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The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

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dc.citedby8
dc.contributor.authorLiu L.en_US
dc.contributor.authorGao Z.en_US
dc.contributor.authorLuo P.en_US
dc.contributor.authorDuan W.en_US
dc.contributor.authorHu M.en_US
dc.contributor.authorMohd Arif Zainol M.R.R.en_US
dc.contributor.authorZawawi M.H.en_US
dc.contributor.authorid57194520601en_US
dc.contributor.authorid58629628400en_US
dc.contributor.authorid42661996000en_US
dc.contributor.authorid55235393800en_US
dc.contributor.authorid55312382200en_US
dc.contributor.authorid57193313971en_US
dc.contributor.authorid39162217600en_US
dc.date.accessioned2024-10-14T03:18:07Z
dc.date.available2024-10-14T03:18:07Z
dc.date.issued2023
dc.description.abstractRapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4437
dc.identifier.doi10.3390/rs15184437
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85173038316
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85173038316&doi=10.3390%2frs15184437&partnerID=40&md5=62bd92f9042fddb6109b580382aa56fe
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34137
dc.identifier.volume15
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleRemote Sensing
dc.subjectdeep learning
dc.subjectdriving performance
dc.subjectremote sensing
dc.subjectsemantic segmentation
dc.subjectstreet view image
dc.subjecttraffic safety
dc.subjectvisual landscape elements
dc.subjectAccident prevention
dc.subjectAutomobile drivers
dc.subjectDeep learning
dc.subjectHeart
dc.subjectHighway planning
dc.subjectMotor transportation
dc.subjectPopulation statistics
dc.subjectRemote sensing
dc.subjectSemantic Segmentation
dc.subjectSemantics
dc.subjectUrban growth
dc.subjectDeep learning
dc.subjectDriver fatigue
dc.subjectDriving performance
dc.subjectLandscape elements
dc.subjectLandscape feature
dc.subjectRemote-sensing
dc.subjectSemantic segmentation
dc.subjectStreet view image
dc.subjectTraffic safety
dc.subjectVisual landscape element
dc.subjectRoads and streets
dc.titleThe Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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