# Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale arXiv: https://arxiv.org/abs/2408.05609 ยท HTML: https://mlsanity.com/paper/2408.05609/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. - Builds on 22 of its 137 references that are in the map. - First posted on arXiv 10 Aug 2024. ## Placed in - End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/) ## Builds on (most cited first, 22 of 22) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017) - [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021) - [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021) - [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019) - [TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios](https://mlsanity.com/paper/2210.06609/) (2022) - [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019) - [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017) - [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022) - [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023) - [A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning](https://mlsanity.com/paper/2007.05156/) (2020) - [Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles](https://mlsanity.com/paper/1812.06120/) (2018) - [Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections](https://mlsanity.com/paper/2201.07833/) (2022) - [Unified Automatic Control of Vehicular Systems with Reinforcement Learning](https://mlsanity.com/paper/2208.00268/) (2022) - [The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning](https://mlsanity.com/paper/2210.08607/) (2022) - [Learning Eco-Driving Strategies at Signalized Intersections](https://mlsanity.com/paper/2204.12561/) (2022) - [Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles](https://mlsanity.com/paper/2105.11640/) (2021) - [Generalizing Cooperative Eco-driving via Multi-residual Task Learning](https://mlsanity.com/paper/2403.04232/) (2024) - [Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach](https://mlsanity.com/paper/2001.01117/) (2020) - [Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline](https://mlsanity.com/paper/2101.09640/) (2021) - [Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors](https://mlsanity.com/paper/2306.01387/) (2023) - [What is a typical signalized intersection in a city? A pipeline for intersection data imputation from OpenStreetMap](https://mlsanity.com/paper/2405.13480/) (2024) ## Built on by (most cited first, 2 of 2) - [Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems](https://mlsanity.com/paper/2507.09836/) (2025) - [Noise-Aware Generative Microscopic Traffic Simulation](https://mlsanity.com/paper/2508.07453/) (2025) ## Abstract (arXiv) The sheer scale and diversity of transportation make it a formidable sector to decarbonize. Here, we consider an emerging opportunity to reduce carbon emissions: the growing adoption of semi-autonomous vehicles, which can be programmed to mitigate stop-and-go traffic through intelligent speed commands and, thus, reduce emissions. But would such dynamic eco-driving move the needle on climate change? A comprehensive impact analysis has been out of reach due to the vast array of traffic scenarios and the complexity of vehicle emissions. We address this challenge with large-scale scenario modeling efforts and by using multi-task deep reinforcement learning with a carefully designed network decomposition strategy. We perform an in-depth prospective impact assessment of dynamic eco-driving at 6,011 signalized intersections across three major US metropolitan cities, simulating a million traffic scenarios. Overall, we find that vehicle trajectories optimized for emissions can cut city-wide intersection carbon emissions by 11-22%, without harming throughput or safety, and with reasonable assumptions, equivalent to the national emissions of Israel and Nigeria, respectively. We find that 10% eco-driving adoption yields 25%-50% of the total reduction, and nearly 70% of the benefits come from 20% of intersections, suggesting near-term implementation pathways. However, the composition of this high-impact subset of intersections varies considerably across different adoption levels, with minimal overlap, calling for careful strategic planning for eco-driving deployments. Moreover, the impact of eco-driving, when considered jointly with projections of vehicle electrification and hybrid vehicle adoption remains significant. More broadly, this work paves the way for large-scale analysis of traffic externalities, such as time, safety, and air quality, and the potential impact of solution strategies.