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.
- Pub Date:
- May 2020
- Electrical Engineering and Systems Science - Systems and Control;
- Computer Science - Machine Learning;
- Electrical Engineering and Systems Science - Signal Processing;
- Statistics - Machine Learning
- 5 pages, 11 figures, 2 tables