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 RNN-LF. 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.
Highlights
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 smart
The remaining of this paper addresses this by proposing a novel framework that integrates an RNN for long-term traffic flow forecasting based on the weather information, the historical traffic flow data, and the contextual information
The scalability of the proposed framework has been investigated, and the results show that RNN-LF outperforms the stateof-the-art learning models for predicting sequence data
Summary
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 smart. In the last few years, several learning algorithms have been proposed for traffic flows forecasting [1,2,3,4,5]. These algorithms are only able to predict short-term flow, i.e flows represented by a single flow observation. Obtaining the best fitting parameters for these techniques is challenging, which reduces the accuracy of the prediction process (noabely in scenarios of heterogeneous data). These methods could not be applied for long-term traffic flow forecasting, and new methods are needed
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