Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction

TL;DR

This paper introduces a three-phase deep learning approach combining data normalization, a hybrid FCN-LSTM model, and a calibration step to improve daily highway traffic volume prediction using heterogeneous spatio-temporal data.

Contribution

It presents a novel phased deep learning framework that effectively captures long-term spatio-temporal features and addresses data imbalance for highway traffic prediction.

Findings

Significant improvement in predictive accuracy over traditional models.

Achieved 5.269 in MPAE and 0.997 in R-squared metrics.

Effective calibration for vital highway stations.

Abstract

Inter-city highway transportation is significant for citizens' modern urban life and generates heterogeneous sensory data with spatio-temporal characteristics. As a routine analysis in transportation domain, daily traffic volume estimation faces challenges for highway toll stations including lacking of exploration of correlative spatio-temporal features from a long-term perspective and effective means to deal with data imbalance which always deteriorates the predictive performance. In this paper, a deep spatio-temporal learning method is proposed to predict daily traffic volume in three phases. In feature pre-processing phase, data is normalized elaborately according to latent long-tail distribution. In spatio-temporal learning phase, a hybrid model is employed combining fully convolution network (FCN) and long short-term memory (LSTM), which considers time, space, meteorology, and calendar from heterogeneous data. In decision phase, traffic volumes on a coming day at network-wide toll stations would be achieved effectively, which is especially calibrated for vital few highway stations. Using real-world data from one Chinese provincial highway, extensive experiments show our method has distinct improvement for predictive accuracy than various traditional models, reaching 5.269 and 0.997 in MPAE and R-squre metrics, respectively.