Do non-motorists understand the traffic safety laws protecting them? Results from a Chinese survey

Travel Behaviour and Society(2024)

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摘要
The government of Nanjing, China, has issued several traffic laws to protect non-motorists (i.e., pedestrians, e-bike riders, cyclists). In reality, however, many non-motorists are unfamiliar with these laws, which impairs the effectiveness of laws. On this background, this study investigates road users’ knowledge on various traffic safety laws protecting non-motorists. An online survey was conducted to collect road users’ basic information and their subjective and objective knowledge of the laws. Then, A latent class analysis model is employed to distinguish between drivers and non-motorists. Furthermore, we explore the gaps between road users’ subjective and objective knowledge, and the knowledge discrepancies between different road users. Subsequently, ordered logit models are developed to identify the influencing factors of road users’ knowledge levels. Finally, text network analysis is used to assess respondent’s comments and suggestions.The results indicate that drivers are more knowledgeable about laws compared with non-motorists. Moreover, some road users have a high level of subjective knowledge but a low level of objective knowledge regarding some laws (e.g., yielding laws). Furthermore, some factors, “group membership” and “reading-news behavior”, significantly affect both subjective and objective knowledge. “Social norm” and “perceived behavioral control” could only affect subjective knowledge, while “perceptions on fine penalty” could influence objective knowledge. Besides, respondents’ positive comments are associated with their perceived benefits from the laws, while the negative comments and suggestions mainly concern non-motorist violators and knowledge discrepancies among different road users. Finally, we provide several targeted suggestions to facilitate the promotion and implementation of traffic safety laws.
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关键词
Traffic safety laws,Knowledge,Road user group,LCA,Text network analysis
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