论文标题
一种基于机器学习和历史数据的新型坡道计量方法
A Novel Ramp Metering Approach Based on Machine Learning and Historical Data
论文作者
论文摘要
高速公路上交通状况的随机性质会导致交通流量过多的交通拥堵和不规则性。坡道计量是一种在各种交通条件下维持高速公路效率的有效方法。创建一种可靠且实用的坡道计量算法，以考虑关键的交通量度和历史数据仍然是一个具有挑战性的问题。在这项研究中，我们使用机器学习方法来开发一种新型的实时预测模型，以用于坡道计量。我们通过将其与基线流量响应式坡道计量算法进行比较来评估方法的潜力。
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.
