Open AccessHierarchical quantitative analysis to evaluate unsafe driving behaviour from massive trajectory data
Author(s) -
Liao Lyuchao,
Chen Bijun,
Zou Fumin,
Eben Li Shengbo,
Liu Jierui,
Wu Xinke,
Dong Ni
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0643
Subject(s) - trajectory , computer science , data mining , analytic hierarchy process , work (physics) , process (computing) , function (biology) , operations research , engineering , physics , astronomy , mechanical engineering , evolutionary biology , biology , operating system
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.