Origin and Destination Estimation in New York City with Automated Fare System Data

Abstract

New York City Transit’s automated fare collection system, known as MetroCard, is an entry-only system that records the serial number of the MetroCard and the time and location (subway turnstile or bus number) of each use. A methodology that estimates station-to-station origin and destination (O-D) trip tables by using this MetroCard information is described. The key is to determine the sequence of trips made throughout a day on each MetroCard. This is accomplished by sorting the MetroCard information by serial number and time and then extracting, for each MetroCard, the sequence of the trips and the station used at the origin of each trip. A set of straightforward algorithms is applied to each set of MetroCard trips to infer a destination station for each origin station. The algorithms are based on two primary assumptions. First, a high percentage of riders return to the destination station of their previous trip to begin their next trip. Second, a high percentage of riders end their last trip of the day at the station where they began their first trip of the day. These assumptions were tested by using travel diary information collected by the New York Metropolitan Transportation Council. This diary information confirmed that both assumptions are correct for a high percentage (90%) of subway users. The output was further validated by comparing inferred destination totals to station exit counts by time of day and by estimating peak load point passenger volumes by using a trip assignment model. The major applications of this project are to describe travel patterns for service planning and to create O-D trip tables as input to a trip assignment model. The trip assignment model is used to determine passenger volumes on trains at peak load points and other locations by using a subway network coded with existing or modified service. These passenger volumes are used for service planning and scheduling and to quantify travel patterns. This methodology eliminates the need for periodic systemwide O-D surveys that are costly and time-consuming. The new method requires no surveying and eliminates sources of response bias, such as low response rates for certain demographic groups. The MetroCard market share is currently 80% and increasing. MetroCard data are available continuously 365 days a year, which allows O-D data estimation to be repeated for multiple days to improve accuracy or to account for seasonality.

Smart Card Data Mining of Public Transport Destination: A Literature Review

“…Trip chaining model was first applied by Barry et al [24] to infer the origin and destination trip tables, which is based on two key assumptions: the alighting station of the current subway trip is the aboard station of next subway trip and most riders end their last journey of the day at the station where they begin their first trip of the day.…”

“…Only several AFC systems belong to entry-exit system, which need users swiping smart card both at the beginning and the end of travel, just like AFC system of South East Queensland, Australia [4], AFC system of Seoul [27]. Hence many researchers devoted themselves to inferring missing destination information for entry-only AFC systems [2,5,21,24,[28][29][30]. However, these scholars used various methods and obtained different matching rate.…”

“…For example, AFC system of New York City, America [24], AFC system of Chicago, America [5], public transport system of Santiago, Chile [25], AFC system of Guangzhou, China [26]. Only several AFC systems belong to entry-exit system, which need users swiping smart card both at the beginning and the end of travel, just like AFC system of South East Queensland, Australia [4], AFC system of Seoul [27].…”

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

“…For some entry-only smart card systems, trip destination information is not recorded but needs to be imputed. Different methodologies haven been proposed to estimate the origindestination pairs and alighting time [7,4]. Jang [5] has studied the travel time and transfer activities in Seoul, South Korea using smart card data, which provides a comprehensive travel time map and basic understanding of transit services.…”

Shared Communications: Volume 1. A Summary and Literature Review

“…MetroCards are individually identifiable by a serial number. Data are collected on card usage every time the card is inserted into a fare reader (Berry, 2001). …”