Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/38068
Title: Evaluating the Impacts of Autonomous Vehicles' Market Penetration on a Complex Urban Freeway during Autonomous Vehicles' Transition Period
Authors: Abdeen, Mohammad A. R.
YASAR, Ansar 
Benaida, Mohamed
Sheltami, Tarek
ZAVANTIS, Dimitrios 
EL HANSALI, Youssef 
Issue Date: 2022
Publisher: MDPI
Source: Sustainability (Basel), 14 (16) (Art N° 10094)
Abstract: Autonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network's safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies.
Notes: Abdeen, MAR (corresponding author), Islamic Univ Madinah, Dept Comp & Informat Syst, Medina 42351, Saudi Arabia.
mohammad.abdeen@iu.edu.sa
Keywords: traffic microsimulation;road safety;Vissim;driving behaviors
Document URI: http://hdl.handle.net/1942/38068
e-ISSN: 2071-1050
DOI: 10.3390/su141610094
ISI #: 000846647100001
Rights: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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