scholarly journals A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City

2021 ◽  
Vol 101 ◽  
pp. 145-161
Author(s):  
Brian Yueshuai He ◽  
Jinkai Zhou ◽  
Ziyi Ma ◽  
Ding Wang ◽  
Di Sha ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Congestion Pricing ◽  
Time Of Day ◽  
Test Bed ◽  
Simulation Test ◽  
Pricing Policies ◽  
Agent Simulation ◽  
Multi Agent

Examination of New York City Transit’s Bus and Subway Ridership Trends During the COVID-19 Pandemic

2021 ◽  
pp. 036119812110288
Author(s):  
Anne Halvorsen ◽  
Daniel Wood ◽  
Darian Jefferson ◽  
Timon Stasko ◽  
Jack Hui ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Metropolitan Area ◽  
Relative Intensity ◽  
York City ◽  
Race And Ethnicity ◽  
New Technology ◽  
Time Of Day ◽  
Data Availability ◽  
Partial Data

The New York City metropolitan area was hard hit by COVID-19, and the pandemic brought with it unprecedented challenges for New York City Transit. This paper addresses the techniques used to estimate dramatically changing ridership, at a time when previously dependable sources suddenly became unavailable (e.g., local bus payment data, manual field checks). The paper describes alterations to ridership models, as well as the expanding use of automated passenger counters, including validation of new technology and scaling to account for partial data availability. The paper then examines the trends in subway and bus ridership. Peak periods shifted by both time of day and relative intensity compared with the rest of the day, but not in the same way on weekdays and weekends. On average, trip distances became longer for subway and local bus routes, but overall average bus trip distances decreased owing to a drop in express bus usage. Subway ridership changes were compared with neighborhood demographic statistics and numerous correlations were identified, including with employment, income, and race and ethnicity. Other factors, such as the presence of hospitals, were not found to be significant.

Flashbulb Memories of Personal Events of 9/11 and the Day after for a Sample of New York City Residents

2004 ◽  
Vol 95 (1) ◽  
pp. 304-310 ◽  
Author(s):  
Cheryl M. Paradis ◽  
Faith Florer ◽  
Linda Zener Solomon ◽  
Theresa Thompson
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
World Trade ◽  
World Trade Center ◽  
Time Of Day ◽  
Stressful Events ◽  
World Trade Center Attack ◽  
Trade Center

The present study assessed consistency of recollections of personal circumstances of the 9/11 World Trade Center attack and events of the day before (9/10), and the day after (9/12), in a sample of 100 New York City college students. The day before 9/11 represented an ordinary event. A questionnaire was administered twice, 1 wk. and 1 yr. after the 9/11 attack. Students were asked to describe their personal circumstances when hearing about the news of the World Trade Center attack and for the same time of day for 9/10 and 9/12. 18 students returned the follow-up questionnaire. Consistency of initial and follow-up responses for the central categories for both 9/11 and 9/12 of where, who, and activity was very high (9/11: “Where”-100%, “Who”-100%, “What”-94%; 9/12: “Where”-100%, “Who”-100%, “What”-80%). Recollections of 9/10 were significantly less consistent (“Where”-79%, “Who”-71%, “What”-71%). Analysis indicated that students formed vivid, consistent recollections during the events of both 9/11 and 9/12. It is likely that the events of 9/12 also became flashbulb memories, vivid recollections of traumatic events, because the emotional impact of the stressful events, i.e., police and military presence, disrupted schedules, relating to the 9/11 attack endured beyond the day of the attack.

The Rule of Choice: Frames and Overflows in the History of New York City Roadway Planning

2021 ◽  
pp. 0739456X2110413
Author(s):  
John H. West
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Congestion Pricing ◽  
Urban Residents ◽  
Jane Jacobs ◽  
Community Mobility ◽  
History Of

Two episodes of roadway planning in New York City—the 1937 Henry Hudson Parkway construction and the 2018 congestion pricing legislation—show the dangers and productive possibilities that result when planners “frame” problems as to be resolved by the choices of either experts or individual urban residents. Framing planning as a matter of choices is a reductive dualism that ignores the intellectual, material, and networked relationships that “overflow” such narrow conceptions. Planners and advocates seeking to intervene in choice frames, from Jane Jacobs to congestion pricing advocates, have repurposed metaphors, technology, and scale to link individuals with community, mobility with environmentalism, and planning with politics.

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

2002 ◽  
Vol 1817 (1) ◽  
pp. 183-187 ◽  
Author(s):  
James J. Barry ◽  
Robert Newhouser ◽  
Adam Rahbee ◽  
Shermeen Sayeda
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Peak Load ◽  
Time Of Day ◽  
Service Planning ◽  
Travel Patterns ◽  
Assignment Model ◽  
Serial Number ◽  
Destination Station

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.

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.

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