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首页> 外文期刊>Accident Analysis & Prevention >The application and extension of the theory of planned behavior to an analysis of delivery riders' red-light running behavior in China
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The application and extension of the theory of planned behavior to an analysis of delivery riders' red-light running behavior in China

机译:计划行为理论的应用与延伸对中国送货车手红光运行行为分析的影响

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摘要

Delivery riders, an occupation that has emerged from China's booming E-commerce industry, have attracted widespread attention due to their red-light running (RLR) and high accident rates. This study aimed to utilize the theory of planned behavior (TPB) to investigate the psychological characteristics of delivery riders' RLR intentions. A survey questionnaire was designed to collect data, including information regarding the extended variables, the basic components of the TPB and demographic characteristics. The survey was conducted in Xi'an, and 228 complete questionnaires were collected. Structural equation modeling was used to examine the data, and a multiple group analysis of the demographic variables was conducted. The results showed that the expanded TPB model had a better model fit and higher variance explanation than the original TPB model. Extended constructs, i.e., conformity tendency (CT) and the traffic environment (TE), were significant predictors, and attitude was the strongest predictor of all the examined variables related to RLR intentions. Finally, the path parameters of the expended TPB model were adapted for different demographic groups, and some differential effects were also found. These results could provide a basis for the design of intervention measures and safety education schemes by delivery platforms and traffic management departments to reduce RLR behavior among delivery riders.
机译:送货车手,从中国蓬勃发展的电子商务行业出现的职业,由于他们的红灯奔跑(RLR)和高意外的速率而引起了广泛的关注。本研究旨在利用计划行为(TPB)的理论来调查送货车队的心理特征。调查问卷旨在收集数据,包括有关扩展变量的信息,TPB的基本组件和人口统计学特征。调查是在西安进行的,收集了228份完整的问卷。结构方程建模用于检查数据,并进行了人口变量的多组分析。结果表明,扩展的TPB模型具有比原始TPB模型更好的模型拟合和更高的方差说明。扩展构建体,即符合性趋势(CT)和交通环境(TE),是重要的预测因子,态度是与RLR意图相关的所有检查变量的最强预测因子。最后,消耗的TPB模型的路径参数适用于不同的人口组,也发现了一些差异效果。这些结果可以通过交付平台和交通管理部门设计干预措施和安全教育方案的基础,以减少送货车队之间的RLR行为。

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