Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/67991

TitleTraffic flow forecasting on data-scarce environments using ARIMA and LSTM Networks
Author(s)Fernandes, B.
Silva, Fábio
Alaiz-Moretón, Hector
Novais, Paulo
Analide, Cesar
Neves, José
KeywordsAutoRegressive Integrated Moving Average
Data-scarce environments
Long Short-Term Memory
Road safety
Traffic flow forecasting
Issue dateJan-2019
PublisherSpringer
JournalAdvances in Intelligent Systems and Computing
CitationFernandes, B., Silva, F., Alaiz-Moretón, H., et. al. (2019, April). Traffic Flow Forecasting on Data-Scarce Environments Using ARIMA and LSTM Networks. In World Conference on Information Systems and Technologies (pp. 273-282). Springer
Abstract(s)Traffic flow forecasting has been in the mind of researchers for the last decades, remaining a challenge mainly due to its stochastic nonlinear nature. In fact, producing accurate traffic flow predictions would be extremely useful not only for drivers but also for those more vulnerable in the road, such as pedestrians or cyclists. With a citizen-first approach in mind, forecasting models can be used to help advise citizens based on the perception of outdoor risks, dangerous behaviors and time delays, among others. Hence, this work develops and evaluates the accuracy of different ARIMA and LSTM based-models for traffic flow forecasting on data-scarce and non-data-scarce environments. The obtained results show the great potential of LSTM networks while, in contrast, expose the poor performance of ARIMA models on large datasets. Nonetheless, both were able to identify trends and the cyclic nature of traffic.
TypeConference paper
URIhttps://hdl.handle.net/1822/67991
ISBN9783030161804
DOI10.1007/978-3-030-16181-1_26
ISSN2194-5357
Publisher versionhttps://link.springer.com/chapter/10.1007/978-3-030-16181-1_26
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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