictor of self-reported crashes.

CONCLUSIONS

Ensuring road safety for delivery riders requires a systemic effort involving multiple stakeholders, and the private sector plays a crucial role in discouraging risky riding behaviors. It is imperative for the government and regulatory bodies to redefine the delivery job to alleviate the strain on riders and provide resources such as rewards. Specifically, riders should be considered employees rather than partners. Furthermore, it is crucial for the police to take a more active role in preventing dangerous behaviors among delivery riders, such as running red lights. At the same time, supporting financial strategies should be implemented for delivery riders, particularly for those who are migrants or married and may face additional challenges.

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

The delivery industry has seen dramatic growth in demand and scale in China. Due to the stock limitations and delivery time restrictions, the couriers may commit traffic violations while delivering, resulting in a pessimistic road safety situation. This study aims to reveal critical factors that influence delivery vehicle crash risks. A cross-sectional structured questionnaire survey is conducted to collect demographic attributes, workload, work emotions, risky driving behavior, and road crash involvement data among 824 couriers in three developed regions of China. The collected data is then analyzed through an established path model to identify the contributing factors of delivery road crash risks and risky behaviors. The road crash risk level (RCRL) indicator is defined by taking into consideration both frequency and severity. While the risky behaviors are defined by both their frequency and correlations to crash risks. The results indicate that 1) Beijing-Tianjin Urban Agglomeration has the highest road crash frequency and RCRL; 2) distracted driving and wrong-lane-use are among the top three risky behaviors for both Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration. For Beijing-Tianjin Urban Agglomeration, distracted driving, aggressive driving, and lack of protection are the top three risky behaviors; 3) time demand and personal efforts are important factors contributing to the cognitive workload of couriers; 4) objective workload can affect the cognitive workload and both workloads influence drivers' emotions (anxiety and anger); 5) the objective, cognitive workload, drivers' emotions influence the RCRL through their impacts on risky behavior but in different paths for three agglomerations. The findings highlight the importance of developing targeted countermeasures to reduce the delivery workers' workload, improve their performance on roads, and mitigate severe crash risks.

Analysis of Risky Riding Behavior Characteristics of the Related Road Traffic Injuries of Electric Bicycle Riders

Electric bicycle (EB) riders, being vulnerable road users (VRUs), are increasingly becoming victims of road traffic injuries (RTIs). This study aimed to determine the current status and epidemiological characteristics of RTIs among EB riders through a questionnaire survey and roadside observations in Shantou to provide a scientific basis for the prevention and control of electric bicycle road traffic injuries (ERTIs). A total of 2412 EB riders were surveyed, and 34,554 cyclists were observed in the study. To analyze the relationship between riding habits and injuries among EB riders, chi-square tests and multi-factor logistic regression models were employed. The findings reveal that the prevalence of ERTIs in Shantou was 4.81%, and the most affected group was children under 16 years old, accounting for 9.84%. Risky behavior was widespread among EB riders, such as the infrequent wearing of safety helmets, carrying people on EBs, riding on sidewalks, and listening to music with headphones while bicycling. Notably, over 90% of those who wore headphones while bicycling engaged in this risky behavior. The logistic regression analysis showed that honking the horn (odds ratio (OR): 2.009, 95% CI: 1.245-3.240), riding in reverse (OR: 4.210, 95% CI: 2.631-6.737), and continuing to ride after a fault was detected (OR: 2.010, 95% CI: 1.188-3.402) all significantly increased the risk of ERTIs (all p < 0.05). Risky riding behavior was significantly less observed at traffic intersections with traffic officers than at those without (all p < 0.001).

Determinants of switching behavior to wear helmets when riding e-bikes, a two-step SEM-ANFIS approach

E-bikes have become one of China's most popular travel modes. The authorities have issued helmet-wearing regulations to increase wearing rates to protect e-bike riders' safety, but the effect is unsatisfactory. To reveal the factors influencing the helmet-wearing behavior of e-bike riders, this study constructed a theoretical Push-Pull-Mooring (PPM) model to analyze the factor's relationship from the perspective of travel behavior switching. A two-step SEM-ANFIS method is proposed to test relationships, rank importance and analyze the combined effect of psychological variables. The Partial Least Squares Structural Equation Model (PLS-SEM) was used to obtain the significant influencing factors. The Adaptive Network-based Fuzzy Inference System (ANFIS), a nonlinear approach, was applied to analyze the importance of the significant influencing factors and draw refined conclusions and suggestions from the analysis of the combined effects. The PPM model we constructed has a good model fit and high model predictive validity (GOF = 0.381, R² = 0.442). We found that three significant factors tested by PLS-SEM, perceived legal norms (β = 0.234, p < 0.001), perceived inconvenience (β = -0.117, p < 0.001) and conformity tendency (β = 0.241, p < 0.05), are the most important factors in the effects of push, mooring and pull. The results also demonstrated that legal norm is the most important factor but has less effect on people with low perceived vulnerability, and low subjective norms will make people with high conformity tendency to follow the crowd blindly. This study could contribute to developing refined interventions to improve the helmet-wearing rate effectively.

What if delivery riders quit? Challenges to last-mile logistics during the Covid-19 pandemic

Notoriously precarious, hazardous, and stressful, delivery jobs became even more onerous and dangerous during the pandemic. In this study, set in Ho Chi Minh City, Vietnam, we applied Structural Equation Modelling to a large sample of primary data to measure delivery riders' intention to quit their jobs at the height of the pandemic. We found that job burnout was the key trigger to the intention to quit whereas the risk of Covid-19 infection did not directly affect this behavioral intention. Female riders, migrants, persons living with chronic diseases, and those who had seen their income decimated during the pandemic were more likely to want to quit their job. But if a mass of delivery drivers or riders had failed to show up for work, the last-mile delivery sector would have become paralysed, leaving individuals in various states of lockdown or isolation without food and supplies. As the sector is poised to retain its importance in the post-pandemic period, we recommend a number of approaches for both private companies and public policy makers to persuade riders to stay in their jobs. First and foremost, strategies to prevent and mitigate job burnout should be formulated.

Characterizing the effects of contributing factors on crash severity involving e-bicycles: a study based on police-reported data

Mitigating e-bicycle crash occurrence has become a great challenge across the world. It is of paramount importance for improving traffic safety to characterize the relationship between e-bicycle crash injury severities and contributing factors. This study positions itself at clarifying the roles of the factors in e-bicycle crashes from time, space, road, environment, rider and object characteristics. The partial proportional odds (PPOs) model as well as its elasticity analysis was employed to identify the influences based on 15,138 police-reported e-bicycle crashes in Shangyu District of Shaoxin City, China. The results evidenced that there were 12 factors having significant effects. Especially, the results emphasized the greater influences of rider gender, age, object hit and road type. Their maximum of the absolutes of elasticities was greater than 24%. Increased crash severity was associated with females, younger riders, and higher speed collisions. However, the remaining significant variables had minor effects (no more than 10%). The findings provide meaningful insights for advancing e-bicycle development, when making related policies and prioritizing safety countermeasures.

A comparison of characteristics between food delivery riders with and without traffic crash experience during delivery in Malaysia

The rapid development of e-commerce and the spread of the COVID-19 virus created many new jobs opportunity including food delivery riders known as P-Hailing riders. The number of food delivery riders has increased drastically, especially in Malaysia. Consequently, the number of food delivery riders involved in traffic crashes also increased. This study aimed to examine the characteristics of food delivery riders involved in traffic crashes during delivery and to compare with the characteristics of food delivery riders without any traffic crash history. This paper explores and compares general characteristics, previous experience of working and receiving traffic tickets, and knowledge of road safety. Due to unavailable official records about the number of active food delivery riders in Malaysia, this study focuses on riders who registered as members of the Malaysian P-Hailing Association, PENGHANTAR. A total of 225 food delivery riders participated in the online survey conducted through Google Form. Categorical data analysis techniques were used to examine the different characteristics of food delivery riders with and without traffic crash experiences. Results show that the odds ratio of young and full-time riders are respectively about 2.05 times and 1.79 times higher than being involved in traffic crashes. Other factors that increase the odds of being involved in traffic crashes include having more than two years of experience in delivery, an average distance travelled of>100 km a day, working previously in the food and grocery sector, and without working experience. The findings from this study will help related agencies to design and develop awareness programs targeting this group of riders.

Risky business: Comparing the riding behaviours of food delivery and private bicycle riders

The growth in the gig economy and a preference for home delivery of meals due to COVID-19 have led to huge growth in the food delivery business internationally and consequent road safety concerns. There is increasing evidence that delivery riding is an occupation with significant road safety risks because work pressures encourage risky behaviours. However, there is little or no research that directly compares delivery and private riders. Thus, the aim of this study was to examine the impact of riding for work by comparing the observable riding behaviours of food delivery and private bicycle riders. Specifically, this investigation used decision trees to analyse the prevalence and patterns of risky riding behaviours of 2274 bicycle food delivery riders (BFDRs) and 1127 private bicycle riders observed in the inner suburbs of Brisbane, Australia. The results showed that helmet use was higher for BFDRs than private riders (99.8% versus 93.4%) but varied by company and for some companies, female BFDRs had lower wearing rates. Male BFDRs on electric bikes were more likely to wear helmets than those on standard bikes (99.7% versus 94.9%). Using a handheld mobile phone or having a mobile phone in a cradle was less common for one company (0.6%) than for the others (3.0%) or among private riders (1.8%). Among riders from the Other Companies, using a handheld mobile phone was more common on standard bikes and differed by time of day. Female BFDRs were more likely to be observed using handheld mobile phones. Overall, 24.0% of riders facing a red traffic or pedestrian signal ("red light") did not stop. This was more common among riders who rode on the footpath (Australian term for sidewalk), and particularly those who moved between the footpath and the road on electric bikes (49.5%) and among those who rode in the wrong direction in the traffic lane (55.0%). Whether the rider was a BFDR or private rider had little influence on red light running. The results suggest that BFDRs are not more likely to perform the risky behaviours examined, but that other factors such as bicycle type, gender, time of day and infrastructure appear to be more important determinants. However, the differences among companies suggest that organisational factors deserve further investigation.

Demographics of road injuries and micromobility injuries among China, India, Japan, and the United States population: evidence from an age-period-cohort analysis

BACKGROUND

Micromobility sharing platforms have involved skyrocketing numbers of users in multiple countries since 2010. However, few studies have examined the overall impact of the growing micromobility market on road injuries.

METHOD

We use road injury data from the Global Burden of Disease Study database to examine the effect of age, period, and cohort on micromobility injury-related deaths and incidence. We compared four countries that vary in demographic background and road infrastructure. By comparing the countries, we analyzed the relationship between the trends in road injuries and these factors.

RESULTS

We found an overall upward trend in micromobility injuries. A higher risk of micromobility-related injuries was witnessed in China and the US in 2015-2019, and people older than 45 showed a growing micromobility-related mortality and incidence rate in China, India, and the US. Cohorts after 1960 showed higher micromobility injury incidence risks in China and India, but the population born after 1990 in India showed a slightly lower risk compared to those before it.

CONCLUSIONS

The boosted usage of micromobility devices explains these increasing trends. Road infrastructure and separated traffic ease the collisions from micromobility devices. The overall situation calls for improvement in legislation as well as road infrastructure.

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

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.