An Empirical Study on High-Risk Driving Behavior to Urban-Scale Pattern in China

Abstract

Discovering the behavior pattern of urban high-risk driving is of great significance for national economy development and social stability. With increasing traffic accidents and crimes on current urban roads, the government endeavors to reduce the traffic violation rate. Exploring the behavior pattern of urban high-risk driving is an effective approach to prevent traffic accidents. In this paper, we explore the urban-scale high-risk driving behavior pattern in China. In order to achieve this goal, we analyze the death records of traffics among more than 300 cities throughout the country. In particular, we first build a city network, in which a node represents the properties of the traffic violation for the discovery of the urban-scale high-risk driving behavior pattern. Six urban-scale high-risk driving behavior patterns about overspeeding, nonlicense, drunk, and violation of the traffic rules are discovered from the traffic violation city network data. The research result leads to three key conclusions. First, urban networks based on death records information show strong spatial dependence and hierarchical characteristics and are largely coupled with the space distribution of major cities in China, reflecting the spatial relationship and core-periphery structure of high-risk driving behavior on the urban-scale. Second, to a large extent, there is a frequent occurrence of death record information around or in major domestic cities, economic belts, and important coastal ports, such as Beijing, Tianjin, Shanghai, the Yangtze River Delta Economic Belt, and the Pearl River Delta Economic Belt. This phenomenon demonstrates the attractiveness of large cities and regions as a domestic economic and political center. Third, the geographical location of the city has an impact on the pattern of high-risk driving behavior. For example, Tangshan city, the largest production base of mineral resources in China, possessing several transportation routes and heavy vehicle flows on the road, shows an increasing probability of violation of the traffic law.

Keywords

• High-risk driving behavior
• traffic
• city network
• urban-scale pattern