DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

TL;DR

DeepTravel is a neural network model that improves travel time estimation by leveraging auxiliary supervision and temporal data, outperforming existing methods in real-world experiments.

Contribution

The paper introduces DeepTravel, a novel neural network model that effectively utilizes temporal labels for improved travel time estimation.

Findings

DeepTravel outperforms existing approaches in real datasets.

The model effectively captures complex cross-segment factors.

Auxiliary supervision enhances feature extraction and prediction accuracy.

Abstract

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to utilize the existing abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches.