A Novel Approach for Transforming Bus Service Planning Using Integrated Electronic AFC & AVL Data at MTA in New York City

The installation of an Automated Vehicle Location (AVL) system alongside existing Automated Fare Collection (AFC) data on all New York City buses spurred development of a city-wide bus passenger boarding and alighting ridership model at MTA in New York City. These advances in ridership modeling allowed for analysis of trip passenger loads and 100% passenger origin-destination data at both route and neighborhood level on single-mode and intermodal trips. Analysis techniques that relied solely on professional judgement due to lack of available data are now being replaced by more sophisticated statistical techniques. This paper describes two case studies and the resulting service planning potential from having access to fully integrated, rich, big data sources. One describes the first neighborhood-wide analysis of performance, ridership, and running times based on 100% electronic data in Co-op City, Bronx. Detailed data allowed planners to pinpoint very specific, low-cost reroutes and stop changes to impact and better serve hundreds of riders daily. The second case study describes the identification of a long route in Manhattan with poor performance and determination of optimal route split location minimizing passenger impact by using modeled route-level origin-destination (O-D) data. Performance and running time data on the original and surrounding routes were used to determine an expectation for performance and running times on the new routes. In both examples, new data sources allowed for novel analysis throughout the entire planning process, beginning with problem investigation through forecasting ridership and cost impacts of proposed service adjustments.