Deep learning

Computer network

Mathematics

Structural engineering

Flow network

Data mining

Mathematical optimization

Machine learning

Engineering

Enhanced Data Rates for GSM Evolution

Artificial intelligence

Traffic flow (computer networking)

Geometry

Flow (mathematics)

Computer science

Node (physics)

Convolutional neural network

IEEE Transactions on Knowledge and Data Engineering

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.

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