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

Junbo Zhang · Yu Zheng · Junkai Sun · Dekang Qi

334 citations0/0 files storedIEEE Transactions on Knowledge and Data Engineering

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Traffic Prediction and Management Techniques Urban Design and Spatial Analysis Data Management and Algorithms

Abstract

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.

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References

1977Book Review: Time Series Analysis: Forecasting and Control 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2002Comparison of parametric and nonparametric models for traffic flow forecasting 1973Time Series Analysis Forecasting and Control.2015Traffic prediction in a bike-sharing system 2009Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models 2014Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data 2014Prediction of human emergency behavior and their mobility following large-scale disaster

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2020Urban flow prediction from spatiotemporal data using machine learning: A survey 2019Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges 2021Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks

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