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.
Speed prediction from mobile sensors using cellular phone-based traffic data
IET Intelligent Transport Systems ; 11 , 7 ; 387-396
2017-05-30
10 pages
Article (Journal)
Electronic Resource
English
time-series concepts , CP-based traffic data environment , telecommunication traffic , belief networks , Bayesian networks , neural networks , mobile sensors , cellular phone-based traffic data , nonlinear autoregressive exogenous model , telecommunication computing , autoregressive moving average processes , mean absolute percentage error , neural nets , MATLAB environment , cellular radio , short-term travel speed prediction models , time series , BNT , graphical-based models , autoregressive integrated moving average model , speed prediction models , MAPE
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