GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs
Qiang Wang, Chen Xu, Wenqi Zhang, Jingjing Li
Abstract
This letter proposes a new travel time estimation model based on graph neural network (GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer Spatiotemporal Graph frame (MSG), which consists of static network and dynamic networks, to fully consider the influence of traffic temporal characteristics and road network topological characteristics on travel time. Moreover, we design an Attention Graph Nodes Impact Index algorithm (AGNII) to score the impact of each node on travel time. In particular, the dynamic networks utilize the graph convolution network and gate recurrent unit to obtain the traffic characteristics, the static network utilizes graph convolution network to obtain the road basic attributes. We combine the real paths sequence with the impact score of nodes to extract the subgraph with a great impact on the trajectory. After the graph representation learning and deep residual network, the estimated time is obtained. A simulator was designed to train and test our model in Chengdu and Xi'an datasets, the results show that the mean absolute percent error (MAPE) is 12.58% and 14.01%, which is 1.54% and 1.78% lower than the baselines.