Risk assessment of various car-following models of new generation vehicles in mixed traffic

New generation vehicles, namely autonomous vehicles (AV) and connected autonomous vehicles (CAV), are advancing rapidly with goals of full automation on roads in the near future. The modelling of car-following (CF) behaviour has progressed significantly over the last few decades with many variants o...

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Main Author: John, Priscilla Amy
Other Authors: Zhu Feng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/151648
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1516482021-06-23T06:05:34Z Risk assessment of various car-following models of new generation vehicles in mixed traffic John, Priscilla Amy Zhu Feng School of Civil and Environmental Engineering zhufeng@ntu.edu.sg Engineering::Civil engineering::Transportation New generation vehicles, namely autonomous vehicles (AV) and connected autonomous vehicles (CAV), are advancing rapidly with goals of full automation on roads in the near future. The modelling of car-following (CF) behaviour has progressed significantly over the last few decades with many variants of car-following models appearing. However, very little research focuses on the safety and risks of car-following models. This paper aims to analyse and provide a comprehensive comparison of different car-following models based on their safety impact in a traffic network. The Pan Island Expressway (PIE) and Bukit Timah Road will be utilised as the two networks on which the simulations will run on. Simulations of Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control (CACC) and SmartSK car-following models against the Intelligent Driver Model (IDM) car-following model will be conducted. In the simulations, market penetration rate (MPR) and traffic density are also taken into consideration. After each simulation process is completed, Surrogate Safety Assessment Model (SSAM) measures such as Time-to-Collision (TTC) and Post Encroachment Time (PET) will be produced to analyse the risks and occurrences of potential collisions. Results show that IDM and SmartSK car-following models fare better in terms of their safety performance in traffic networks as compared to ACC and CACC car-following models. Furthermore, the varying performance of car- following models on highway and intersection can also be seen through the results. At the intersection, vehicles tend to have greater critical potential collisions in comparison to the highway. Insights from this study will help deepen understanding on the safety performances of the respective car-following models used. Bachelor of Engineering (Civil) 2021-06-23T06:05:34Z 2021-06-23T06:05:34Z 2021 Final Year Project (FYP) John, P. A. (2021). Risk assessment of various car-following models of new generation vehicles in mixed traffic. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/151648 https://hdl.handle.net/10356/151648 en TR-11AB application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering::Transportation
spellingShingle Engineering::Civil engineering::Transportation
John, Priscilla Amy
Risk assessment of various car-following models of new generation vehicles in mixed traffic
description New generation vehicles, namely autonomous vehicles (AV) and connected autonomous vehicles (CAV), are advancing rapidly with goals of full automation on roads in the near future. The modelling of car-following (CF) behaviour has progressed significantly over the last few decades with many variants of car-following models appearing. However, very little research focuses on the safety and risks of car-following models. This paper aims to analyse and provide a comprehensive comparison of different car-following models based on their safety impact in a traffic network. The Pan Island Expressway (PIE) and Bukit Timah Road will be utilised as the two networks on which the simulations will run on. Simulations of Adaptive Cruise Control (ACC), Cooperative Adaptive Cruise Control (CACC) and SmartSK car-following models against the Intelligent Driver Model (IDM) car-following model will be conducted. In the simulations, market penetration rate (MPR) and traffic density are also taken into consideration. After each simulation process is completed, Surrogate Safety Assessment Model (SSAM) measures such as Time-to-Collision (TTC) and Post Encroachment Time (PET) will be produced to analyse the risks and occurrences of potential collisions. Results show that IDM and SmartSK car-following models fare better in terms of their safety performance in traffic networks as compared to ACC and CACC car-following models. Furthermore, the varying performance of car- following models on highway and intersection can also be seen through the results. At the intersection, vehicles tend to have greater critical potential collisions in comparison to the highway. Insights from this study will help deepen understanding on the safety performances of the respective car-following models used.
author2 Zhu Feng
author_facet Zhu Feng
John, Priscilla Amy
format Final Year Project
author John, Priscilla Amy
author_sort John, Priscilla Amy
title Risk assessment of various car-following models of new generation vehicles in mixed traffic
title_short Risk assessment of various car-following models of new generation vehicles in mixed traffic
title_full Risk assessment of various car-following models of new generation vehicles in mixed traffic
title_fullStr Risk assessment of various car-following models of new generation vehicles in mixed traffic
title_full_unstemmed Risk assessment of various car-following models of new generation vehicles in mixed traffic
title_sort risk assessment of various car-following models of new generation vehicles in mixed traffic
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/151648
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