An Empirical Experiment on Deep Learning Models for Predicting Traffic Data

TL;DR

This paper evaluates various deep learning models for traffic prediction using consistent datasets and environments, highlighting their performance variations during abrupt traffic changes and identifying Graph-WaveNet and GMAN as top performers.

Contribution

It provides a systematic comparison of state-of-the-art traffic prediction models under uniform conditions and analyzes their robustness during difficult traffic intervals.

Findings

Graph-WaveNet and GMAN outperform other models overall

Model performance varies significantly across different traffic conditions

Models struggle during abrupt traffic changes, indicating need for further robustness improvements

Abstract

To tackle ever-increasing city traffic congestion problems, researchers have proposed deep learning models to aid decision-makers in the traffic control domain. Although the proposed models have been remarkably improved in recent years, there are still questions that need to be answered before deploying models. For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments. It is also difficult to determine which models would work when traffic conditions change abruptly (e.g., rush hour). In this work, we conduct two experiments to answer the two questions. In the first experiment, we conduct an experiment with the state-of-the-art models and the identical public datasets to compare model performance under a consistent experiment environment. We then extract a set of temporal regions in the datasets, whose speeds change abruptly and use these regions to explore model performance with difficult intervals. The experiment results indicate that Graph-WaveNet and GMAN show better performance in general. We also find that prediction models tend to have varying performances with data and intervals, which calls for in-depth analysis of models on difficult intervals for real-world deployment.