Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction

• URL: http://arxiv.org/abs/2308.06155v1
• Date: Fri, 11 Aug 2023 14:33:20 GMT
• Title: Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction
• Authors: Weilong Ding, Tianpu Zhang, Zhe Wang
• Abstract summary: Deep-temporal learning method is proposed to predict daily traffic volume in three phases. In decision phase, traffic volumes on a coming day network-wide toll stations would be achieved effectively.
• Score: 3.8277254030074537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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