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.

通过深度时空学习方法，在处理交通流量数据不平衡和从长期视角挖掘相关时空特征方面取得了显著改进，实验结果表明该方法在预测准确性方面具有明显优势。