The development of a prescreening model to identify failed and gross polluting vehicles

Abstract

The California State Bureau of Automotive Repair uses a high-emitter profile model to direct, or screen a fraction of the vehicle fleet in for inspection and maintenance testing at test-only facilities. Reviews by the California Inspection/Maintenance Review Committee showed the high-emitter profile to be inefficient and in need of improvement. In this study, using in-use vehicle emissions data from California's statewide smog check program, we specified a new multinomial logit model designed to improve the screening efficiency for targeting potential failed and gross polluting vehicles. Modeling results show that factors such as odometer reading, model year, vehicle make, as well as the presence of emissions control systems are significant factors in predicting the likelihood that a screened vehicle will test as a failed or a gross polluting vehicle. Comparisons indicate that the new multinomial logit model specification can predict various inspection/maintenance test outcomes more accurately than the existing high-emitter profile model. © 2007.