A recurrent neural network for urban long-term traffic flow forecasting
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A recurrent neural network for urban long-term traffic flow forecasting Asma Belhadi1 · Youcef Djenouri2 · Djamel Djenouri3 · Jerry Chun-Wei Lin4
© The Author(s) 2020
Abstract This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNNLF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data. Keywords Learning long-term flows · Recurrent neural network · Weather information · Contextual information
1 Introduction Recent advances in technologies and infrastructures, such as high support GPS, mobile communications, wireless sensing and internet of things, make our cities more and more connected, digitalized, and thus smart. One of the most attractive smart city applications is urban traffic flow Asma Belhadi
[email protected] Youcef Djenouri [email protected] Djamel Djenouri [email protected] Jerry Chun-Wei Lin [email protected] 1
RIMA, USTHB University, Algiers, Algeria
2
Department of Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway
3
Computer Science Research Centre, Department of Computer Science and Creative Technologies, University of the West of England, Bristol, UK
4
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
analysis. The traffic flow is computed by counting the number of objects (cars, passengers, cabs, buses, etc.) that cross a given location during a time interval. In the last few years, several learning algorithms have been proposed for traffic flows forecasting [1–5]. However, these algorithms are only able to predict short-term flow, i.e flows represented by a single flow observation. That is, they are only able to provide short-term flow forecasting, but not long-term. Long-term flows, is defined by the set of flow sequences captured during a specific time period [6, 7]. In the last decade, several works have been proposed for sequence data forecasting. Zhao et al. [8] proposed the global weighting algorithm for forecasting sequence data retrieved from real image datasets. The sequence data is constructed from the set of the training data, which is viewed as basis function. Each sequence data is estimated by weighting all the basis functions using the average distance of all the training data. The
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