Keywords: machine learning, AI, LSTM, neural network
Summary:For a public transit authority, providing accurate bus arrival times plays a critical role in the organization’s quality of service and customer satisfaction. Utilizing the power of AI – in particular long short-term memory (LSTM) artificial recurrent neural network architecture provides the ability to look at a series of data observations and create a model to predict the next value in the sequence. Faced with the task of predicting bus’ arrival times, the Metropolitan Transit Authority in Houston, TX and EastBanc Technologies harnessed the power of AI and machine learning to explore the predictive power of external information. They faced complicated challenges however, including limited access to structured data on surrounding traffic, maps, and city structures and alternatively the team leveraged related, easily accessible data (i.e. weather information, social activity data, city events) to improve predictions. This session will examine what data was gathered, the architecture used and how the LSTM model was trained to accurately create predictions. The audience will learn how to assess the predictive power of each dataset, how to train a LSTM model, and the approach used for each dataset to train a separate model and apply ensemble algorithm to predict arrival time. Polina Reshetova, a Data Scientist at EastBanc Technologies, earned her PhD in complex systems data analysis.