In response to the DOTs FY21 SBIR topic 21-FT2, Interphase Materials (IPM) proposes the development of a low-cost automated data collection system that uses machine learning (ML), a type of artificial intelligence (AI), to predict and alert transit agencies of biological and viral contamination risks making condition-based sanitization possible for more effective and efficient operations.The COVID-19 pandemic has highlighted the risk that bacteria and viruses pose to those that use public transportation and demonstrated the need for improvementin the current abilities of public transit agencies to monitor and reduce the risk of disease transmission on transit vehicles effectively and efficiently. IPM proposes to develop an ML model that takes readily available data, such as rider numbers, vehicle size, weather conditions, etc., and additional data from inexpensive sensors, such as aerosol samplers, to predict spikes in bacteria and virus growth on transit vehicles. This ML model will be used as a risk assessment tool to make informed and flexible condition-based sanitization schedules for transit vehicles to ensure rider safety while minimizing the cost of vehicle cleaning.
