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

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.