RT Conference Proceedings
T1 Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: 												Alternatives and Pitfalls
A1 Manibardo, Eric L.
A1 Laña, Ibai
A1 Del Ser, Javier
AB This work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-quality predictive models becomes feasible under the TL paradigm, enabling the generation of new proper models with few data. In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting. Then, traditional batch learning is compared against TL based models using real traffic flow data, collected by deployed loops managed by the City Council of Madrid (Spain). In addition, we apply Online Learning (OL) techniques, where model receives an update after each prediction, in order to adapt to traffic flow trend changes and incrementally learn from new incoming traffic data. The obtained experimental results shed light on the advantages of transfer and online learning for traffic flow forecasting, and draw practical insights on their interplay with the amount of available training data at the location of interest.
PB Institute of Electrical and Electronics Engineers Inc.
SN 978-1-7281-4150-3
SN 9781728141497
SN 978-1-7281-4149-7
YR 2020
FD 2020-09-20
LA eng
NO Manibardo , E L , Laña , I & Del Ser , J 2020 , Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions : Alternatives and Pitfalls . in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 . , 9294557 , 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 , Institute of Electrical and Electronics Engineers Inc. , pp. 1-6 , 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 , Rhodes , Greece , 20/09/20 . https://doi.org/10.1109/ITSC45102.2020.9294557
NO conference
NO Publisher Copyright: © 2020 IEEE.
NO The authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELKARTEK programs, as well as the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project). 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 jun 2024