University of Kentucky (UK) Evaluation of Lextran's Accelerating Innovative Mobility (AIM) Program

UK personnel will work with TripShot and Lextran to provide an assessment of application usage and impact. In addition, we will explore the use of artificial intelligence (AI) to forecast demand of services to better inform optimal timing of Lextran bus arrivals across campus. Specifically, machine learning (ML) techniques will be implemented to provide expected demand information for Lextran buses across campus through TripShot app & website, which can inform the spacing and timing of vehicle arrivals and departures throughout the day. UK personnel will function as external evaluator to TripShot and Lextran for this engagement. Focus Area 1: UK personnel will work with TripShot and Lextran to evaluate the historic bus travel patterns of arrivals, departures, and stops at an individual vehicle level with a focus placed on headway adherence. UK personnel will review the efforts undertaken by the AIM grant study and identify any impact on efficiency and convenience for users. Focus Area 2: UK personnel will work with TripShot and Lextran to perform analysis and create predictive models based on both internal and external historical data. For example, monitoring covariates (such as weather, day of week, time of year, local events) will be important for predicting travel patterns and forecasting the demand of buses.