New Results on Multi-Step Traffic Flow Prediction

TL;DR

This paper introduces two novel methods to enhance multi-step traffic flow prediction, addressing the degradation issue over longer horizons by incorporating temporal context and data augmentation, validated on real-world data.

Contribution

It proposes a recursive model with temporal context awareness and a GAN-based data augmentation technique for multi-output traffic prediction, improving accuracy.

Findings

Improved multi-step prediction accuracy in recursive models.

Enhanced multi-output prediction performance with GAN-based data augmentation.

Validated results on real-world traffic data.

Abstract

In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly more difficult than its single-step variant and is known to suffer from degradation in predictions as the time step increases. In this paper, two approaches to improve multi-step traffic flow prediction performance in recursive and multi-output settings are introduced. In particular, a model that allows recursive prediction approaches to take into account the temporal context in term of time-step index when making predictions is introduced. In addition, a conditional generative adversarial network-based data augmentation method is proposed to improve prediction performance in the multi-output setting. The experiments on a real-world traffic flow dataset show that the two methods improve on multi-step traffic flow prediction in recursive and multi-output settings, respectively.