Abstract

Mobility and transportation services in modern large-scale cities face traffic congestion as one of the main sources of discomfort and economic losses. In this context, taking preventive measures based on traffic predictions looks like an appealing alternative to mitigate congestion. The increasing availability of detectors in the transportation infrastructure has allowed tackling the traffic prediction problem by using a purely data-driven approach, where deep learning models have excelled. Unfortunately, the implementation of these techniques in real time is still under development. This work presents the implementation of a real-time traffic prediction application in the Las Vegas, NV, USA, urban area, built as a cyberphysical system with real-time data streaming from field sensors to a cloud-like environment where deep learning-based traffic predictors are hosted. Implementation results show the feasibility of doing traffic prediction in real time with the current technology and the usefulness of periodic retraining to maintain prediction accuracy.