New Results on Multi-Step Traffic Flow Prediction

Arief Koesdwiady; Fakhri Karray

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arXiv · 1803.01365

New Results on Multi-Step Traffic Flow Prediction

Arief Koesdwiady·Fakhri Karray

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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.

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BibTeX RIS

Arief Koesdwiady, Fakhri Karray. 2018-03-04. New Results on Multi-Step Traffic Flow Prediction. https://arxiv.org/abs/1803.01365

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