The environmental impact of connected and automated vehicles’ car-following behavior
This thesis focuses on the environmental impact of car-following (CF) driving behavior in mixed autonomy traffic. The thesis introduces a new calibration framework applicable to various CF models. The framework uses the concept of ‘adaptability’ which improves the simulation of AVs’ driving behavior by enabling real-time changes of the CF model parameters based on prevailing traffic conditions. The thesis also provides an environmental assessment of mixed autonomy traffic considering different vehicle arrangements in a platoon using real data and simulation outcomes. The thesis provides several novel findings regarding the impact of the driving behavior of AVs on-road emissions. For instance, the analysis of the trade-off between traffic stability and mobility in a mixed autonomy environment reveals that an automated eco-driving strategy that minimizes the reciprocal of the platoon average velocity produces fewer emissions compared to strategies that aim to minimize traffic instability measured by the standard deviation of velocity or average acceleration. This is because the latter can considerably decrease the platoon average velocity to increase stability. In addition, vehicle arrangements, specifically whether the AV is leading or following, affect the AV’s driving behavior and thus, the emissions, especially in congested conditions. Furthermore, the correlation between mobility and emissions shows that traffic emissions are highly influenced by acceleration at a high-velocity level and by time headway at a low-velocity level. Overall, the thesis explores the environmental implications of AV driving behavior considering different aspects such as traffic network, market penetration rate, and vehicle arrangements. Therefore, the findings of this thesis can be insightful at this early stage of AV deployment to ensure that the future mixed autonomy environment does not only improve traffic and safety but also contribute to a more environmentally friendly transport system.
- Record URL:
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Supplemental Notes:
- PhD thesis
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Authors:
- Alhariqi, A
- Publication Date: 2023
Media Info
- Pagination: 1 file
Subject/Index Terms
- TRT Terms: Air quality management; Automated vehicle control; Autonomous vehicles; Car following; Connected vehicles; Ecodriving; Environment; Environmental impacts; Headways; Mathematical models; Mechanical acceleration; Vehicle to vehicle communications
- Uncontrolled Terms: Acceleration
- ATRI Terms: Acceleration; Autonomous vehicle; Car following; Ecodriving; Emissions control; Environmental effects; Headway; Modelling; Vehicle to vehicle communications
- Subject Areas: Environment; I15: Environment;
Filing Info
- Accession Number: 01907050
- Record Type: Publication
- Source Agency: ARRB Group Limited
- Files: ITRD, ATRI
- Created Date: Feb 6 2024 9:05AM