{"title":"Risk prediction model for distracted driving: Characterizing interactions of eye glances and manual sequences","authors":"Sixian Li, Dalin Qian, Pengcheng Li, Xinwu Yuan, Qiong Fang","doi":"10.1016/j.tbs.2024.100851","DOIUrl":null,"url":null,"abstract":"<div><p>Distracted driving substantially undermines road safety, and the frequent, low-accuracy distracted driving risk prediction alarms may detrimentally affect drivers’ judgments. The study aims to improve risk prediction accuracy by mapping the distinctive interactions of eye glances and manual sequences in distracted driving into a graph structure of nodes and edges. Distraction patterns are categorized as visual distractions (VD), manual distractions (MD), and visual-manual distractions (VMD) based on daily driving behaviours, such as eating, drinking, reaching for something, and using mobile phones. 1,806 distraction alarm records came from an active safety platform (ASP) in Beijing, covering 69 drivers from 23 hazardous materials road transport companies. The study extracts characteristics to assess distracted driving risks, including visual, manual, and driving performance features. Subsequently, unsupervised learning is used to cluster risk features into three categories (low, medium, and high), which serve as labels for the risk prediction model. In addition, each distraction alarm sequence is divided into nodes and edges of a graph. More specifically, five visual areas, forward (F), object of distraction (Dis), left window (LW), rear-view mirror (RM), center dashboard (C), as well as manual sequences, single hand (H), double hands (2H) are represented as nodes. The edges are connected in parallel (occurring simultaneously) or in series (occurring sequentially), with arrows pointing from the earlier node to the later one. Furthermore, coupled with time, global, and environmental features, a temporal graph attention network (TGAT) with integrated time functions and multi-head attention mechanisms is developed to predict distracted driving risks. The results indicated that VMD demanded more visual and manual resources and led to more high-risk alarms than VD and MD. Besides, TGAT reached a promising result, outperforming other time series methods. This study is valuable for driver distraction monitoring and driving risk assessment, significantly contributing to the enhancement of road safety.</p></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Travel Behaviour and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214367X24001145","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Distracted driving substantially undermines road safety, and the frequent, low-accuracy distracted driving risk prediction alarms may detrimentally affect drivers’ judgments. The study aims to improve risk prediction accuracy by mapping the distinctive interactions of eye glances and manual sequences in distracted driving into a graph structure of nodes and edges. Distraction patterns are categorized as visual distractions (VD), manual distractions (MD), and visual-manual distractions (VMD) based on daily driving behaviours, such as eating, drinking, reaching for something, and using mobile phones. 1,806 distraction alarm records came from an active safety platform (ASP) in Beijing, covering 69 drivers from 23 hazardous materials road transport companies. The study extracts characteristics to assess distracted driving risks, including visual, manual, and driving performance features. Subsequently, unsupervised learning is used to cluster risk features into three categories (low, medium, and high), which serve as labels for the risk prediction model. In addition, each distraction alarm sequence is divided into nodes and edges of a graph. More specifically, five visual areas, forward (F), object of distraction (Dis), left window (LW), rear-view mirror (RM), center dashboard (C), as well as manual sequences, single hand (H), double hands (2H) are represented as nodes. The edges are connected in parallel (occurring simultaneously) or in series (occurring sequentially), with arrows pointing from the earlier node to the later one. Furthermore, coupled with time, global, and environmental features, a temporal graph attention network (TGAT) with integrated time functions and multi-head attention mechanisms is developed to predict distracted driving risks. The results indicated that VMD demanded more visual and manual resources and led to more high-risk alarms than VD and MD. Besides, TGAT reached a promising result, outperforming other time series methods. This study is valuable for driver distraction monitoring and driving risk assessment, significantly contributing to the enhancement of road safety.
期刊介绍:
Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.