Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto

• URL: http://arxiv.org/abs/2308.14914v3
• Date: Mon, 27 Jan 2025 02:19:00 GMT
• Title: Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto
• Authors: Saba Sabet, Bilal Farooq,
• Abstract summary: This study explores decarbonization strategies for urban traffic in downtown Toronto through microsimulation. Using transformer-based prediction models, we forecast Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions. The key findings show that 100% Battery Electric Vehicles (BEVs) reduce GHG emissions by 75%, but face challenges related to cost and infrastructure.
• Score: 4.378407481656902
• License:
• Abstract: As global efforts to combat climate change intensify, transitioning to sustainable transportation is crucial. This study explores decarbonization strategies for urban traffic in downtown Toronto through microsimulation, evaluating the environmental and economic impacts of vehicle technologies, traffic management strategies (eco-routing), and driving behaviours (eco-driving). The study analyzes 140 decarbonization scenarios involving different fuel types, Connected and Automated Vehicle (CAV) penetration rates, and anticipatory routing strategies. Using transformer-based prediction models, we forecast Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions, along with average speed and travel time. The key findings show that 100% Battery Electric Vehicles (BEVs) reduce GHG emissions by 75%, but face challenges related to cost and infrastructure. Hybrid Electric Vehicles (HEVs) achieve GHG reductions of 35-40%, while e-fuels result in modest reductions of 5%. 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. By incorporating a comprehensive cost analysis, the study offers valuable insights into the economic feasibility of these strategies. The findings provide practical guidance for policymakers and stakeholders in developing effective decarbonization policies and supporting sustainable transportation systems.

Related papers

• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  Much of this debate has concentrated on direct impact without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale [3.5052652317043846]
  We consider an emerging opportunity to reduce carbon emissions: the growing adoption of semi-autonomous vehicles. A comprehensive impact analysis has been out of reach due to the vast array of traffic scenarios and the complexity of vehicle emissions. We perform an in-depth prospective impact assessment of dynamic eco-driving at 6,011 signalized intersections across three major US metropolitan cities.
  arXiv  Detail & Related papers  (2024-08-10T18:23:59Z)
• EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption [9.42048156323799]
  This study introduces EcoFollower, a novel eco-car-following model developed using reinforcement learning (RL) to optimize fuel consumption in car-following scenarios. The model achieved a significant reduction in fuel consumption, lowering it by 10.42% compared to actual driving scenarios.
  arXiv  Detail & Related papers  (2024-07-22T16:48:37Z)
• Carbon Market Simulation with Adaptive Mechanism Design [55.25103894620696]
  A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility. We propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL) Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions.
  arXiv  Detail & Related papers  (2024-06-12T05:08:51Z)
• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• 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)
• 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)
• 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)
• Optimizing carbon tax for decentralized electricity markets using an agent-based model [69.3939291118954]
  Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. Carbon taxes have been shown to be an efficient way to aid in this transition. We use the NSGA-II genetic algorithm to minimize average electricity price and relative carbon intensity of the electricity mix.
  arXiv  Detail & Related papers  (2020-05-28T06:54:43Z)

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.