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WorldCIST'19 - 7th World Conference on Information Systems and Technologies

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Traffic Flow Forecasting on Data-Scarce Environments using ARIMA and LSTM Networks

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.

Bruno Fernandes
Department of Informatics, ALGORITMI Centre, University of Minho
Portugal

Fábio Silva
Department of Informatics, ALGORITMI Centre, University of Minho
Portugal

Hector Alaiz-Moretón
Department of Electrical and Systems Engineering, Universidad de León, Escuela de Ingenierías
Spain

Paulo Novais
Department of Informatics, ALGORITMI Centre, University of Minho
Portugal

Cesar Analide
Department of Informatics, ALGORITMI Centre, University of Minho
Portugal

José Neves
Department of Informatics, ALGORITMI Centre, University of Minho
Portugal

 


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