RT Conference Proceedings
T1 New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
A1 Manibardo, Eric L.
A1 Lana, Ibai
A1 Lobo, Jesus L.
A1 Del Ser, Javier
AB This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive models aimed to learn this pattern may become eventually obsolete, hence failing to sustain performance levels of practical use. To overcome this model degradation, online learning methods incrementally learn from new data samples arriving over time, and accommodate eventual changes along the data stream by implementing assorted concept drift strategies. In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data. We draw interesting insights on the performance degradation when the forecasting horizon is increased. As opposed to what is done in most literature, we provide evidence of the importance of assessing the distribution of classes over time before designing and tuning the learning model. This previous exercise may give a hint of the predictability of the different congestion levels under target. Experimental results are discussed over real traffic speed data captured by inductive loops deployed over Seattle (USA). Several online learning methods are analyzed, from traditional incremental learning algorithms to more elaborated deep learning models. As shown by the reported results, when increasing the prediction horizon, the performance of all models degrade severely due to the distribution of classes along time, which supports our claim about the importance of analyzing this distribution prior to the design of the model.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781728169262
YR 2020
FD 2020-07
LK https://hdl.handle.net/11556/2726
UL https://hdl.handle.net/11556/2726
LA eng
NO Manibardo , E L , Lana , I , Lobo , J L & Del Ser , J 2020 , New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data . in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings . , 9207661 , Proceedings of the International Joint Conference on Neural Networks , Institute of Electrical and Electronics Engineers Inc. , 2020 International Joint Conference on Neural Networks, IJCNN 2020 , Virtual, Glasgow , United Kingdom , 19/07/20 . https://doi.org/10.1109/IJCNN48605.2020.9207661
NO conference
NO Publisher Copyright: © 2020 IEEE.
NO The authors would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs. Eric L. Manibardo receives funding support from the Basque Government through its BIKAINTEK PhD support program (grant no. 48AFW22019-00002).
DS TECNALIA Publications
RD 29 jul 2024