DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction

TL;DR

DeepTrend is a hierarchical deep learning model that effectively captures and utilizes the temporal trend in traffic flow data, significantly improving prediction accuracy over traditional and existing neural network methods.

Contribution

We introduce DeepTrend, a novel deep hierarchical neural network that explicitly models and extracts time-variant trends for improved traffic flow prediction.

Findings

DeepTrend outperforms traditional models in traffic prediction accuracy.

Layer-wise pre-training enhances the model's effectiveness.

DeepTrend significantly improves prediction performance compared to LSTM with detrending methods.

Abstract

In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend, a deep hierarchical neural network used for traffic flow prediction which considers and extracts the time-variant trend. DeepTrend has two stacked layers: extraction layer and prediction layer. Extraction layer, a fully connected layer, is used to extract the time-variant trend in traffic flow by feeding the original flow series concatenated with corresponding simple average trend series. Prediction layer, an LSTM layer, is used to make flow prediction by feeding the obtained trend from the output of extraction layer and calculated residual series. To make the model more effective, DeepTrend needs first pre-trained layer-by-layer and then fine-tuned in the entire network. Experiments show that DeepTrend can noticeably boost the prediction performance compared with some traditional prediction models and LSTM with detrending based methods.