Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

• URL: http://arxiv.org/abs/2310.13129v2
• Date: Mon, 23 Oct 2023 22:08:13 GMT
• Title: Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions
• Authors: Pedram Agand, Alexey Iskrov, Mo Chen
• Abstract summary: 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.
• Score: 6.851243292023835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, 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. Increased levels of air pollution and extended commute times caused by traffic bottlenecks make intersection traffic signal controllers a crucial component of modern transportation infrastructure. Despite several adaptive traffic signal controllers in literature, limited research has been conducted on their comparative performance. Furthermore, despite carbon dioxide (CO2) emissions' significance as a global issue, the literature has paid limited attention to this area. In this report, 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. We compare the performance of tabular Q-Learning, DQN, SARSA, and A2C algorithms using metrics such as travel time, CO2 emissions, waiting time, and stopped time. Our evaluation considers multiple scenarios that encompass a range of road users (trucks, buses, cars) with varying pollution levels.

Related papers

• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• 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)
• Differentiable Predictive Control for Large-Scale Urban Road Networks [1.3414298287600035]
  Transportation is a major contributor to CO2 emissions. This paper presents a novel approach to traffic network control using Differentiable Predictive Control (DPC) Our approach demonstrates a 4 order of magnitude reduction in computation time and an up to 37% improvement in traffic performance.
  arXiv  Detail & Related papers  (2024-06-14T22:42:02Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• Airport take-off and landing optimization through genetic algorithms [55.2480439325792]
  This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports.
  arXiv  Detail & Related papers  (2024-02-29T14:53:55Z)
• 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)
• Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline [0.0]
  We propose and implement an integrated machine learning pipeline that utilizes traffic images to obtain vehicular counts. We verify the utility and accuracy of this pipeline on an open-source dataset of traffic images obtained for a location in Singapore. The roadside particulate emission is observed to correlate well with obtained vehicular counts with a correlation coefficient of 0.93, indicating that this method can indeed serve as a quick and effective correlate of particulate emissions.
  arXiv  Detail & Related papers  (2022-11-24T07:48:29Z)
• How Routing Strategies Impact Urban Emissions [2.436885905080739]
  Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.
  arXiv  Detail & Related papers  (2022-07-04T14:46:08Z)
• 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)
• Simulating the Effects of Eco-Friendly Transportation Selections for Air Pollution Reduction [1.9968351444772683]
  We propose a method to simulate the effectiveness of an eco-friendly transport mode selection for reducing air pollution by using map search logs. The total amount of CO2 emissions can be reduced by 9.23%, whereas the average travel time can in fact be reduced by 9.96%.
  arXiv  Detail & Related papers  (2021-09-10T12:30:32Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)

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.