An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System - Alkhatib, Osama (2019)
Predictive maintenance of Electronic Toll Collection System is a major subject in traffic engineering due to the complexity of the system and the difficulty of predicting the components failures. Two types of machine learning models namely classification and regression model were developed and implemented to predict the failure and abnormal behavior of the system. Nevertheless, the accuracy and performance of these models are questioned since they do not account for other system information. Therefore, for this paper multiple machine learning algorithms are investigated to predict system failure based on vehicle trips information as well as maintenance management historical data including preventive maintenance and corrective maintenance. Historical data of Dubai Toll Collection System is utilized to investigate multiple machine learning algorithms. Experiment is performed using Azure Machine Learning (ML) platform to test and assess the most efficient model that would predict the failure of system elements and predict the abnormality of the operation. Based on the experimental results, the predictions can be made to detect failure and forecast traffic amount. The models presented prove that data analytics can create new value in an ETC environment. The methods and tools used for modeling the prediction model can be generalized to be used in the rest of the ETC system also. As the amount of data grows daily, the model can be trained with more and more data as time passes. Therefore, the model can be re-generated from time to time to gain better results. There are no previous papers or literature reviews on applying artificial intelligence in predictive maintenance for Electronic Toll Collection failure forecast to perform a comparison of the effectiveness of Machine Learning Models. Despite having different performance results on predicting failures, most of the models produced close outcomes. Meaning no “perfect” machine learning algorithm that will produce good results at particular problem, in fact for each type of problem a specific algorithm is suited and might achieves good outcome, while another algorithm fails heavily. In addition, it relates to a great extent on the nature of dataset and the aim of model development.