Engineering

Transport engineering

Feature learning

Exploit

Traffic flow (computer networking)

Computer security

Real-time computing

Urban computing

Data mining

Computer science

Computer network

Traffic generation model

Database

Data science

Artificial intelligence

Deep learning

Floating car data

Traffic congestion

Machine learning

Data modeling

IEEE Network

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale.

Urban Traffic Prediction from Mobility Data Using Deep Learning