2020
Forecasting Sparse Traffic Congestion Patterns Using Message-Passing RNNS
ICASSP 2020accepted
The ability to forecast traffic congestion ahead of time given road conditions has remained a prominent problem in road traffic analysis. In this work, we leverage mobility traces of public transport vehicles tracked by the New York City MTA and formulate Message-Passing Recurrent Neural Nets (MPRNN…