Hierarchical quantitative analysis to evaluate unsafe driving behaviour from massive trajectory data. Issue 8 (29th June 2020)

• Record Type: Journal Article
• Authors: Liao, Lyuchao; Chen, Bijun; Zou, Fumin; Eben Li, Shengbo; Liu, Jierui; Wu, Xinke; Dong, Ni
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 8(2020)
• Page Start: 849
• Page End: 856
• Publication Date: 2020-06-29
• Subjects: analytic hierarchy process -- road safety -- data mining -- data analysis -- transportation -- driver information systems -- normal distribution -- human factors -- real‐time systems -- computerised monitoring; hierarchical quantitative analysis -- unsafe driving behaviour -- massive trajectory data -- large‐scale trajectory data -- UDB mining -- transportation applications -- four‐layer processing framework -- statistical distribution -- normal distribution -- membership function -- analytical hierarchy process‐based method -- real‐time driving behaviour monitoring; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0643
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : The large‐scale trajectory data provide the potential opportunity to a better understanding of driving behaviour for transportation applications and research. However, limited effort has been paid to the study on the evaluation of unsafe driving behaviour (UDB) based on trajectory data. In this work, the authors propose a four‐layer processing framework for evaluation of driving behaviour using trajectory data, and first analyse the statistical distribution of various factors and mine UDBs from trajectory data by measuring the deviation from a normal distribution. Then, a membership function is designed to evaluate the severity rating of UDBs, and finally, an analytical hierarchy process‐based method is employed to analyse UDBs both qualitatively and quantitatively. With experiments on trajectory data derived from August to September 2018 in Jiangxi, China, vehicles were classified in levels of risk, and the result shows that the proposed method offers a feasible and applicable way for transportation enterprises and drivers to monitor driving behaviour in real‐time.