RT Conference Proceedings
T1 Joint feature selection and parameter tuning for short-term traffic flow forecasting based on heuristically optimized multi-layer neural networks
A1 Laña, Ibai
A1 Del Ser, Javier
A1 Vélez, Manuel
A1 Oregi, Izaskun
A2 Del Ser, Javier
AB Short-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory performance scores, a process that usually relies on manual trial-and-error adjustment. This paper explores the use of Harmony Search optimization for tuning the parameters of neural network jointly with the selection of the input features from the dataset at hand. Results are discussed and compared to other tuning methods, from which it is concluded that neural predictors optimized via the proposed heuristic wrapper outperform those tuned by means of na¨ıve parametrized algorithms, thus allowing for longer-term predictions. These promising results unfold potential applications of this technique in multi-location neighbor-aware traffic prediction.
PB Springer Verlag
SN 9789811037276
SN 2194-5357
YR 2017
FD 2017
LK https://hdl.handle.net/11556/2149
UL https://hdl.handle.net/11556/2149
LA eng
NO Laña , I , Del Ser , J , Vélez , M & Oregi , I 2017 , Joint feature selection and parameter tuning for short-term traffic flow forecasting based on heuristically optimized multi-layer neural networks . in J Del Ser (ed.) , Harmony Search Algorithm - Proceedings of the 3rd International Conference on Harmony Search Algorithm (ICHSA 2017) . Advances in Intelligent Systems and Computing , vol. 514 , Springer Verlag , pp. 91-100 , Proceedings of the 3rd International Conference on Harmony Search Algorithm, ICHSA 2017 , Bilbao , Spain , 22/02/17 . https://doi.org/10.1007/978-981-10-3728-3_10
NO conference
NO Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2017.
NO This work has been supported by the Basque Government through the ELKARTEK program (ref. KK-2015/0000080 and the BID3ABI project).
DS TECNALIA Publications
RD 28 jul 2024