TTPNet: A Neural Network for Travel Time Prediction Based on Tensor Decomposition and Graph Embedding

Abstract

Travel time prediction of a given trajectory plays an indispensable role in intelligent transportation systems. Although many prior researches have struggled for accurate prediction results, most of them achieve inferior performance due to insufficient feature extraction of travel speed and road network structure from the trajectory data, which confirms the challenges involved in this topic. To overcome those issues, we propose a novel neural <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>Net</u>work</i> for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>T</u>ravel</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>T</u>ime</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>P</u>rediction</i> based on tensor decomposition and graph embedding, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TTPNet</i> , which can extract travel speed and representation of road network structure effectively from historical trajectories, as well as predict the travel time with better accuracy. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TTPNet</i> consists of three components: the first module (Travel Speed Features Layer) leverages non-negative tensor decomposition to restore travel speed distributions on different roads in the previous hour, and integrates a CNN-RNN model to extract both long-term and short-term travel speed features of the query trajectory; the second module (Road Network Structure Features Layer) utilizes graph embedding to generate the representation of local and global road network structure; the last module (Deep LSTM Prediction Layer) completes the final predicting task. Empirical results over two real-world large-scale datasets show that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TTPNet</i> model can achieve significantly better performance and remarkable robustness.