Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

By Junbo Zhang, Yu Zheng, Junkai Sun, Dekang Qi. IEEE Transactions on Knowledge and Data Engineering, 2020

313 citations recorded by the source corpus.

Predicting flows (e.g., the traffic of vehicles, crowds, and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a very challenging problem, affected by multiple complex factors, such as the spatial correlation between different locations, temporal correlation among different time intervals, and external factors (like events and weather). In addition, the flow at a node (called node flow) and transitions between nodes (edge flow) mutually influence each other. To address these issues, we propose a multitask deep-learning framework that simultaneously predicts the node flow and edge flow throughout a spatio-temporal network. Based on fully convolutional networks, our approach designs two sophisticated models for predicting node flow and edge flow, respectively. These two models are connected by coupling their latent representations of middle layers, and trained together. The external factor is also integrated into the framework through a gating fusion mechanism. In the edge flow prediction model, we employ an embedding component to deal with the sparse transitions between nodes. We evaluate our method based on the taxicab data in Beijing and New York City. Experimental results show the advantages of our method beyond 11 baselines, such as ConvLSTM, CNN, and Markov Random Field.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/4f8811bb5a9a3d4563aa3e0d4f9f0783ce23e9db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Shengna Guo
2. Youfang Lin
3. Huaiyu Wan
4. Gao Cong
5. Xiucheng Li
6. Zezhi Shao
7. Fei Wang
8. Zhao Zhang

In Transportation

1. Shengna Guo
2. Youfang Lin
3. Huaiyu Wan
4. Gao Cong
5. Zezhi Shao
6. Mengzhang Li
7. Zhanxing Zhu
8. Fei Wang

Citations in the corpus

• Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021, background): “In particular, the proposals [21], [22], [23], [26], [32], [33], [34], [35] partition the region into 2D or 3D cells,”