Assessing Rideshare Satisfaction among a University Community

On-demand mobile applications for rideshare services are a relatively new transportation phenomenon that encourages the reallocation of resources on a peer-to-peer basis. This shared economy promotes efficiency and sustainability by maximizing the use of existing resources and minimizing fuel consumption, congestion, and transportation inequity; however, customer satisfaction and retention must be assessed to fully realize these benefits. Prior research explores ride ratings through the lens of customer biases, but the literature lacks an evaluation of the interplay between customer satisfaction and various trip characteristics. This study analyzed the service criteria of rideshare trips taken both to and from a university in Arlington, Texas, between 2021 and 2022 and developed a random forest model to evaluate the impact of various trip characteristics on ride ratings and predict the likelihood of the ratings being high. The findings revealed that ride distance, duration, month, and day of the week most significantly impacted university rideshare ratings. Partial dependence plots were also developed to enhance the model's interpretability, and managerial implications and policy strategies to improve customer satisfaction and encourage feedback are discussed. The findings offer valuable implications for rideshare service providers, policymakers, and transportation professionals.