Modern data analytics approach to on-time performance monitoring of historical transit data.
Transit agencies must know the quality of transit services as much as possible. However, the underlying data about arrivals, predicted arrival times, occupancy levels, and vehicle locations may come from various sources and exist in different formats. Additionally, the scale of accumulated data may require specialized tools for working with "big data." Nevertheless, staff members who make decisions about service deserve access to accurate and clear data regardless of the complexity and scale of the underlying information.
Riders know through their direct experience when service doesn't meet their expectations. While some riders can advocate for themselves, others may accept or get accustomed to poor service. When agencies have access to reliable service information, they can improve service, how they communicate about service, and let data speak for service quality.