Multitask Learning with Graph Neural Network for Travel Time Estimation.

Abstract

Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. Even for a given trajectory, the travel time is affected by many spatial-temporal factors, including static ones such as distance, road type, and so on and dynamic ones such as speed, traffic condition, and so on. Challenges of accurate estimation lie in proper representation of these spatial-temporal factors and more importantly capturing the complex relationship among them for TTE. To tackle such challenges, we present a framework based on the fact that the travel time of each road segment is affected by its adjacent segments. It features a graph convolutional neural network and a recurrent neural network for basic TTE for each road segment and a graph attention network for the relation to estimations on the adjacent road segments. Finally, a multitask learning model is proposed for the travel time of the entire given path and that for each road segment. Experimental results on real taxi trajectory datasets of two cities show that the percentage estimation error of the new approach is well controlled at 13.91% and the proposed method outperforms three state-of-the-art methods significantly.