Decision-adjusted driver risk predictive models using kinematics information

Simple item page
dc.contributor.authorMao, Huiyingen
dc.contributor.authorGuo, Fengen
dc.contributor.authorDeng, Xinweien
dc.contributor.authorDoerzaph, Zachary R.en
dc.contributor.departmentStatisticsen
dc.contributor.departmentVirginia Tech Transportation Instituteen
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.date.accessioned2021-07-26T13:38:11Zen
dc.date.available2021-07-26T13:38:11Zen
dc.date.issued2021-06en
dc.description.abstractAccurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.en
dc.description.notesThis study is partially funded by the National SafeD University Transportation Center at the Virginia Tech.en
dc.description.sponsorshipNational SafeD University Transportation Center at the Virginia Techen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.aap.2021.106088en
dc.identifier.eissn1879-2057en
dc.identifier.issn0001-4575en
dc.identifier.other106088en
dc.identifier.pmid33866156en
dc.identifier.urihttp://hdl.handle.net/10919/104399en
dc.identifier.volume156en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAutomobile crash risken
dc.subjectDecision-adjusted modelingen
dc.subjectPredictive modelingen
dc.subjectTelematics dataen
dc.subjectNaturalistic driving studyen
dc.titleDecision-adjusted driver risk predictive models using kinematics informationen
dc.title.serialAccident Analysis and Preventionen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s2.0-S0001457521001196-main.pdf
Size:
854.89 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Scholarly Works, Statistics
Destination Area: Data and Decisions (D&D)
Scholarly Works, Biomedical Engineering and Mechanics
Scholarly Works, Virginia Tech Transportation Institute