Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model

ABSTRACT

This paper explores the joint effects of economic development, demographic characteristics and road network on road safety. Although extensive efforts have been undertaken to model safety effects of various influential factors, little evidence is provided on the relative importance of explanatory variables by accounting for their mutual interactions and non-linear effects. We present an innovative gradient boosting decision tree (GBDT) model to explore joint effects of comprehensive factors on four traffic accident indicators (the number of traffic accidents, injuries, deaths, and the economic loss). A total of 27 elaborated influential factors in Zhongshan, China during 2000–2016 are collected. Results show that GBDT not only presents high prediction accuracy, but can also handle the multicollinearity between explanatory variables; more importantly, it can rank the influential factors on traffic accidents. We also investigate the partial effects of key influential factors. Based on key findings, we highlight the practical insights for planning practice.

KEYWORDS:

• Traffic accidents
• socio-economic
• demographics
• relative importance
• gradient boosting decision tree
• partial effect

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ronghui Liu http://orcid.org/0000-0003-0627-3184

Additional information

Funding

This work is jointly supported by Science and Technology Program of Guangzhou, China (Project No. 201904010202), and the National Natural Science Foundation of China (Project No. 61703165; 71861023; 71890972; 71890970).