PMID- 37058902
OWN - NLM
STAT- MEDLINE
DCOM- 20230424
LR  - 20230424
IS  - 1879-2057 (Electronic)
IS  - 0001-4575 (Linking)
VI  - 186
DP  - 2023 Jun
TI  - An interpretable prediction model of illegal running into the opposite lane on 
      curve sections of two-lane rural roads from drivers' visual perceptions.
PG  - 107066
LID - S0001-4575(23)00113-6 [pii]
LID - 10.1016/j.aap.2023.107066 [doi]
AB  - Illegal running into the opposite lane (IROL) on curve sections of two-lane rural 
      roads is a frequently hazardous behavior and highly prone to fatal crashes. 
      Although driving behaviors are always determined by the information from drivers' 
      visual perceptions, current studies do not consider visual perceptions in 
      predicting the occurrence of IROL. In addition, most machine learning methods 
      belong to black-box algorithms and lack the interpretation of prediction results. 
      Therefore, this study aims to propose an interpretable prediction model of IROL 
      on curve sections of two-lane rural roads from drivers' visual perceptions. A new 
      visual road environment model, consisting of five different visual layers, was 
      established to better quantify drivers' visual perceptions by using deep neural 
      networks. In this study, naturalistic driving data was collected on curve 
      sections of typical two-lane rural roads in Tibet, China. There were 25 input 
      variables extracted from the visual road environment, vehicle kinematics, and 
      driver characteristics. Then, XGBoost (eXtreme Gradient Boosting) and SHAP 
      (SHapley Additive exPlanation) methods were combined to build a prediction model. 
      The results showed that our prediction model performed well, with an accuracy of 
      86.2% and an AUC value of 0.921. The average lead time of this prediction model 
      was 4.4 s, sufficient for drivers to respond. Due to the advantages of SHAP, this 
      study interpreted the impacting factors on this illegal behavior from three 
      aspects, including relative importance, specific impacts, and variable 
      dependency. After offering more quantitative information on the visual road 
      environment, the findings of this study could improve the current prediction 
      model and optimize road environment design, thereby reducing IROL on curve 
      sections of two-lane rural roads.
CI  - Copyright (c) 2023 Elsevier Ltd. All rights reserved.
FAU - He, Li
AU  - He L
AD  - Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 
      College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, 
      Shanghai 201804, China. Electronic address: 2131290@tongji.edu.cn.
FAU - Yu, Bo
AU  - Yu B
AD  - Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 
      College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, 
      Shanghai 201804, China. Electronic address: boyu@tongji.edu.cn.
FAU - Chen, Yuren
AU  - Chen Y
AD  - Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 
      College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, 
      Shanghai 201804, China. Electronic address: chenyr@tongji.edu.cn.
FAU - Bao, Shan
AU  - Bao S
AD  - University of Michigan Transportation Research Institute, 2901 Baxter Rd, Ann 
      Arbor, MI 48109-2150, USA. Electronic address: shanbao@umich.edu.
FAU - Gao, Kun
AU  - Gao K
AD  - Department of Architecture and Civil Engineering, Chalmers University of 
      Technology, Gothenburg SE-412 96, Sweden. Electronic address: gkun@chalmers.se.
FAU - Kong, You
AU  - Kong Y
AD  - College of Transport and Communications, Shanghai Maritime University, No.1550, 
      Haigang Avenue, Lin'gang Xincheng, Pudong, Shanghai 201303, China. Electronic 
      address: kongyou@shmtu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20230413
PL  - England
TA  - Accid Anal Prev
JT  - Accident; analysis and prevention
JID - 1254476
SB  - IM
MH  - Humans
MH  - *Accidents, Traffic/prevention & control
MH  - Rural Population
MH  - *Automobile Driving
MH  - Visual Perception
MH  - Environment Design
OTO - NOTNLM
OT  - Curve sections of two-lane rural roads
OT  - Deep neural networks
OT  - Illegal running into the opposite lane
OT  - Interpretable machine learning
OT  - Naturalistic driving data
OT  - Visual road environment quantification
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/04/15 06:00
MHDA- 2023/04/24 06:42
CRDT- 2023/04/14 18:05
PHST- 2022/09/21 00:00 [received]
PHST- 2022/10/31 00:00 [revised]
PHST- 2023/04/02 00:00 [accepted]
PHST- 2023/04/24 06:42 [medline]
PHST- 2023/04/15 06:00 [pubmed]
PHST- 2023/04/14 18:05 [entrez]
AID - S0001-4575(23)00113-6 [pii]
AID - 10.1016/j.aap.2023.107066 [doi]
PST - ppublish
SO  - Accid Anal Prev. 2023 Jun;186:107066. doi: 10.1016/j.aap.2023.107066. Epub 2023 
      Apr 13.