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

Autor: Hoda Talaat, Hazem M. Abbas, Yarah Basyoni, Ibrahim El Dimeery
Rok vydání: 2017
Předmět:
Zdroj: IET Intelligent Transport Systems. 11:387-396
ISSN: 1751-9578
DOI: 10.1049/iet-its.2016.0279
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: