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Due to an impending fiscal crisis, we were tasked by a large public transit agency to analyze the entire bus route network to identify opportunities for immediate cost savings. Using a quick response analytical assessment of the system’s 235 routes, we were able to quickly identify routes that...
During a period of high and growing bus ridership at a large city bus system, there was a need to systematically and quickly identify the routes and time periods that needed increases and where buses could be harvested at identified declines. With approximately 4,500 buses operating on 235...
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. These advances in ridership modeling allowed for analysis of trip passenger loads and 100%...
Over two million trips are taken every weekday across the New York City Transit (NYCT) bus network. Revising the schedules for each of these routes is a labor-intensive process, and, thanks to limited resources, fewer than half of all routes are examined each year. Traditionally, schedules have been...
We were tasked by a major commuter rail operator to analyze ticket sales data to determine ridership with origin-destination and time-of-day detail. The operator had four distinct sales channels: mobile ticketing, paper ticket vending machines, monthly direct-mail ticket subscriptions, and on-board sales by train crew. The mobile ticket...
Outside the railroad industry, passenger trip times are often treated as a performance measure or yardstick of the industry’s health. However, many factors not known or well understood by casual observers affect trip times—indeed, some are recognized techniques utilized by infrastructure owners to deliver journey time reductions. Factors...
Over two million trips are taken every weekday across the New York City Transit (NYCT) bus network. Revising the schedules for each of these routes is a labor-intensive process, and because of limited resources, fewer than half of all routes are examined each year. Traditionally, schedules have been...
The installation of an Automatic Vehicle Location (AVL) system alongside existing Automated Fare Collection (AFC) data spurred development of an inferred bus boarding and alighting ridership model at New York City Transit (NYCT), allowing for 100% passenger origin-destination (O-D) data citywide. Analysis techniques that relied primarily on professional...
Subway ridership estimates are important to transit operators for both internal applications (e.g., setting service frequencies, prioritizing station upgrades) and external reporting (e.g., to the National Transit Database). New York City Transit (NYCT) is developing a new model that will accomplish three primary objectives: (a) estimating subway ridership...
To support bus service scheduling and planning, New York City Transit (NYCT) put into production a ridership application to determine surface transit boarding and alighting locations for each of approximately 2.8 million daily passenger trips on 218 bus routes. The application combined data from an automated vehicle location...
