Abstract
Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.
Highlights
Road traffic has become a more difficult event to manage as a result of the growth of cities and the increasing demand for transportation
The main motivation of the developed method is to prepare the training sets to improve the predictive performance of models that can be trained with datasets with consecutive data samples
The Periodic Clustering and Prediction (PCP) approach which is the cooperation of k-Means Clustering Algorithm and the Artificial Neural Networks (ANN) has been developed for short-term traffic flow prediction
Summary
Road traffic has become a more difficult event to manage as a result of the growth of cities and the increasing demand for transportation. The main motivation of the developed method is to prepare the training sets to improve the predictive performance of models that can be trained with datasets with consecutive data samples For this purpose, the Periodic Clustering and Prediction (PCP) has been developed with a novel approach of k-means clustering algorithm and ANNs. The general procedure of PCP is as follows. There are parameters in this approach, but initially, their numbers and attributes are variable and cannot be set at the beginning [6] These models are data-driven and make traffic flow predictions by using sophisticated algorithms. The Periodic Clustering and Prediction (PCP) approach which is the cooperation of k-Means Clustering Algorithm and the ANNs has been developed for short-term traffic flow prediction. The dataset is divided into periods and the periods are grouped with k-Means Clustering
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