A Novel Ramp Metering Approach Based on Machine Learning and Historical Data
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- May 2020
- DOI:
- 10.48550/arXiv.2005.13992
- arXiv:
- arXiv:2005.13992
- Bibcode:
- 2020arXiv200513992S
- Keywords:
-
- Electrical Engineering and Systems Science - Systems and Control;
- Computer Science - Machine Learning;
- Electrical Engineering and Systems Science - Signal Processing;
- Statistics - Machine Learning
- E-Print:
- 5 pages, 11 figures, 2 tables