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。

基于时空深度学习的高速公路交通量预测

Phased Deep Spatio-temporal Learning for Highway Traffic Volume  Prediction

Inter-city highway transportation is significant for urban life. As one of
the key functions in intelligent transportation system (ITS), traffic
evaluation always plays significant role nowadays, and daily traffic flow
prediction still faces challenges at network-wide toll stations. On the one
hand, the data imbalance in practice among various locations deteriorates the
performance of prediction. On the other hand, complex correlative
spatio-temporal factors cannot be comprehensively employed in long-term
duration. In this paper, a prediction method is proposed for daily traffic flow
in highway domain through spatio-temporal deep learning. In our method, data
normalization strategy is used to deal with data imbalance, due to long-tail
distribution of traffic flow at network-wide toll stations. And then, based on
graph convolutional network, we construct networks in distinct semantics to
capture spatio-temporal features. Beside that, meteorology and calendar
features are used by our model in the full connection stage to extra external
characteristics of traffic flow. By extensive experiments and case studies in
one Chinese provincial highway, our method shows clear improvement in
predictive accuracy than baselines and practical benefits in business.

通过时空深度学习方法，提出了一个用于高速公路上每日交通流量预测的新方法，该方法通过数据归一化策略解决了网络全域收费站交通流量的数据不平衡问题，并基于图卷积网络构建了不同语义的网络来捕捉时空特征，同时利用气象和日历特征在全连接阶段提取交通流量的外部特征，经过大量实验证明该方法较基准模型在预测准确性方面有明显提升，并在商业领域带来实际效益。