Integrating semantic zoning information with the prediction of road link speed based on taxi GPS data
Integrating semantic zoning information with the prediction of road link speed based on taxi GPS data
复制标题
将语义分区信息与基于出租车 GPS 数据的道路连接速度预测相结合
DOI：
10.1155/2020/6939328
复制
发表时间：
2020
期刊：
影响因子：
2.3
通讯作者：
Ma Zhanwu
中科院分区：
文献类型：
--
作者：
He Bing;Xu Zhifeng;Xu Yangjie;Hu Jinxing;Ma Zhanwu
道路连接速度是交通状态的重要指标之一。为了将道路连接点的时空动态和相关性特征融入到速度预测中，提出了一种基于LDA和GCN的速度预测方法。首先，利用地图匹配的出租车GPS定位数据构建轨迹数据集。然后，利用LDA算法提取城市区域的语义函数向量，并基于出租车轨迹量化道路连接的空间动态特征。最后，我们将语义函数向量添加到数据集中，并训练一个图卷积网络来学习道路连接的时空依赖关系。该学习模型用于预测未来道路连接的速度。将该方法与深圳出租车GPS生成的同一数据集上的6个基线模型进行了比较，结果表明，当加入语义分区信息时，该方法具有更好的预测性能。复合和单值语义分区信息分别可以使图卷积网络的性能提高6.46%和8.35%，而基线机器学习模型仅对实验数据集上的单值语义分区信息有效。
Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.