No abstract

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 at a train level for the first time, ( b) basing path choice on actual train movements rather than on schedules so that uneven loadings can be accurately captured, and ( c) running fast enough to be used daily and being sufficiently automated to run with minimal human intervention. The model integrates entry data from fare cards with actual train movement data from a wide range of electronic systems and schedules. The model assigns riders to trains by using a Frank–Wolfe approach, including Dijkstra’s algorithm for shortest paths, with customizations designed for transit. These customizations improve speed, enable the algorithm to model delays better, and allow for multiple types of riders with different preferences for transfers and crowding. The size and the complexity of the NYCT system make for a challenging test case computationally. Approximately 6 million trips are made on a busy weekday, and these are assigned to a time-expanded network containing more than 3 million nodes and 7 million arcs. The model is automated and runs fast enough that it can be used daily. Validation against manual counts indicates strong results, with the R2 for max load point volumes for the morning peak hour equal to .91.

No abstract

With ridership near modern highs, New York City Transit's (NYCT) subway network frequently operates at or near capacity. This makes maintaining a high-quality service both challenging, due to the lack of ''slack,'' and exceptionally important, due to the large number of riders affected by disruptions. To this end, train dispatchers constantly monitor the network and adjust service to respond to delays. This paper presents a decision support system developed by NYCT which uses real-time train movements and historical ridership information to provide dispatchers with recommendations for holds and station skips in real time. The system uses heuristic headway criteria to determine hold or skip candidate trains, and then estimates the net passenger time savings of each potential hold or skip using estimated origin-destination flows and basic assumptions about passenger behavior. Potential actions that meet a passenger benefit threshold are recommended, and communicated to dispatchers with a simple dashboard. A pilot implementation of the system has been in use at NYCT's Rail Control Center (RCC) for several months, though many details of the system are still in development. Initial observations indicate the system is helping dispatchers manage train service more effectively, producing large passenger time savings.