A Panel Data Model-Based Multi-Factor Predictive Model of Highway Electromechanical Equipment Faults

Highway electromechanical equipment faults can be caused by multiple factors. To study the effects of these factors on electromechanical equipment faults, a panel data model was developed. Traffic flow, temperature difference, relative humidity, and wind speed were selected as explanatory variables, with the electromechanical faults being the response variable. The data was based on an investigation of electromechanical equipment faults, and the above-mentioned environmental variables were collected from six highways in Beijing during 2012–2013. Panel data models for pooled regression models and individual fixed and random effects were established. A unit root test was performed on the data sequence. Finally, the F-test and the Hausman-test were used to compare the three predictive models and determine the optimal one. It was found that the individual fixed effects model was superior to the other two. Positive effects of temperature, relative humidity, and wind speed on the electromechanical equipment faults were found to be significant, while traffic flow had a significant negative effect on the electromechanical equipment faults. This paper included four factors, more influences from other factors should be discussed in the further research. In general, this paper showed the importance of traffic flow to highway electromechanical equipments and can be potentially used to increase the service life of the equipments. The contribution of this work would provide theoretical support for fault prediction and emphasize the importance of environment-related preventive maintenance on the highway electromechanical equipment.