论文标题
数据驱动的交通状态的功能工程在城市道路网络中的预测
Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks
论文作者
论文摘要
大多数交通状态预测算法应用于城市道路网络时，仅考虑与目标位置紧邻的链接。但是，对于长期预测，也有望为数据驱动算法提供更遥远的链接或网络区域的交通状态。本文研究了使用网络聚类算法和一年的一年浮动汽车（FCD）的期望。首先，将聚类算法应用于数据，以便在慕尼黑城网络中提取容易拥堵的地区。在统计工具的帮助下，分析了这些集群内部的拥塞水平。确定了清晰的时空拥塞模式和聚类区域之间的相关性。这些相关性集成到K-最近的邻居（KNN）旅行时间预测算法中。在与其他方法的比较中，此方法可实现最佳结果。 KNN预测器的统计结果和性能表明，对网络范围的流量的考虑是预测变量的宝贵功能，也是一种有前途的方式，可以在将来开发更准确的算法。
Most traffic state forecast algorithms when applied to urban road networks consider only the links in close proximity to the target location. However, for longer-term forecasts also the traffic state of more distant links or regions of the network are expected to provide valuable information for a data-driven algorithm. This paper studies these expectations of using a network clustering algorithm and one year of Floating Car (FCD) collected by a large fleet of vehicles. First, a clustering algorithm is applied to the data in order to extract congestion-prone regions in the Munich city network. The level of congestion inside these clusters is analyzed with the help of statistical tools. Clear spatio-temporal congestion patterns and correlations between the clustered regions are identified. These correlations are integrated into a K- Nearest Neighbors (KNN) travel time prediction algorithm. In a comparison with other approaches, this method achieves the best results. The statistical results and the performance of the KNN predictor indicate that the consideration of the network-wide traffic is a valuable feature for predictors and a promising way to develop more accurate algorithms in the future.
