Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections

Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (I...

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Main Authors: Mohammad Khashayarfard, Habibollah Nassiri
Format: Article
Language:English
Published: Hindawi-Wiley 2021-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2021/6677010
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spelling doaj-d82eb7019d0f40b8be90921d00371ae42021-07-05T00:01:35ZengHindawi-WileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/6677010Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized IntersectionsMohammad Khashayarfard0Habibollah Nassiri1Civil Engineering DepartmentCivil Engineering DepartmentHuman error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.http://dx.doi.org/10.1155/2021/6677010
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Khashayarfard
Habibollah Nassiri
spellingShingle Mohammad Khashayarfard
Habibollah Nassiri
Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
Journal of Advanced Transportation
author_facet Mohammad Khashayarfard
Habibollah Nassiri
author_sort Mohammad Khashayarfard
title Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
title_short Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
title_full Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
title_fullStr Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
title_full_unstemmed Studying the Simultaneous Effect of Autonomous Vehicles and Distracted Driving on Safety at Unsignalized Intersections
title_sort studying the simultaneous effect of autonomous vehicles and distracted driving on safety at unsignalized intersections
publisher Hindawi-Wiley
series Journal of Advanced Transportation
issn 2042-3195
publishDate 2021-01-01
description Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.
url http://dx.doi.org/10.1155/2021/6677010
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