Real-Time Measurements of Nitrogen Oxide Emissions from In-Use New York City Transit Buses Using a Chase Vehicle

2005 ◽  
Vol 39 (20) ◽  
pp. 7991-8000 ◽  
Author(s):  
Joanne H. Shorter ◽  
Scott Herndon ◽  
Mark S. Zahniser ◽  
David D. Nelson ◽  
Joda Wormhoudt ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Nitrogen Oxide ◽  
Real Time ◽  
York City ◽  
Nitrogen Oxide Emissions ◽  
Transit Buses

A Conversation and Two Epilogues

2016 ◽  
Author(s):  
Judy Malloy
Keyword(s):  
New York ◽  
New York City ◽  
Los Angeles ◽  
Real Time ◽  
York City ◽  
Cultural Centers ◽  
Main Street ◽  
The World ◽  
As If

When Kit Galloway and Sherrie Rabinowitz arrived in Telluride for Tele-Community in the summer of 1993, it seemed as if the whole town joined them on Main Street, as using slow scan video they connected townspeople and visiting digerati with artists, universities, and cultural centers around the world. Their Electronic Café had already presented New York City pedestrians with display windows of people waving and talking real time from Los Angeles (...

Real-Time Measurements of SO2, H2CO, and CH4Emissions from In-Use Curbside Passenger Buses in New York City Using a Chase Vehicle

2005 ◽  
Vol 39 (20) ◽  
pp. 7984-7990 ◽  
Author(s):  
Scott C. Herndon ◽  
Joanne H. Shorter ◽  
Mark S. Zahniser ◽  
Joda Wormhoudt ◽  
David D. Nelson ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City

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

2020 ◽  
Vol 2674 (9) ◽  
pp. 843-854
Author(s):  
Shay Lehmann ◽  
Alla Reddy ◽  
Chan Samsundar ◽  
Tuan Huynh
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Service Management ◽  
Short Term ◽  
Subway System ◽  
Competing Priorities ◽  
Position Monitoring

Like any legacy subway system that first opened in the early 1900s, the New York City subway system operates using technology that dates from many different eras. Although some of this technology may be outdated, efforts to modernize are often hindered by budgetary limits, competing priorities, and managing the tradeoff between short-term service disruptions and long-term service improvements. At New York City Transit (NYCT), the locations of all trains on all lines are not visible to any one person in any one place and, for much of the system, train locations can only be seen at field towers for the handful of interlockings in its operational jurisdiction as result of the legacy signal system, which may come as a surprise to many daily commuters or personnel at newer metros. In 2019, developers at NYCT gained full access to the legacy signal system’s underlying track circuit occupancy data and developed an algorithm to automatically track trains and match these data with schedules and manual dispatchers’ logs in real time. This data-driven solution enables real-time train identification and tracking long before a full system modernization could be completed. This information is being provided to select personnel as part of a pilot program via several different tools with the aim of improving service management and reporting.

Real-Time Estimation of Platform Crowding for New York City Subway: Case Study at Wall Street Station on No. 2 and No. 3 Lines in Financial District

2017 ◽  
Vol 2648 (1) ◽  
pp. 68-78 ◽  
Author(s):  
Adam Caspari ◽  
Brian Levine ◽  
Jeffrey Hanft ◽  
Alla Reddy
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Time Estimation ◽  
Time Of Day ◽  
Time Data ◽  
Wall Street ◽  
Time Model ◽  
Control Center

Amid significant increases in ridership (9.8% over the past 5 years) on the more than 100 year-old New York City Transit (NYCT) subway system, NYCT has become aware of increased crowding on station platforms. Because of limited platform capacity, platforms become crowded even during minor service disruptions. A real-time model was developed to estimate crowding conditions and to predict crowding for 15 min into the future. The algorithm combined historical automated fare collection data on passenger entry used to forecast station entrance, automated fare collection origin–destination inference information used to assign incoming passengers to a particular direction and line by time of day, and general transit feed specification–real time data to determine predicted train arrival times used to assign passengers on the platform to an incoming train. This model was piloted at the Wall Street Station on the No. 2 and No. 3 Lines in New York City’s Financial District, which serves an average 28,000 weekday riders, and validated with extensive field checks. A dashboard was developed to display this information graphically and visually in real time. On the basis of predictions of gaps in service and, consequently, high levels of crowding, dispatchers at NYCT’s Rail Control Center can alter service by holding a train or skipping several stops to alleviate any crowding conditions and provide safe and reliable service in these situations.

Development of a Real-Time Stringlines Tool to Visualize Subway Operations and Manage Service at New York City Transit

2015 ◽  
Vol 2538 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Boris Suchkov ◽  
Mikhail Boguslavsky ◽  
Alla Reddy
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City

scholarly journals Nowcasting for Real-Time COVID-19 Tracking in New York City: Evaluation Study Using Reportable Disease Data From the Early Stages of the Pandemic (Preprint)

10.2196/25538 ◽  
2020 ◽  
Author(s):  
Sharon K Greene ◽  
Sarah F McGough ◽  
Gretchen M Culp ◽  
Laura E Graf ◽  
Marc Lipsitch ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Evaluation Study ◽  
Early Stages

scholarly journals Evaluation of Nowcasting for Real-Time COVID-19 Tracking — New York City, March–May 2020

2020 ◽  
Author(s):  
Sharon K. Greene ◽  
Sarah F. McGough ◽  
Gretchen M. Culp ◽  
Laura E. Graf ◽  
Marc Lipsitch ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Negative Binomial ◽  
Situational Awareness ◽  
Prediction Interval ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Moving Window

AbstractTo account for delays between specimen collection and report, the New York City Department of Health and Mental Hygiene used a time-correlated Bayesian nowcasting approach to support real-time COVID-19 situational awareness. We retrospectively evaluated nowcasting performance for case counts among residents diagnosed during March–May 2020, a period when the median reporting delay was 2 days. Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days the nowcasts were conducted, with Mondays having the lowest mean absolute error, of 183 cases in the context of an average daily weekday case count of 2,914. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported health department leadership in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

Sewer System SCADA and Real Time Controls To Potentially Help New York City DEP Save Millions of Dollars

2004 ◽  
Vol 2004 (7) ◽  
pp. 145-156
Author(s):  
Stella Rozelman ◽  
Joseph Frissora ◽  
Vatche Minassian ◽  
Jean Lamarre
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Sewer System
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