Travel Time Estimation by Learning Driving Habits and Traffic Conditions

Abstract

Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly.