Speed prediction from mobile sensors using cellular phone‐based traffic data

Abstract

The formulation of data‐driven short‐term traffic state prediction models is highly dependent on the characteristics of collected data. Mobile sensors, specifically, on‐board cellular phones (CPs) have proven success in wide scale real‐time traffic data collection, in areas with limited traffic surveillance infrastructure. In this research, four short‐term travel speed prediction models have been examined to cater the CP‐based traffic data environment. Time‐series concepts were adopted for speed prediction by autoregressive integrated moving average model and non‐linear autoregressive exogenous model that is trained by neural networks. Alternatively, Bayesian networks (BNTs) and dynamic BNTs (DBNs) speed prediction models, from the graphical‐based arena, have been investigated. The developed prediction models were tested in MATLAB environment on data from a simulation platform for 26‐of‐July corridor in Greater Cairo, Egypt. Testing results revealed the advantage of graphical‐based models in restricting the propagation of prediction errors from one time step to the next. BNT reported a mean absolute percentage error (MAPE) of 6.31 ± 1.03, whereas the DBN model reported a MAPE of 5.34 ± 1.90.