Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

被引:7
|
作者
Naji, Hasan A. H. [1 ]
Xue, Qingji [1 ]
Lyu, Nengchao [2 ]
Duan, Xindong [1 ]
Li, Tianfeng [1 ]
机构
[1] Nanyang Inst Technol, Sch Digital Media, Chang Jiang Rd 80, Nanyang 473004, Peoples R China
[2] Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan 430063, Peoples R China
关键词
near-crash events; driving risk levels; classification; statistical methods; machine learning; deep learning; INJURY SEVERITY; LOGISTIC-REGRESSION; PREDICTION; TIME; IDENTIFICATION; EVENTS;
D O I
10.3390/su14106032
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables related to naturalistic driving, temporal data, participants, and road geometry, among others. Hierarchical clustering was applied to categorize the near-crashes into several risk levels based on high-risk driving variables. The adaptive lasso variable model was adopted to reduce factors and select significant driving risk factors. In addition, several machine and deep learning models were used to compare near-crash classification performance by training the models and examining the model with testing data. The results showed that the deep learning models outperformed the machine learning and statistical models in terms of classification performance. The LSTM model achieved the highest performance in terms of all evaluation metrics compared with the state-of-the-art models (accuracy = 96%, recall = 0.93, precision = 0.88, and F1-measure = 0.91). The LSTM model can improve the classification accuracy and prediction of most near-crash events and reduce false near-crash classification. The finding of this study can benefit transportation safety in predicting and classifying driving risk. It can provide useful suggestions for reducing the incidence of critical events and forward road crashes.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] Distracted Driving and Risk of Crash or Near-Crash Involvement Among Older Drivers Using Naturalistic Driving Data With a Case-Crossover Study Design
    Huisingh, Carrie
    Owsley, Cynthia
    Levitan, Emily B.
    Irvin, Marguerite R.
    MacLennan, Paul
    McGwin, Gerald
    JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES, 2019, 74 (04): : 550 - 555
  • [42] GIS Mapping of Driving Behavior Based on Naturalistic Driving Data
    Balsa-Barreiro, Jose
    Valero-Mora, Pedro M.
    Berne-Valero, Jose L.
    Varela-Garcia, Fco-Alberto
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (05):
  • [43] ConvMLP for Driving Behavior Detection from Naturalistic Driving Data
    Gao, Jun
    Yi, Jiangang
    Murphey, Yi Lu
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 640 - 645
  • [44] Characterisation of motorway driving style using naturalistic driving data
    Itkonen, Teemu H.
    Lehtonen, Esko
    Selpi
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2020, 69 : 72 - 79
  • [45] DRIVING AND ALZHEIMERS-DISEASE - THE RISK OF CRASHES
    DRACHMAN, DA
    SWEARER, JM
    BARNES, HJ
    WOODWARD, BM
    PETERSON, KE
    MOONIS, M
    WEINTRAUB, S
    MORECROFT, K
    GUINESSEY, J
    ACAR, D
    SANDSON, T
    BLASS, J
    NOLAN, K
    RYAN, B
    MORRIS, JC
    BALL, LA
    FRIEDLAND, RP
    MARTIN, R
    DEKOSKY, ST
    FUQUAWHITLEY, D
    LINDEMAN, DA
    CASTLE, J
    NEUROLOGY, 1993, 43 (12) : 2448 - 2456
  • [46] Driving Maneuvers Analysis Using Naturalistic Highway Driving Data
    Li, Guofa
    Li, Shengbo Eben
    Jia, Lijuan
    Wang, Wenjun
    Cheng, Bo
    Chen, Fang
    2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, : 1761 - 1766
  • [47] Driving speed and the risk of road crashes: A review
    Aarts, L
    van Schagen, I
    ACCIDENT ANALYSIS AND PREVENTION, 2006, 38 (02): : 215 - 224
  • [48] Analysis of near crashes among teen, young adult, and experienced adult drivers using the SHRP2 naturalistic driving study
    Seacrist, Thomas
    Douglas, Ethan C.
    Huang, Elaine
    Megariotis, James
    Prabahar, Abhiti
    Kashem, Abyaad
    Elzarka, Ayya
    Haber, Leora
    MacKinney, Taryn
    Loeb, Helen
    TRAFFIC INJURY PREVENTION, 2018, 19 : S89 - S96
  • [49] Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining
    Qu, Yansong
    Li, Zhenlong
    Liu, Qin
    Pan, Mengniu
    Zhang, Zihao
    JOURNAL OF ADVANCED TRANSPORTATION, 2022, 2022
  • [50] Naturalistic Driving A New Method of Data Collection
    Winkelbauer, Martin
    Eichhorn, Anita
    Sagberg, Fridulv
    Backer-Grondahl, Agathe
    DATA AND MOBILITY: TRANSFORMING INFORMATION INTO INTELLIGENT TRAFFIC AND TRANSPORTATION SERVICES, PROCEEDINGS OF THE LAKESIDE CONFERENCE 2010, 2010, 81 : 163 - 176