Data-driven Bus Crowding Prediction Models Using Context-specific Features

Abstract

Public transit is one of the first things that come to mind when someone talks about “smart cities.” Most transit technologies have focused on answering “When will my bus arrive?”; little has been done to answer “How full will my next bus be?” — which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictive models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines by up to 8 times.

This work is part of the PittSmartLiving project.