A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

• URL: http://arxiv.org/abs/2005.13992v1
• Date: Tue, 26 May 2020 21:05:01 GMT
• Title: A Novel Ramp Metering Approach Based on Machine Learning and Historical Data
• Authors: Anahita Sanandaji, Saeed Ghanbartehrani, Zahra Mokhtari, Kimia Tajik
• Abstract summary: Ramp metering is a proven method to maintain freeway efficiency under various traffic conditions. We use machine learning approaches to develop a novel real-time prediction model for ramp metering.
• Score: 0.7349727826230861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The random nature of traffic conditions on freeways can cause excessive congestions and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use machine learning approaches to develop a novel real-time prediction model for ramp metering. We evaluate the potentials of our approach in providing promising results by comparing it with a baseline traffic-responsive ramp metering algorithm.

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)
• A Predictive and Optimization Approach for Enhanced Urban Mobility Using Spatiotemporal Data [0.0]
  This study introduces a novel method for enhancing urban mobility by combining machine learning algorithms with live traffic information. We developed predictive models for journey time and congestion analysis using data from New York City's yellow taxi trips. This research contributes to ongoing efforts aimed at reducing urban congestion and improving transportation efficiency through advanced data-driven methods.
  arXiv  Detail & Related papers  (2024-10-07T16:16:49Z)
• 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)
• Uncertainty Quantification for Image-based Traffic Prediction across Cities [63.136794104678025]
  Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning. We investigate their application to a large-scale image-based traffic dataset spanning multiple cities. We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow.
  arXiv  Detail & Related papers  (2023-08-11T13:35:52Z)
• A Transfer Learning Framework for Proactive Ramp Metering Performance Assessment [2.269435462100948]
  This study presents a framework for predicting freeway traffic parameters for the after situations when a new ramp metering control strategy is implemented. By learning the association between the spatial-temporal features of traffic states in before and after situations for known freeway segments, the proposed framework can transfer this learning to predict the traffic parameters for new freeway segments.
  arXiv  Detail & Related papers  (2023-08-07T12:44:10Z)
• Leveraging Neo4j and deep learning for traffic congestion simulation & optimization [0.0]
  We show how traffic propagates backward in case of congestion or accident scenarios and its overall impact on other segments of the roads. We also train a sequential RNN-LSTM (Long Short-Term Memory) deep learning model on the real-time traffic data to assess the accuracy of simulation results based on a road-specific congestion.
  arXiv  Detail & Related papers  (2023-04-01T01:23:10Z)
• Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies [2.3204178451683264]
  ITS requires a reliable traffic prediction that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. A deep learning based approach has been developed using both the spatial and temporal dependencies. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error.
  arXiv  Detail & Related papers  (2021-11-03T10:17:48Z)
• 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)
• 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)
• Learning Traffic Speed Dynamics from Visualizations [3.0969191504482243]
  We present a deep learning method to learn the macroscopic traffic speed dynamics from space-time visualizations. Compared to existing estimation approaches, our approach allows a finer estimation resolution. We present the high-resolution traffic speed fields estimated for several freeway sections using the data obtained from the Next Generation Simulation Program (NGSIM) and German Highway (HighD) datasets.
  arXiv  Detail & Related papers  (2021-05-04T11:17:43Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)

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.