Phased Deep Spatio-temporal Learning for Highway Traffic Volume
  Prediction
Weilong Ding Tianpu Zhang Zhe Wang
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.