Loading...
Layered ensemble model for short-term traffic flow forecasting with outlier detection
Abdullatif, Amr R.A. Rovetta, S. Masulli, F.
Abdullatif, Amr R.A.
Rovetta, S.
Masulli, F.
Publication Date
2016-11
End of Embargo
Supervisor
Rights
© 2016 IEEE. Reproduced in accordance with the publisher's self-archiving policy.
Peer-Reviewed
Yes
Open Access status
Accepted for publication
2016
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
Real time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taking into consideration both spatial (road links) and temporal (lag or past traffic flow values) information. We propose a Layered Ensemble Model (LEM) which combines Artificial Neural Networks and Graded Possibilistic Clustering obtaining an accurate forecast of the traffic flow rates with outlier detection. Experimentation has been carried out on two different data sets. The former was obtained from real UK motorway and the later was obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed LEM model for short-term traffic forecasting provides promising results and given the ability for outlier detection, accuracy, robustness of the proposed approach, it can be fruitful integrated in traffic flow management systems.
Version
Accepted manuscript
Citation
Abdullatif A, Rovetta S and Masulli F (2016) Layered ensemble model for short-term traffic flow forecasting with outlier detection. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). 7-9 Sep 2016, Bologna, Italy.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Conference paper