Passenger-Centric Performance Metrics for the New York City Subway

Abstract

Like many transit agencies, New York City Transit (NYCT) has long relied on operations-focused metrics to measure its performance. Although these metrics, such as capacity provided and terminal on-time performance, are useful internally to indicate the actions needed to improve service, they typically do not represent the customer experience. To improve its transparency and public communications, NYCT launched a new online Subway Dashboard in September 2017. Two new passenger-centric metrics were developed for the dashboard: additional platform time (APT), the extra time passengers spend waiting for a train over the scheduled time, and additional train time (ATT), the extra time they spend riding a train over the scheduled time. Unlike similar existing metrics, NYCT's new methodology is easily transferable to other agencies, even those without exit data from an automated fare collection system. Using a representative origin-destination matrix and daily scheduled and actual train movement data, a simplified train assignment model assigns each passenger trip to a train based on scheduled and actual service. APT and ATT are calculated as the difference in travel times between these two assignments for each individual trip and can then be aggregated based on line or time period. These new customer-centric metrics received praise from transit advocates, academics, other agencies, and the press, and are now used within NYCT for communicating with customers, as well as to understand the customer impacts of operational initiatives.

Transit Accessibility Measurement Considering Behavioral Adaptations to Reliability

“…Transit agencies typically rely on operational reliability metrics or satisfaction surveys to quantify service quality at a route or network level (e.g., 23 – 25 ). Despite being discussed in literature (e.g., 26 , 27 ), practical applications of passenger-focused travel time metrics are rare ( 27 , 28 ). Given the difficulty of representing the distribution of passenger travel times for performance measurement, several proposed measures instead rely on the 95th percentile of travel time.…”

“…Recommendation for practice: Pending further research, we recommend applying simple heuristic decision rules (e.g., the passenger takes the first bus that arrives) or assuming that passengers take the route that is fastest according to the schedule ( 28 ). We consider the former approach more appropriate in the case of overlapping lines and the latter approach more appropriate in the case of non-overlapping routes.…”

Automated Train Identification and Train Position Monitoring at New York City Transit

“…In 2014, NYCT released a real-time stringlines application for internal use ( 6 ). This tool plots time versus distance at a station level, allowing each train’s movement to be abstracted into a single line where it can be easily compared relative to surrounding trips to identify headway irregularities and slowdowns.…”

“…Dispatchers who previously operated remotely in local master towers with information only about anonymous track occupancy within the interlocking controlled by their tower could now see exactly which trains occupy track circuits from terminal to terminal, and routes are automated based on the schedule with the potential to manually override them in the electronic system for service management. The system, known as Automatic Train Supervision (ATS-A), enabled a more holistic response to service management along with many ancillary benefits, including countdown clocks, more accurate reporting of performance metrics ( 5 , 6 ), and development of internal service management tools ( 7 , 8 ).…”

Linking Incidents to Customers (LINC): An Algorithm for Linking Incidents to Rail Customer Delays Inspired by Traffic Flow Theory

“…Other transit systems including the New York City Subway have since moved toward customer-centric performance metrics ( 5 ).…”