The relationships between traffic enforcement, personal norms and aggressive driving behaviors among normal e-bike riders and food delivery e-bike riders

Dong, Hongming; Zhong, Shiquan; Xu, Shu-Xian; Tian, Junfang; Feng, Zhongxiang

doi:10.1016/j.tranpol.2021.09.014

Cited by 38 publications

(12 citation statements)

References 89 publications

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“…Although a large number of accidents have emerged as a painful lesson that there may be danger/loss in the situation, many delivery riders still choose risky behaviors. One important factor leading to the growing number of delivery riders’ dangerous behaviors and accidents is the strict time limits from the delivery platform [ 3 ]. Therefore, it is worth discussing why people engage in risky behaviors under time pressure caused by time limitations.…”

Section: Introductionmentioning

confidence: 99%

Time Pressure Affects the Risk Preference and Outcome Evaluation

Lin

Jia

2023

IJERPH

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It is ubiquitous that food delivery riders do not have unlimited periods of time for deliberation to make decisions. Time pressure plays a significant role in decision-making processes. This study investigated how time pressure affected risk preference and outcome evaluation through behavioral and electrophysiological responses during decision-making. Participants finished a simple gambling task under three different time constraint conditions (high/medium/low). Behavioral and event-related potentials (ERPs) data were recorded during the experiment. The results showed that the decision time of people was shorter under high time pressure than under medium and low time pressures. People tend to make more risky choices when under high time pressure. The feedback-related negativity (FRN) amplitude was smaller in the high time pressure than in medium and low time pressure conditions. These findings provided evidence that time pressure has an impact on the risk decision-making process.

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Section: Introductionmentioning

confidence: 99%

Time Pressure Affects the Risk Preference and Outcome Evaluation

Lin

Jia

2023

IJERPH

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“…Among these types of e-bike collisions, head injury is a common cause of severe morbidity and mortality. In addition, some studies have also shown that some factors, such as the road traffic characteristics [ 48 ], high-speed roads [ 49 ], weather, temperature [ 50 ], latent psychological factors [ 51 ], time pressure [ 52 ], traffic environment [ 53 ], and rider demographic factors, are also closely related to e-bike collisions. However, few studies have focused on e-bike helmet laws and regulations, particularly in China.…”

Section: Literature Reviewmentioning

confidence: 99%

“…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. According to the new policy issued by China’s Ministry of Public Security (CMPS), fines will be handed out to e-bike riders who do not wear a helmet and car drivers who fail to fasten their seatbelts.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…To deliver orders on time, workers may conduct dangerous actions including fatigue driving, speeding, or violating traffic laws during the labor process, all of which potentially harm their safety and may even threaten their lives. 36 In addition, workers who have a "servile relationship to others" are viewed as socially tainted. 11 The payment and future work allocation of delivery workers are closely related to their scoring and rating according to online rating schemes.…”

Section: Literature Review and Hypotheses Development Gig Workers And...mentioning

confidence: 99%

“…On the other hand, under strict “algorithm management” or “algorithm control,” food-delivery work tends to be highly intensive and time limited. To deliver orders on time, workers may conduct dangerous actions including fatigue driving, speeding, or violating traffic laws during the labor process, all of which potentially harm their safety and may even threaten their lives 36 …”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%