Exploring sustainable pathways for urban traffic decarbonization:
vehicle technologies, management strategies, and driving behaviour
- URL: http://arxiv.org/abs/2308.14914v1
- Date: Mon, 28 Aug 2023 22:17:36 GMT
- Title: Exploring sustainable pathways for urban traffic decarbonization:
vehicle technologies, management strategies, and driving behaviour
- Authors: Saba Sabet and Bilal Farooq
- Abstract summary: This research conducts a comprehensive micro-simulation of traffic and emissions in downtown Toronto, Canada.
To achieve this, transformers-based prediction models accurately forecast Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions.
The study finds that 100% battery electric vehicles have the lowest GHG emissions, showing their potential as a sustainable transportation solution.
- Score: 5.172508424953869
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The global fight against climate change and air pollution prioritizes the
transition to sustainable transportation options. Understanding the impacts of
various sustainable pathways on emissions, travel time, and costs is crucial
for researchers and policymakers. This research conducts a comprehensive
microsimulation of traffic and emissions in downtown Toronto, Canada, to
examine decarbonization scenarios. The resulting 140 scenarios involve
different fuel types, Connected and Automated Vehicles (CAV) penetration rates,
and routing strategies combined with driving style. To achieve this,
transformers-based prediction models accurately forecast Greenhouse Gas (GHG)
and Nitrogen Oxides (NOx) emissions and average speed for eco-routing. The
study finds that 100% battery electric vehicles have the lowest GHG emissions,
showing their potential as a sustainable transportation solution. However,
challenges related to cost and availability persist. Hybrid Electric Vehicles
and e-fuels demonstrate considerable emission reductions, emerging as promising
alternatives. Integrating CAVs with anticipatory routing strategies
significantly reduces GHG emissions. Additionally, eco-driving practices and
eco-routing strategies have a notable impact on NOx emissions and travel time.
Comprehensive cost analysis provides valuable insights into the economic
implications of various strategies and technologies. These findings offer
guidance to various stakeholders in formulating effective strategies, behaviour
changes, and policies for emission reduction and sustainable transportation
development.
Related papers
- The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition [136.32656319458158]
The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies.
This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries.
The competition culminated in 15 top-performing solutions.
arXiv Detail & Related papers (2024-05-14T17:59:57Z) - A Scoping Review of Energy-Efficient Driving Behaviors and Applied
State-of-the-Art AI Methods [2.765388013062202]
There is no comprehensive investigation into energy-efficient driving behaviors and strategies.
Many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available.
This paper conducts a thorough literature review on ecological driving behaviors and styles and analyzes the driving factors influencing energy consumption.
arXiv Detail & Related papers (2024-03-04T13:57:34Z) - Deep Reinforcement Learning-based Intelligent Traffic Signal Controls
with Optimized CO2 emissions [6.851243292023835]
Transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion.
Despite several adaptive traffic signal controllers in literature, limited research has been conducted on their comparative performance.
We propose EcoLight, a reward shaping scheme for reinforcement learning algorithms that not only reduces CO2 emissions but also achieves competitive results in metrics such as travel time.
arXiv Detail & Related papers (2023-10-19T19:54:47Z) - A Review on AI Algorithms for Energy Management in E-Mobility Services [4.084938013041068]
E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns.
This paper seeks to explore the potential of artificial intelligence (AI) in addressing various challenges related to effective energy management in e-mobility systems.
arXiv Detail & Related papers (2023-09-26T16:34:35Z) - DenseLight: Efficient Control for Large-scale Traffic Signals with Dense
Feedback [109.84667902348498]
Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network.
Most prior TSC methods leverage deep reinforcement learning to search for a control policy.
We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
arXiv Detail & Related papers (2023-06-13T05:58:57Z) - Driver Assistance Eco-driving and Transmission Control with Deep
Reinforcement Learning [2.064612766965483]
In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance.
It trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience.
It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
arXiv Detail & Related papers (2022-12-15T02:52:07Z) - Federated Reinforcement Learning for Real-Time Electric Vehicle Charging
and Discharging Control [42.17503767317918]
This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments.
A horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments.
Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various factors.
arXiv Detail & Related papers (2022-10-04T08:22:46Z) - Unified Automatic Control of Vehicular Systems with Reinforcement
Learning [64.63619662693068]
This article contributes a streamlined methodology for vehicular microsimulation.
It discovers high performance control strategies with minimal manual design.
The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
arXiv Detail & Related papers (2022-07-30T16:23:45Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - Learning Eco-Driving Strategies at Signalized Intersections [1.7682859739940435]
We propose a reinforcement learning approach to learn effective eco-driving control strategies.
We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time.
Results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.
arXiv Detail & Related papers (2022-04-26T19:45:11Z) - Modelling the transition to a low-carbon energy supply [91.3755431537592]
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change.
Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely.
Runaway emissions could lead to extremes in weather conditions around the world.
arXiv Detail & Related papers (2021-09-25T12:37:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.