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Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.
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Impact of Fog on Vehicular Emissions and Fuel Consumption in a Mixed Traffic Flow with Autonomous Vehicles (AVs) and Human-Driven Vehicles Using VISSIM Microsimulation Model
Driving in foggy conditions poses high risks to road users due to the reduction of visibility, affecting the drivers’ vision and perception, and making changes in driving behavior, which is one of the most important factors affecting vehicular emissions and fuel consumption. This study analyzes the PTV VISSIM traffic microsimulation outputs for exhaust emissions and fuel consumption of vehicles simulated under adverse weather conditions. This weather-dependent simulation is developed by using the advanced psychophysical car-following model “Wiedemann’s 99,” to flexibly control the driving behavior parameters in various driving conditions. Results show that vehicles under foggy conditions consume more fuel and produce more emissions in comparison with clear sky conditions and other scenarios. With the transition of current cities to smart sustainable cities and by introducing automated vehicles (AVs) to the traditional traffic network and gradually increasing their penetration rate, negative environmental impacts of driving under foggy conditions will be reduced, and improvement in overall mobility of a shared network of autonomous and human-driven is observable.
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- PAR ID:
- 10424962
- Date Published:
- Journal Name:
- 2023 International Conference on Transportation and Development 2023
- Page Range / eLocation ID:
- 253 to 262
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784484876.023
