An Empirical Experiment on Deep Learning Models for Predicting Traffic Data

• URL: http://arxiv.org/abs/2105.05504v1
• Date: Wed, 12 May 2021 08:28:12 GMT
• Title: An Empirical Experiment on Deep Learning Models for Predicting Traffic Data
• Authors: Hyunwook Lee, Cheonbok Park, Seungmin Jin, Hyeshin Chu, Jaegul Choo, Sungahn Ko
• Abstract summary: Deep learning models have been proposed to aid decision-makers in the traffic control domain. It is difficult to figure out which models provide state-of-the-art performance. It is also difficult to determine which models would work when traffic conditions change abruptly.
• Score: 18.103216508546645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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