Changing Idling Behavior Through Dynamic Idle Detection and Air Quality Messaging

Air quality impacts on human health are an increasing concern globally. Vehicle pollution is a particular concern because of its multiple adverse health effects, and discretionary vehicle idling contributes significantly to local-scale poor air quality. This study introduces a novel approach to traditional static (non-changing) anti-idling signage. Here, we demonstrate a system, called SmartAir, that provides dynamic social-norm messages to drivers coupled with information about idling status or vehicle emissions in the area. A machine learning algorithm with audio and video inputs determines vehicle idling status. Vehicle emissions are measured using a suite of low-cost air quality nodes. In this study, we show that the SmartAir system reduces idling time by 28.0% and local CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentrations by 29.5% compared to background.