Open Data about Mobility
NYC Open Data Week – March 25, 2026
VIDEO | AUDIO | RECAP EN / ES / FR | INFO | INDEX
Speakers: Hong Yuan - Open Data Administrator, NYC DOT; Esteban Doyle - Senior Planning Coordinator, NYC DOT; Lisa Mae Fiedler - Open Data Manager, MTA; Jaspreet Lal - Graduate Intern, Open Data, MTA; Alec Bardey - Mobility Data Program Lead, NYC DOT; Mark Seaman - Senior Economist, Policy Unit, NYC DOT
Moderator: Hong Yuan - NYC DOT
Introduction and Overview of Mobility Data Collaboration
Hong Yuan opened the session by introducing the collaboration between NYC DOT and the MTA around mobility-related open data. She explained that the presentation would cover multiple transportation datasets spanning pedestrian activity, buses, subways, traffic counting, congestion pricing, and the newly released Citywide Mobility Survey.
Hong emphasized the broader goal of encouraging agencies to work together by:
Sharing transportation datasets
Identifying relationships between datasets
Supporting cross-agency analysis
Improving public understanding of mobility patterns
She also encouraged participants to continue conversations during an Open Data Week networking event later that evening.
Pedestrian Counts and the Public Realm
Esteban Doyle introduced NYC DOT’s Public Realm Unit, which focuses on improving walkability and pedestrian comfort throughout New York City. He argued that pedestrian activity is historically undercounted compared to vehicle traffic, despite its importance for urban planning and street design.
Doyle stressed that understanding where and how people walk is essential when balancing competing uses of public street space, including:
Cars
Buses
Bicycles
Pedestrians
Curb uses
Deliveries
NYC DOT’s Biannual Pedestrian Count Program
Doyle explained that NYC DOT’s pedestrian count program began in 2007 as a way to track economic activity during the Great Recession. The city now conducts pedestrian counts at 114 locations across all five boroughs.
Count locations include:
Commercial corridors
East River bridges
Harlem River bridges
Counts are collected:
Twice annually
During weekday morning peaks
During weekday evening peaks
During Saturday midday periods
DOT uses these counts to calculate a “pedestrian volume index,” which compares average pedestrian activity against the original 2007 baseline.
The data showed:
Significant pedestrian growth during the early 2010s
Sharp declines during the COVID-19 pandemic
Partial post-pandemic recovery
Volumes still below pre-pandemic levels
Doyle cautioned that the dataset reflects only part of the city because many locations are concentrated in Manhattan commercial areas rather than residential neighborhoods.
Limitations of Traditional Pedestrian Counting
Doyle outlined several limitations in the existing pedestrian count program.
Challenges include:
Limited geographic coverage
Heavy concentration in Manhattan
Lack of residential street counts
Seasonal variability
Weather-related variability
Labor-intensive manual counting
The process currently requires:
Setting up cameras
Recording footage
Manual review and counting
Cataloging results
DOT is therefore exploring alternative approaches for measuring pedestrian activity more comprehensively and efficiently.
The Pedestrian Demand Map
Doyle introduced DOT’s Pedestrian Demand Map, which was created as part of the city’s pedestrian mobility planning process.
Because the city cannot directly count pedestrians everywhere, DOT instead modeled pedestrian demand using “pedestrian generators,” including:
Parks
Schools
Tourist attractions
Commercial districts
Using these variables, every street in New York City was categorized into one of five demand levels:
Global
Regional
Neighborhood
Local
Baseline
These classifications help guide decisions about:
Sidewalk widths
Street redesigns
Pedestrian improvements
Public realm investments
Future Pedestrian Modeling Efforts
Doyle discussed future ambitions to move beyond static demand mapping toward dynamic pedestrian route modeling.
Potential future analyses include:
Which side of a street pedestrians choose
Preferred crosswalks
Route selection behavior
Street-level pedestrian flow modeling
MIT recently released a preliminary pedestrian model for New York City, and DOT provided pedestrian count data to help calibrate the system.
DOT expressed strong interest in integrating such modeling techniques into future planning tools.
MTA Bus Route Segment Speed Data
Lisa Mae Fiedler introduced several MTA open datasets hosted on the New York State Open Data Portal.
One major dataset was the Bus Route Segment Speeds dataset, which provides operational speed information for buses between major route stops known as “time points.”
The dataset includes:
Average speed between time points
Average travel time
Distance traveled
Number of bus trips
aggregated by:
Month
Day of week
Hour of day
The dataset is generated using GPS “Bus Time” pings from more than 6,000 buses, with location updates every 30 seconds.
What Bus Speed Data Captures
Fiedler emphasized that the dataset intentionally reflects the real rider experience.
Speed calculations include:
Passenger boarding delays
Stoplights
Operator changes
Traffic congestion
Double-parked vehicles
Construction slowdowns
She demonstrated how combining segment-speed data with geospatial route and stop datasets allows analysts to build detailed route performance maps.
Examples included visualizations of the M1 bus in Manhattan, showing highly variable speeds across different route segments.
Subway Hourly Ridership Data
Jaspreet Lal presented MTA subway datasets.
The Subway Hourly Ridership dataset provides:
Hourly ridership estimates
Station-level entries
Payment method information
OMNY versus MetroCard usage
The dataset is updated weekly and is based on turnstile entries.
Visualizations showed:
January 2024 as the point where OMNY and MetroCard usage converged
Rapid subsequent dominance of OMNY
Sharp decline in MetroCard usage
Lal explained that three-month rolling averages were used to smooth fluctuations caused by:
Weekday seasonality
Holidays
Weather
Subway Schedule Data and CBTC
Lal also discussed the Subway Schedules dataset, which includes:
Base schedules
Supplemental schedules
Construction-related service changes
Holiday schedules
Using the data, the team visualized train arrival frequency distributions during weekday rush hours.
The presentation highlighted the superior consistency of the:
7 line
L line
which use Communication-Based Train Control (CBTC).
CBTC allows trains to:
Continuously communicate positions
Operate closer together safely
Adjust dynamically in real time
resulting in:
Shorter waits
More reliable service
Modernizing Vehicle Classification Counts with Computer Vision
Alec Bardey introduced NYC DOT’s effort to modernize vehicle classification counting using computer vision.
Current traffic counting methods rely heavily on:
Contractors setting up cameras
Manual review of footage
Human classification of vehicles
Approximately:
80% of costs are associated with manual counting
Only 20% involve recording and storage
DOT possesses:
15 years of traffic video footage
Corresponding vehicle count data
creating a major opportunity for machine learning applications.
YOLO Computer Vision Prototype
Bardey described the prototype system built using a YOLO computer vision model led by NYMTC fellow and PhD candidate Boshra Khalili.
The system:
Detects vehicles in video
Draws bounding boxes
Tracks movement across threshold lines
Automatically classifies vehicles
DOT demonstrated a prototype video where vehicles crossing a roadway were automatically identified and counted in real time.
Cleaning 15 Years of Legacy Traffic Data
A major challenge involves standardizing legacy vehicle classification count files.
Bardey showed examples of older spreadsheet formats containing:
Merged cells
Inconsistent headers
Contractor-specific layouts
Poor formatting
DOT is building automated workflows to:
Identify file formats
Clean data
Standardize structures
Create centralized databases
The goal is eventually to publish standardized traffic count datasets on NYC Open Data.
Future Computer Vision Applications
DOT hopes the new system will eventually:
Reduce counting costs
Expand traffic count coverage
Enable analysis-ready datasets
Generate counts from archived footage
Importantly, archived footage originally collected for one purpose — such as truck counts — could later be reused to generate:
Pedestrian counts
Bicycle counts
Additional mobility analyses
Congestion Pricing Open Data
Fiedler next discussed congestion pricing data.
The MTA’s Congestion Relief Zone Vehicle Entries dataset tracks:
Vehicle crossings into Manhattan south of 60th Street
Entry locations
Vehicle classes
10-minute intervals
The dataset began with the launch of congestion pricing in January 2025 and updates weekly.
Visualizations showed clear behavioral shifts:
Drivers rushing into Manhattan just before toll activation at 5 a.m.
Sharp immediate drops in entries after tolling begins
Fiedler emphasized that open data has been central to public reporting around congestion pricing.
Bridge and Tunnel Crossings Data
Fiedler also introduced MTA Bridges and Tunnels hourly crossing data, which includes:
Facility-level crossings
Directional flows
Vehicle class
Payment method estimates
The dataset uses:
Electronic tolling systems
Automated vehicle classification technology
to estimate:
E-ZPass usage
Toll-by-mail usage
Traffic flows
Fiedler described the dataset as one of the richest available sources for understanding regional traffic movement patterns.
The Citywide Mobility Survey
Mark Seaman presented NYC DOT’s Citywide Mobility Survey (CMS), a household travel survey conducted every two to three years.
The survey captures detailed information about:
How New Yorkers travel
Trip purposes
Transportation modes
Demographic differences
Equity impacts
The 2024 survey included:
3,500 participants
More than 100,000 trips
GPS-tracked travel data
Smartphone app participation
Web and phone survey options
Mobility Trends Revealed by CMS
Seaman reviewed several high-level findings.
Mode Share
Walking and driving were approximately tied as the city’s most common transportation modes in 2024.
Other findings included:
Transit usage still below 2019 levels
Partial post-pandemic recovery
Modest but notable increases in biking
Growth in Deliveries
The survey found:
41% of NYC households received deliveries on a typical day in 2024
Up from 31% in 2019
Growth occurred across categories including:
Packages
Restaurant takeout
Grocery deliveries
Open Streets Awareness
CMS also measured public familiarity with Open Streets programs.
Results showed:
Highest awareness in Manhattan core neighborhoods
Lower awareness in outer Brooklyn and Queens
Parking Analysis
Using CMS data, DOT analyzed where residents park their cars.
The survey identified areas with especially high levels of on-street parking dependence, including:
Inner Brooklyn
Upper Manhattan
Southern Bronx
This information helps DOT evaluate:
Parking demand
EV charging needs
Potential cruising-for-parking behavior
Estimating Mode Shift to Biking
CMS was also used to estimate what transportation modes bike trips may have replaced.
By comparing bike trip distances to similar trips by other modes, DOT estimated:
Approximately 36% of citywide bike trips may replace car trips
Approximately 15% in Manhattan specifically
The analysis helped DOT estimate potential greenhouse gas impacts of cycling growth.
Pedestrian Route Choice Modeling
Seaman described an advanced pedestrian route-choice model built using 17,000 walk trips from the 2019 CMS.
Using GPS traces and statistical modeling, researchers evaluated how pedestrians value street characteristics including:
Sidewalk width
Street trees
Traffic volume
Street lighting
Land use
Crime levels
Scaffolding
The project became the basis of a doctoral dissertation and is expected to be formally published.
Discussion on Post-Pandemic Pedestrian Declines
During Q&A, participants asked why pedestrian volumes have struggled to fully recover since COVID-19.
Doyle and Seaman pointed to several factors:
Remote work reducing commuting
Lower transit ridership
Growth in deliveries
Declines in shopping trips
Increased online shopping
Seaman noted that while total walking trips declined, walking’s share of overall trips remained relatively stable.
Closing Remarks
Hong Yuan concluded the session by encouraging participants to explore the many mobility datasets discussed during the presentation.
She emphasized that NYC Open Data now hosts thousands of datasets and highlighted the importance of continuing cross-agency collaboration to improve mobility analysis and public understanding of transportation trends in New York City.
RESOURCES
Open Data About Mobility — the NYC Open Data Week session page for this MTA + NYC DOT joint presentation
MTA Bus Route Segment Speeds: Beginning 2025 — dataset of bus speeds between timepoints, highlighted by Lisa Mae Fiedler
It’s 2 a.m. Do you know where your bus is? — MTA blog post on the bus-matching algorithm, written by Gayan Seneviratna
Mapping Movement: Exploring NYC Bus Route Shapes — MTA blog post pairing bus geometries with segment speed data
MTA Subway Hourly Ridership: Beginning 2025 — hourly ridership by station complex and fare payment class, presented by Jaspreet Lal
MTA Congestion Relief Zone Vehicle Entries: Beginning 2025 — vehicle crossings into the CRZ in 10-minute intervals
The Most Detailed View of NYC Traffic (So Far) — MTA blog post explaining the Congestion Relief Zone entries dataset
MTA Bridges and Tunnels Hourly Crossings: Beginning 2019 — hourly crossings by facility, direction, and vehicle class, created by Niki Keramat
NYC DOT Citywide Mobility Survey — DOT’s household travel survey, presented by Mark Seaman; 2024 data newly released