Athanasios I. Salamanis
ABSTRACT
The increasing availability of large volumes of traffic data has led to the development of several short-term traffic prediction models. Training these models is a computationally intensive process due to the volume of available traffic data. Therefore, having effective methods for accelerating this process is considered necessary. In this paper, we propose an efficient method for accelerating the training process of multiple short-term traffic prediction models in large-scale traffic networks. In particular, the traffic data is organized into separate files so that the training process for one model is independent of the others. These files are distributed in the cores of a shared-memory multicore processor so as to train multiple models simultaneously. Appropriate measures have been taken to limit the memory footprint of the proposed method, as well as to enhance its load balancing capabilities. The proposed method was applied to five short-term traffic prediction models, and evaluated using large-scale real-world traffic data. Preliminary experimental results indicate that the proposed method exhibits nearly linear speedup for the training process of all models, while maintaining their prediction performance.
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