Designing and Implementing Real-Time Bus Time Predictions using Artificial Intelligence
mode - bus, technology - intelligent transport systems, planning - methods, ridership - commuting
Bus, machine learning (ML) modeling
Managing expectations is vital to ensuring commuter satisfaction with their public transportation service. When customers are given an estimated time for their bus and must wait way longer, they lose a sense of control over their commute and become frustrated. In this paper, we present a novel machine learning (ML) modeling approach in which we train and implement specialized models for every single segment of travel time and stop dwell time in our system to capture its uniqueness. The features for training the models include simple calendar and weather data. Most papers assume ideal operational conditions but in practice that’s almost never the case. Here, we combine our ML modeling approach with a flexible production-tested algorithm to combine model-generated dwell and travel time (or run time) predictions to produce predicted bus departure times. This algorithm is designed to handle real transit agency challenges like missing models (including those caused by changes to schedules and routes), timing points, and partially traveled segments. We also provide a reference architecture of how this algorithm can be brought to life in a scalable and cost-effective manner.
Permission to publish the abstract has been given by SAGE, copyright remains with them.
Wai, B., & Zhou, W. (2020). Designing and Implementing Real-Time Bus Time Predictions using Artificial Intelligence. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674(11), pp. 636-648.