An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment

This study develops an algorithm to detect the risk of collision between trucks (i.e., yard tractors) and pedestrians (i.e., workers) in the connected environment of the port. The algorithm consists of linear regression-based movable coordinate predictions and vertical distance and angle judgments c...

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Autores principales:	Seung-oh Son, Juneyoung Park, Cheol Oh, Chunho Yeom
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/d6ff7a4a6558474f89f571902b108e3a
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spelling	oai:doaj.org-article:d6ff7a4a6558474f89f571902b108e3a2021-11-08T02:36:01ZAn Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment2042-319510.1155/2021/9907698https://doaj.org/article/d6ff7a4a6558474f89f571902b108e3a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9907698https://doaj.org/toc/2042-3195This study develops an algorithm to detect the risk of collision between trucks (i.e., yard tractors) and pedestrians (i.e., workers) in the connected environment of the port. The algorithm consists of linear regression-based movable coordinate predictions and vertical distance and angle judgments considering the moving characteristics of objects. Time-to-collision for port workers (TTCP) is developed to reflect the characteristics of the port using the predictive coordinates. This study assumes the connected environment in which yard tractors and workers can share coordinates of each object in real time using the Internet of Things (IoT) network. By utilizing microtraffic simulations, a port network is implemented, and the algorithm is verified using data from simulated workers and yard trucks in the connected environment. The risk detection algorithm is validated using confusion matrix. Validation results show that the true-positive rate (TPR) is 61.5∼98.0%, the false-positive rate (FPR) is 79.6∼85.9%, and the accuracy is 72.2∼88.8%. This result implies that the metric scores improve as the data collection cycle increases. This is expected to be useful for sustainable transportation industry sites, particularly IoT-based safety management plans, designed to ensure the safety of pedestrians from crash risk by heavy vehicles (such as yard tractors).Seung-oh SonJuneyoung ParkCheol OhChunho YeomHindawi-WileyarticleTransportation engineeringTA1001-1280Transportation and communicationsHE1-9990ENJournal of Advanced Transportation, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
spellingShingle	Transportation engineering TA1001-1280 Transportation and communications HE1-9990 Seung-oh Son Juneyoung Park Cheol Oh Chunho Yeom An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment
description	This study develops an algorithm to detect the risk of collision between trucks (i.e., yard tractors) and pedestrians (i.e., workers) in the connected environment of the port. The algorithm consists of linear regression-based movable coordinate predictions and vertical distance and angle judgments considering the moving characteristics of objects. Time-to-collision for port workers (TTCP) is developed to reflect the characteristics of the port using the predictive coordinates. This study assumes the connected environment in which yard tractors and workers can share coordinates of each object in real time using the Internet of Things (IoT) network. By utilizing microtraffic simulations, a port network is implemented, and the algorithm is verified using data from simulated workers and yard trucks in the connected environment. The risk detection algorithm is validated using confusion matrix. Validation results show that the true-positive rate (TPR) is 61.5∼98.0%, the false-positive rate (FPR) is 79.6∼85.9%, and the accuracy is 72.2∼88.8%. This result implies that the metric scores improve as the data collection cycle increases. This is expected to be useful for sustainable transportation industry sites, particularly IoT-based safety management plans, designed to ensure the safety of pedestrians from crash risk by heavy vehicles (such as yard tractors).
format	article
author	Seung-oh Son Juneyoung Park Cheol Oh Chunho Yeom
author_facet	Seung-oh Son Juneyoung Park Cheol Oh Chunho Yeom
author_sort	Seung-oh Son
title	An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment
title_short	An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment
title_full	An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment
title_fullStr	An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment
title_full_unstemmed	An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment
title_sort	algorithm for detecting collision risk between trucks and pedestrians in the connected environment
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/d6ff7a4a6558474f89f571902b108e3a
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An Algorithm for Detecting Collision Risk between Trucks and Pedestrians in the Connected Environment

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