A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data

• URL: http://arxiv.org/abs/2012.12104v1
• Date: Wed, 9 Dec 2020 05:08:41 GMT
• Title: A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data
• Authors: Bing Liu (1), Yu Tang (2), Yuxiong Ji (1), Yu Shen (1), and Yuchuan Du (1) ((1) Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China, (2) Tandon School of Engineering, New York University, New York, USA)
• Abstract summary: Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measures collected by point detectors. We propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measures collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras-which have been increasingly deployed on road networks-could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. The proposed method uses traffic video frames as inputs and learns the optimal control strategies directly from the high-dimensional visual inputs. A real-world case study demonstrates that, in comparison with a state-of-the-practice method, the proposed DRL method results in 1) lower travel times in the mainline, 2) shorter vehicle queues at the on-ramp, and 3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.

Related papers

• Strada-LLM: Graph LLM for traffic prediction [62.2015839597764]
  A considerable challenge in traffic prediction lies in handling the diverse data distributions caused by vastly different traffic conditions. We propose a graph-aware LLM for traffic prediction that considers proximal traffic information. We adopt a lightweight approach for efficient domain adaptation when facing new data distributions in few-shot fashion.
  arXiv  Detail & Related papers  (2024-10-28T09:19:29Z)
• MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning [13.369840354712021]
  MoveLight is a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging detailed real-time data and advanced machine learning techniques, MoveLight overcomes the limitations of traditional traffic signal control methods.
  arXiv  Detail & Related papers  (2024-07-24T14:17:16Z)
• Enhancing Vehicle Re-identification and Matching for Weaving Analysis [12.549381266302959]
  Vehicle weaving on highways contributes to traffic congestion, raises safety issues, and underscores the need for sophisticated traffic management systems. Current tools are inadequate in offering precise and comprehensive data on lane-specific weaving patterns. This paper introduces an innovative method for collecting non-overlapping video data in weaving zones, enabling the generation of quantitative insights into lane-specific weaving behaviors.
  arXiv  Detail & Related papers  (2024-07-05T17:50:35Z)
• 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)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Reinforcement Learning Approaches for Traffic Signal Control under Missing Data [5.896742981602458]
  In real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors. We propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents.
  arXiv  Detail & Related papers  (2023-04-21T03:26:33Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• RONELDv2: A faster, improved lane tracking method [1.3965477771846408]
  Lane detection is an integral part of control systems in autonomous vehicles and lane departure warning systems. This paper proposes an improved, lighter weight lane detection method, RONELDv2. Experiments using the proposed improvements show a consistent increase in lane detection accuracy results across different datasets and deep learning models.
  arXiv  Detail & Related papers  (2022-02-26T13:12:09Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• A Deep Reinforcement Learning Approach for Fair Traffic Signal Control [1.8275108630751837]
  We introduce two notions of fairness: delay-based and throughput-based fairness, which correspond to the two issues mentioned above. We propose two DRL-based traffic signal control methods for implementing these fairness notions, that can achieve a high throughput as well.
  arXiv  Detail & Related papers  (2021-07-21T15:23:52Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)

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.