Towards Predicting Bus On-Time Performance in the Inland Empire

Riders of public transportation in recent years tend to expect the intelligence of transit systems to progress in tandem with modern technology, with a key factor of this being an improved on-time prediction and scheduling of bus systems. This work explores various regression algorithms, including Support Vector Regression and Linear Regression, along with evaluation of loss functions and regulation terms to address the bus arrival time problem. The experiment is a case study of Route 280 from the Foothill Transit Agency, and resulted in Huber Regression with ElasticNet penalty giving the most accurate prediction.