# Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction

arXiv: https://arxiv.org/abs/2308.06155 · HTML: https://mlsanity.com/paper/2308.06155/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [Deep Air Quality Forecasting Using Hybrid Deep Learning Framework](https://mlsanity.com/paper/1812.04783/) (2018)
- [Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting](https://mlsanity.com/paper/2004.10958/) (2020)
- [Motorway Traffic Flow Prediction using Advanced Deep Learning](https://mlsanity.com/paper/1907.06356/) (2019)

## Abstract (arXiv)

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.