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Crowdsourcing Course (Part 2 of 5) Data Sources and Management
(June 20, 2023)

T3 webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation (USDOT)’s ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.

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Many of the slides in this presentation contain the U.S. DOT logo, the ITS PCB logo, the Federal Highway Administration (FHWA) logo, and/or the Every Day Counts (EDC) logo.

Crowdsourcing 101

Slide 1: Talking Transportation Technology (T3) Webinars

Data Sources and Management: Part 2 of 5 in the Crowdsourcing for Operations Course via Webinar
Course developed by the Federal Highway Administration (FHWA) Every Day Counts (EDC)
Crowdsourcing for Operations
Tuesday, June 20, 2023 - 1:00 P.M. ET

Slide 2: ITS PCB

Talking Transportation Technology (T3) Webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation’s (USDOT) ITS Joint Program Office (JPO).

For more information, visit: PCB Home.

[This slide contains a photo of a laptop with a translucent layer added over it: the word ITS centered within, and connected to, eight icons: a connected car, a connected bus, a traffic cone, a freight truck, a smartphone, two credit cards, and a bicycle.]

Slide 3: PDH Policy

• The T3 Webinar Program does not officially offer Professional Development Hours (PDHs); however, your participation in a T3 Webinar may qualify as PDH-eligible activity with your licensing agency.
• Upon request, the T3 Webinar Program can provide a letter verifying your attendance. Please contact T3@dot.gov to make a request.

For more information, please visit: /t3_pdh_policy.aspx.

Slide 4: Ask a Question/Make a Comment

Use the Chat Pod

• Click on Chat icon on your screen
• Submit your question or comments in the Chat window

Questions/comments will be addressed after the last presentation, as time permits

[This slide contains a screenshot of the bottom of a Zoom window with the Chat icon circled in red.]

Slide 5: Data Sources and Management: Part 2 of 5 in the Crowdsourcing for Operations Course via Webinar

Intelligent Transportation Systems Joint Program Office (ITS JPO) Professional Capacity Building (PCB) Program Presents

June 20, 2023

Course developed by the Federal Highway Administration (FHWA) Every Day Counts (EDC) Crowdsourcing for Operations Innovation, and delivered by the FHWA Office of Operations

Slide 6: DISCLAIMER

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this presentation only because they are considered essential to the objective of the presentation. They are included for informational purposes only and are not intended to reflect a preference, approval, or endorsement of any one product or entity.

This presentation was created and is being co-presented by both FHWA and outside parties. The views and opinions expressed during this presentation are the presenters’ and do not necessarily reflect those of FHWA or the U.S. Department of Transportation (USDOT).

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Every Day Counts

Host: Ralph Volpe, EDC-6 Crowdsourcing Colead, FHWA Resource Center Operations Technical Service Team

Slide 7: Today’s Host and Presenters

Greg Jones, Host
Every Day Counts 6 (EDC-6) Crowdsourcing Colead
FHWA Office of Operations and Resource Center

Alex Wassman, Instructor
Traffic Management and Operations Engineer
Missouri Department of Transportation (MoDOT)

Chris Lambert, Instructor
Systems Consultant
Kentucky Transportation Cabinet (KYTC)

[This slide contains photos of Greg Jones, Alex Wassman, and Chris Lambert.]

Slide 8: Webinar Agenda

• 1:05 p.m. FHWA EDC-6 Crowdsourcing Innovation and Course Background
• 1:10 p.m. Data Sources Lesson
• 1:40 p.m. Data Management Lesson
• 2:05 p.m. Question and Answer
• 2:30 p.m. Webinar Close

[This slide contains an image of a four-lane highway with stripes of light to represent fast moving vehicles.]

Slide 9: What Is Every Day Counts?

• State-based model
• Proven but underutilized innovations
• 2-year cycles

http://www.fhwa.dot.gov/innovation/everydaycounts/

Slide 10: EDC-6: Deeper Crowdsourcing Roots for a Bountiful Suite of Benefits

• Adding data sources and applications
• Improving data management
• Improving archived data usage
• Sharing and integration of data

[This slide contains a drawing of a fruit tree and its root system. The tree is bearing a lot of fruit.]

Slide 11: Crowdsourcing Course in a Box

Course Goals:

• Broaden understanding and knowledge about how crowdsourced data can improve transportation operations
• Help participants consider whether specific applications of crowdsourcing may meet their organizations’ needs

Course Tools:

• Editable instructor templates
• Instructor materials
• Course slide decks
• Student materials

[This slide contains an overhead photo of items on a desk: coffee in a coffee mug, a notepad, pens, a camera, two rubber stamps, twine, two packages secured with twine, and a hole punch.]

Slide 12: Whom Is the Course Targeting? Transportation Groups

• Traffic management centers (TMCs)
• Traffic signal systems
• Operations
• Maintenance
• Public works departments
• Emergency planning
• Work zone
• Safety and planning

Consider nontraditional invitees such as policymakers, local elected officials, administrators, or other leaders.

Slide 13: Course Is Modular by Design

• 5 Lessons: Introduction, Data Sources, Application Areas, Data Management, and Next Steps
• 6 Application Modules: traffic incident management, traveler information, arterial management, work zone management, road weather management, and emergency management

[This slide contains a graphic of one puzzle piece (“Introduction”) that connects to four other puzzle pieces: “Data Sources,” “Application Areas,” “Data Management,” and “Next Steps.”]

Slide 14: Crowdsourcing Course Delivery by Webinar

Webinar	Date	Time
1	May 16	Crowdsourcing Introduction and Application Lessons
2	June 20	Data Sources and Management
3	July 18	Traveler Information and Traffic Incident Management
4	August 15	Road Weather and Arterial Management
5	September 19	Emergency and Work Zone Management and Next Steps

Slide 15: Summary of Webinar 1 Lessons

Introduction

• Crowdsourced data help fill gaps in geographic coverage, improve information timeliness, and remove jurisdictional stovepipes.
• Crowdsourced data help agencies increase travel time reliability, improve safety, and save cost.

Application Areas

• One data source can benefit multiple transportation systems management and operations (TSMO) strategies.
• Real-time crowdsourced data can be archived for many more uses such as project prioritization or before and after studies.

Slide 16: Introductions

Please enter your name, agency, and job title in the chat window.

[This slide contains a reproduction of a “Hello my name is” sticker.]

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Data Sources

Host: Alex Wassman, Missouri Department of Transportation, Data Sources Instructor, Traffic Management and Operations Engineer

Many of the slides in this presentation include the Missouri DOT (MoDOT) logo.

Slide 17: Lesson: Data Sources

Instructor: Alex Wassman, Missouri DOT

[This slide contains a photo of a city skyline at night. A faint globe with a multitude of connected nodes has been drawn in the background behind the buildings.]

Slide 18: Lesson Objectives

1. Describe the different types of crowdsourced data
2. Understand differences between crowdsourced and traditional intelligent transportation systems (ITS) data

[This slide contains four circular photos: (1) a truck plowing snow from a highway, (2) an overhead view of a highway interchange, (3) a view, from the road, of overhead traffic lights with tall, snowy mountains in the background, and (4) a view through an intersection up a busy city street.]

Slide 19: Crowdsourced Data Characteristics

• Greater volume, velocity, and variety than traditional ITS Infrastructure
• No roadside ITS infrastructure such as loop detectors required
• Active or passive data collection
• Real-time or archived data

[This slide contains a photo of a hand holding a smartphone with the camera app open, capturing the trail ahead, which is covered with fallen yellow leaves and lined with trees with yellow leaves.]

Slide 20: Sources of Crowdsourced Data for Transportation Operations

• Vehicle probe
• Navigation app​
• Social media
• Connected vehicle
• 311 and 511 apps
• Multimodal probe data

[This slide contains a photo of 4 clear cylinders filled with jellybeans: caramel, pear, mixed, and tangerine.]

Slide 21: What is the typical frequency of real-time data from many common crowdsourced data providers?

1. Every second or two
2. ✓ Every minute or two
3. Every 10 or 20 minutes
4. Every hour or so

[This slide contains a photo of a pile of various analog clocks.]

Slide 22: 1. Vehicle Probe Data

• Source data
• Data providers
• Data elements
• Data frequency
• Common uses

[This slide contains a map with certain routes highlighted. To the right of the map is a list titled “Sample of vehicle probe data fields,” which includes segment ID, Date/Time, Direction, Length, Speed, Hist. Average, Free Flow, and Travel Time. An arrow points from this to a section on the map. Below the map and list is a key showing which colors indicate speed (in miles per hour). An arrow points from the key to a route highlighted in green. The key indicates green refers to speeds greater than 60 mph.]

Slide 23: 1. Vehicle Probe Data Characteristics

Characteristic	High-Level Descriptors
Data sources	Cellular triangulation, fleet and traveler Global Positioning System (GPS) devices or applications, connected vehicle data, and State or local ITS data.
Data providers	INRIX®, HERE®, TomTom®, Verizon®, and others.
Data elements	Speed and travel time by road segment, traffic event alerts, traffic tiles with color-coded speeds.
Data frequency	Typically, real-time data are transmitted every minute. Some vendors also offer archived data.
Common uses	Real-time traffic monitoring, real-time traveler information, archived data for performance, before and after studies, project prioritization, and planning for operations.

Slide 24: Missouri Crowdsourced Data Use Case #1: Rural Queue Warning System

[This slide contains two images: (1) a photo of a highway with accident near an exit closing one of two lanes causing traffic to backup and (2) a photo of a dynamic messaging sign displaying this message: Stopped Traffic Past Exit 161, 60-70 Min Delay.]

Slide 25: 2. Navigation App Data

• Source data
• Data providers
• Data elements
• Data frequency
• Common uses

[This slide contains a Waze app screenshot showing a map and a table of data about unusual traffic events in the area.]

Slide 26: 2. Navigation App Data Characteristics

Characteristic	High-Level Descriptors
Source data	User account data, traveler reports, mobile app GPS, historic data, State-provided road closure data.
Data providers	Waze®, Google Maps®, others.
Data elements	Waze provides jams (length, speed, delay), alerts (crashes, stalled vehicle, potholes, weather hazards), reliability and confidence scores.
Data frequency	Typically, real-time data are transmitted every 2 min. Some analytics vendor partners access 1-minute Waze® data.
Common uses	Real-time traffic monitoring, real-time traveler information, archived data for performance, before/after studies, traffic incident management, road weather management, work zone management, project prioritization, and operations planning.

Slide 27: Missouri Crowdsourced Data Use Case #2: Waze Events for Incident Identification

[This slide contains a map of the major roads in Kansas City, Missouri marked with icons: red and yellow circles containing an exclamation point and red circles containing a person holding a flag. To the left of the map is a key, which shows that the red circle with an exclamation point means that the road has been closed off due to an incident, the yellow circle with an exclamation point means that drivers can expect delays due to an incident, and the red circle with a person holding a flag indicates that the road has been closed due to a work zone.]

Slide 28: 3. Social Media

• Source data
• Data providers
• Data elements
• Data frequency
• Common uses

[This slide contains screenshots of two Twitter messages: (1) from a citizen: “ The arrow light to turn left onto railroad hwy from hwy 6 over by Bomgaars in Council Bluffs is not working. I sat through 4 rounds last Sunday and this Sunday. Could you make sure the correct authorities are aware? Thank you!” and (2) the response from Iowa DOT: “Thanks for the heads up. Traffic sgnals are maintained by the city. I will send them a note to ask them to take a look.”]

Slide 29: 3. Social Media Characteristics

Characteristic	High-Level Descriptors
Source data	Individuals actively sharing information. State and local agency communication to the public.
Data providers	Twitter®, NextDoor®, Facebook®, Instagram®, blogs, LinkedIn®.
Data elements	Photo, text narrative, video, links to relevant information.
Data frequency	Near realtime.
Common uses	Planned special events, traveler information, public sentiment analysis.

Slide 30: Missouri Crowdsourced Data Use Case #3: Social Media for Maintenance

[This slide contains two images: (1) a screenshot of a citizen tweet that tagged Missouri DOT: “sewage drain not doing its job page blvd (Hwy D) & Cora Ave as well as page and Marcus Ave,” including a photo of a flooded street and (2) Missouri DOT’s response tweet, which tags MSDProject Clear: “would you be so kind as to take a look at these spots?”]

Slide 31: 4. Connected Vehicle Data

• Source data
• Data providers
• Data elements
• Data frequency
• Common uses

[This slide contains a graphic of a two-way highway with connected vehicles on both sides. One side has free-flowing traffic and the other side has backed-up traffic because two of the three lanes are closed for work crews and only the center lane is open to traffic.]

Slide 32: 4. Connected Vehicle Data Characteristics

Characteristic	High-Level Descriptors
Source data	Onboard vehicle system among various major vehicle manufacturers.
Data providers	Wejo®, Ford Safety Insights™, Replica®.
Data elements	Varies. Some currently offered data include “breadcrumb” location, hard braking, windshield wiper status, speed, and aggregate analytics.
Data frequency	University research and private entities mainly exploring archived or near-real-time use—either semidaily, daily, weekly, or monthly content.
Common uses	Safety studies, transportation planning, before and after studies.

Slide 33: 5. 311 and 511 Apps

• Source data
• Data providers
• Data elements
• Data frequency
• Common uses

[This slide contains two images: a screenshot of a “USDOT Citizen Report” page in a Utah DOT app and (2) a graphic of a smartphone displaying the Delaware DOT app home screen.]

Slide 34: 5. Mobile/Web 311 and 511 App Characteristics

Characteristic	High-Level Descriptors
Source data	Typically, active reports by public.
Data providers	CivicPlus/SeeClickFix®, PublicStuff®, CitySourced®, and FixMyStreet™ provide 311 service. Agency-specific providers also support 511 applications.
Data elements	Transportation-focused report types include crashes, abandoned vehicles, infrastructure repair needs (pothole, traffic signal, sign), and object on road.
Data frequency	A few reports per week to dozens or more per day.
Common uses	Traveler information, road weather management, maintenance of roads and signals.

Slide 35: 6. Multimodal Data

• Source data
• Data providers
• Data elements
• Data frequency
• Common uses

[This slide contains two images: (1) photo of an e-bike against a wall and (2) a photo of an e-scooter on a sidewalk.]

Slide 36: 6. Multimodal Data Characteristics

Characteristic	High-Level Descriptors
Source data	Active reports by public, passively collected by mobile app (e.g., bicycle, commercial vehicle, or transit app), smartphone, or device (e.g., dockless bicycle or scooters, or a dashboard camera).
Data providers	Examples: StreetLight Data™ and Nexar®
Data elements	Ridership, road user location and frequency, trip/route, financial statistics.
Data frequency	Varies by source.
Common uses	Long-term planning studies, micromobility policy development, alternate route planning, multimodal demand modeling.

Slide 37: Understand the Data Before Using It!

• Accuracy depends on “market penetration” of data generators.
• Data quality needs differ based on intended use.
• Some data are a “Black Box” and requires routine validation.
• Crowdsourced data do not measure traffic volumes.

Slide 38: Crowdsourced Data Cost

• Vehicle probe and connected vehicle data costs vary by coverage area, vendor, and data-sharing flexibility.
• Some navigation app data is free through an agreement (e.g., Waze^® for Cities).
• Social media data can be monitored, but posting information comes with a cost.
• All data, including crowdsourced data, require investment of staff time and data management resources.
• Most data providers remove personally identifiable information prior to delivering data.

[This slide contains a photo of a taximeter on the dashboard of car.]

Slide 39: Knowledge Check No. 1

Which of the following data are considered crowdsourced data?

• Vehicle probe data
• Loop detector data
• Navigation app data
• Traffic signal data
• ✓ A and C

[This slide contains a photo of a small pile of books on a desk with the top book opened.]

Slide 40: Knowledge Check No. 2

Which of the following data is not readily available through crowdsourcing?

• Speed data
• Traffic incident data
• Path or route data
• ✓ Traffic volume data

[This slide contains a photo of a small pile of books on a desk with the top book opened.]

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Data Management

Presenter: Chris Lambert, Kentucky Transportation Cabinet (KYTC), Data Management Instructor

Slide 41: Lesson: Data Management

Instructor: Chris Lambert, KYTC

[This slide contains a close-up photo of components on a circuit board.]

Slide 42: Lesson Objectives

1. Understand the importance of data management for crowdsourced and broader agency data.
2. Identify how modern data management differs from traditional data management.

[This slide contains four circular photos: (1) a truck plowing snow from a highway, (2) an overhead view of a highway interchange, (3) a view, from the road, of overhead traffic lights with tall, snowy mountains in the background, and (4) a view through an intersection up a busy city street.]

Slide 43: Data Management Challenges

• Internal resistance to data sharing
• Storage and processing policies
• Software policies
• Technical know how
• Resource constraints
• Demonstrate value

In 2020 the California Utility Agency, which collects ride-hailing data, reversed a 7-year policy that precluded sharing it with transportation agencies.
https://www.sfcityattorney.org/2020/03/13/cpuc-follows-san-franciscos-urging-to-make-uber-lyft-data-public/

Slide 44: “Big” Crowdsourced and Other Data Needs for Data Management

• Data volume, velocity, variety, and granularity are unprecedented.
• Ways agencies traditionally manage data will not work or not work well.
• Modern data management approaches offer greater data functionality and value.

[This slide contains a photo of a servers in a server room]

Slide 45: Data Management Systems Are Evolving

Traditional Systems		Modern Systems
Well-defined, fixed, purposed	← →	Flexible, self-adjusting
Centralized storage and processing	← →	Distributed storage and processing
Coupled hardware and software	← →	Decoupled hardware and software
Centralized data governance	← →	Distributed data governance
Few access and use data	← →	Many access and use data
Extract transform load (ETL)	← →	Extract load transform (ELT)

Slide 46: Data Governance Evolution

• Governance is part of the overall management of data.
• Governance addresses topics of data ownership, processing, access, security, and other policy-setting efforts.
• Decentralized, coordinated governance offers flexibility and accountability needed for “big” crowdsourced data.

[This slide contains a photo of three people in yellow safety vests and hard hats standing at a construction site, looking at a large plan together.]

Slide 47: Example: How do Agencies Manage Data from a Free Navigation Application?

• Stored
  In-house Hadoop^™ or Cloud, structured query language (SQL) or another database, third-party stored, or not stored
• Filtered
  Filtering for crashes, by reliability score, by number of reports
• Validated
  Mostly qualitative; some quantitative validation
• Challenges
  Duplicate reports, integration with advanced traffic management system (ATMS) and 511 platforms, real-time analytics, long-term storage, attribution

Slide 48: Trends in Data Management

• Data aggregation and storage
• Quality controls
• Data reproducibility
• Outsourcing
• Customization
• Third-party data and tools use

[This slide contains a photo of a country road lined by trees with leaves that have turned yellow.]

Slide 49: Data Management at KYTC ITS Team

Presentation by Chris Lambert
Webinar | June 20, 2023

[This slide contains the TEAM Kentucky Transportation Cabinet logo.]

Slide 50: Vision/Mission

Vision
Striving to be national leaders in transportation who provide transportation infrastructure and services for the 21^st century that deliver new economic opportunities for all Kentuckians.

Mission
To provide a safe, efficient, environmentally sound and fiscally responsible transportation system that delivers economic opportunity and enhances the quality of life in Kentucky.

[This slide contains the TEAM Kentucky Transportation Cabinet logo.]

Slide 51: General Purpose

[This slide contains a circular flowchart of four rectangles, clockwise from the top: “Monitor,” “Alert,” “Communicate,” and “Respond.”]

Slide 52: Data Sources

• HERE Traffic Speeds
• HERE Incidents
• Waze Jams
• Waze Traffic Viewer
• Waze Incidents
• iCone Traffic Speeds
• Twitter
• KYMesonet
• CoCoRahs
• NWS Doppler Radar
• NWS Forecasts
• Statewide TMC Reports
• Metro TMC Reports
• Snowplows
• Roadway Weather Stations
• County SNIC Activity Reports
• Dynamic Message Signs
• Truck Parking
• Permitted Work Zones
• AASHTOWare SiteManager

Slide 53: Raw Data

Stored as Native File Format: JSON, XML, CSV, DBF, etc.

Raw Data Folder Structure: ../type/provider/version/year/month/day

• Type of data
• Provider
• Version of API
• Year
• Month
• Day

[This slide contains a screenshot of the contents of the raw data folder.]

Slide 54: Processed Data

[This slide contains three flowcharts. Their details are reproduced below.]

Geo Source Geo KYTC Point KYTC Line

↓

Time Source Date / Time Capture Date / Time

↓

KYTC County Route Mile Point

↓

Source Report Type Metrics

Date & Time → District 7 → Fayette → I-75 → MP 115 → Crash → 10 Thumbsup

Date & Time → District 7 → Fayette → I-75 → MP 115 → Speed → 25 MPH

Slide 55: On-Premise Architecture

Advantages:

• Free compute*
• Fixed cost
• Works with traditional procurement processes

Disadvantages:

• High upfront cost of servers
• Replacement of servers
• Maintenance of server OS, etc.
• More technical for end-users
• Limited visualization tools
• Limited sharing behind firewall
• Difficulty scaling

Slide 56: Cloud Architecture

Advantages:

• Inexpensive storage
• More scalable
• No maintenance
• More end-user friendly
• More visualization options
• More flexibility for sharing

Disadvantages:

• Expensive compute
• Variability in cost, required different procurement process
• Keep an eye on changes in service provider terms

Slide 57: Cloud Costs Depend on Use Case

• KYTC ITS, Real-Time Data: ~$20k/month
  • 110TB of Raw and Processed Data
  • Storage (data lake, cold storage) = 6%
  • Pub/Sub (messaging middleware) = 40%
  • SQL (storage + usage) = 48%
• KYTC Photolog / LiDAR: ~$46k/month
  • 100TB NetApp, Hot Storage = 41%
  • 300TB Cloud Storage = 40%
  • Windows VMs = 14%
  • SQL = 1%

Slide 58: Visualization and Analytics

Our Current Tool Set:

• Kibana & Elasticsearch by Elastic.co, “elegant and lightning fast”
• PowerBI by Microsoft, “user friendly”
• Looker Studio by Google, “free, web-based”
• UrbanSDK by UrbanSDK, “data science and analytics”

Slide 59: Combine People & Data

[This slide contains a flowchart. On the left is a column of rectangles: Executive Leadership, Public Affairs, District Personnel, County Personnel, Emergency Management, Statewide Operations Center, and Traffic Management Center. Arrows point from this column to the right (and back from) an End-User Experience block containing a Microsoft Teams logo and a column of rectangles: GoKY Public, Looker Studio: Decision Support, SNIC Dashboard: Decision Support, Twitter: Automated/Conditional, and GovDelivery: Automated/Conditional. On the far right is a block (Data Sources: ~166 million records per day) containing boxes: County Status, CoCoRahs, DMS Messages, GeoTab AVL, HERE, KYMesonet, NWS Forecasts, Doppler, RWIS, Traffic Cameras, TOC, TRIMARC, Truck Parking, Twitter, and Waze. This block has an arrow pointing left to a block containing Google Cloud Platform information: Data Lake (raw data) and Real-time Processing (~166mil records per day, 110TB in BigQuery, and 2TB per Month). This block has an arrow pointing left to each of the rectangles in the End-User Experience block.

Slide 60: General Use Cases

[This slide contains two images: (1) a graphic with a circle (DATA) surrounded by a ring of circles: Monitoring, Detection, Alerts, Analysis, Summaries, Ad Hoc Analytics, and After Action and (2) the Team Kentucky Transportation Cabinet logo.]

Slide 61: Monitoring

[This slide contains a screenshot of the externally facing KYTC 511 system dashboard. There are three elements to the Dashboard: (1) A map displaying a highway with a traffic cone icon marker signifying a KYTC Reported Work Zone. From this icon is a pop-up window with details about this work zone: the location county, date of first report, a description, the road name, the route name, the mile point, and the source type, (2) an Information Summary column showing traffic, incident, and work zone information and includes selectors that allow the user to customize the information displayed on the map and in the column, and (3) a map insert showing a similar messaging system, but of a snow and ice report instead of a work zone report.]

Slide 62: Detection

[This slide contains two images: (1) a diagram of a real-time incident detection event: a line represents a one-half mile road segment. One spot on the segment has two boxes connected to it: a Waze logo labeled “Waze ‘Accident’ with ‘Thumbs Up’ ≥ 1.” and “Traffic Speeds ≤ 70% of free-flow.” and (2) a dashed line labeled “Clustering: Single Incident.” Three Waze logos are on the dashed line, and the line is marked with 1.0 mp at the left end, 1.5 mp at the center, and 2.0 mp at the right end.]

Slide 63: Alerts

[This slide contains two screenshots of KYTC alerts. The details of each alert are reproduced below.]

Crash Reported in Work Zone
[ITS] I-265 in Jefferson County Work Zone Incident Report

Please check here (linked) for the most up-to-date information.
The data contained in this alert is current at the time of sending and may be subject to change.

[ITS] Work Zone Interstate Speed Summary
Work Zone averages 25mph or less for 10min or more

Please check here (linked) for the most up-to-date information.
The data contained in this alert is current at the time of sending and may be subject to change.

Slide 64: Analysis

[This slide contains a KYTC Work Zone Monitoring Summary screenshot from the KYTC Dashboard. A left column allows the user to select Active Work Zones, Work Zone Overview, Crash Summaries, Mobility Summaries, Monitoring, Links and Alerts, or Download. Monitoring is selected in this screenshot. To the right of this column is a Work Zone Monitoring map, which shows a road section marked with colored circles indicating the average speed. It also includes the following information: dates, county, road name, average speeds each day. Below the map are two line graphs: (1) current average MPH versus free flow MPH for a 24-hour period and (2) the average speen mean.]

Slide 65: Summaries

[This slide contains a screenshot of a KYTC dashboard displaying ITS: Real-Time Information. The dashboard displays a map marked with colored circles to indicate Traffic, Hazard, Work Zone, Weather, and Crash locations. Below the map is a table of details for the locations with circles.]

Slide 66: Ad Hoc Analysis

[This slide contains a screenshot of a KYTC dashboard displaying an Ad Hoc Analysis. There is a map in the top left showing traffic impact of an incident. Below the map is a chart indicating impact per 10 min. To the right of the map are three line graphs, indicating impact on interstate routes, US routes, and Kentucky routes. To the right of the graphs are two speed gauges.]

Slide 67: After Action

[This slide contains a screenshot of a KYTC dashboard displaying an After Action Report. This dashboard includes a radar map and a line and bar graph.]

Slide 68: Knowledge Check No. 1

Why is data management important for crowdsourced and broader agency data?

1. Keeps data organized and usable
2. Keeps data safe and accessible
3. Keeps data indefinitely
4. ✓ A and B

[This slide contains a photo of a small pile of books on a desk with the top book opened.]

Slide 69: Knowledge Check No. 2

Which of the following are characteristic of modern data management?

1. Distributed storage and processing
2. Decoupled hardware/software
3. Many access and use the data
4. ✓ All of the above

[This slide contains a photo of a small pile of books on a desk with the top book opened.]

Slide 70: What is Your Agency’s State of Practice Regarding Modern Data Management?

1. nonexistent
2. exploration
3. some demonstrations
4. practiced by some groups
5. institutionalized (or nearly institutionalized)
6. Not sure

Slide 71: Questions?

Team Kentucky Transportation Cabinet
Twitter: @KYTC
Facebook: @kytc120
Instagram: @KYtransportation
YouTube: @KYtransportation

Chris.Lambert@ky.gov
@ChrisLambertKY

[This slide contains the Team Kentucky Transportation Cabinet logo.]

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Q&A Discussion

Host: Ralph Volpe, EDC-6 Crowdsourcing Colead, FHWA Resource Center Operations Technical Service Team

Slide 72: Question, Answer, and Discussion

[This slide contains a photo of a person presenting to five other people with empty, multicolored translucent thought bubbles overlaid over the photo. The thought bubbles represent multiple viewpoints being shared.]

Slide 73: Crowdsourced Data Resources

• Adventures in Crowdsourcing webinar on Validating Crowdsourced Data
• Lake County Free Travel Time Poller: Free GitHub Code for Waze^® Connected Cities partners to send email alerts for defined road segments that become ”congested.”
• The Eastern Transportation Coalition Coalition Vehicle Probe Project: INRIX^®, HERE^®, and TomTom^® resource guides and validation reports.

[This slide contains a screenshot of the Crowdsourcing for Advancing Operation resource website: https://www.fhwa.dot.gov/innovation/everydaycounts/edc_6/crowdsourcing.cfm]

Slide 74: Data Management Resources

• Adventures in Crowdsourcing webinars with data management content:
  • Data Management and Governance
  • New Destinations with FHWA EDC-6 Innovation: Modern Data Management
• National Cooperative Highway Research Program Report 952 Guidebook for Managing Data from Emerging Technologies for Transportation
• FHWA Data Business Plan Report

[This slide contains a screenshot of the Crowdsourcing for Advancing Operation resource website: https://www.fhwa.dot.gov/innovation/everydaycounts/edc_6/crowdsourcing.cfm]

Slide 75: Crowdsourcing Beyond Round Six of the Every Day Counts (EDC) Program

• New web presence
• Continue course delivery
• Continue technical support
• Continue free access to the EDC-6 Adventures in Crowdsourcing webinar series hosted by the National Operations Center of Excellence

[This slide contains a screenshot of a concept website (Crowdsourcing for Advancing Operations) that is on development and intended for FHWA Office of Operations.]

Slide 76: Thank you

James Colyar, james.colyar@dot.gov, 360‒753‒9408

Greg Jones, gregm.jones@dot.gov, 404‒895‒6220

Ralph Volpe, ralph.volpe@dot.gov, 404‒985‒1268

Slide 77: Upcoming T3 Webinars

Webinar	Date	Time
Crowdsourcing for Advancing Operations: Traveler Information and Traffic Incident Management	Tuesday, July 18, 2023	1:00 P.M. - 2:30 P.M. ET
Crowdsourcing for Advancing Operations: Road Weather and Arterial Management	Tuesday, August 15, 2023	1:00 P.M. - 2:30 P.M. ET
Crowdsourcing for Advancing Operations: Emergency and Work Zone Management and Next Steps	Tuesday, September 19, 2023	1:00 P.M. - 2:30 P.M. ET

Register: /t3_webinars.aspx

To access the recording and past T3 webinars, visit: /t3_archives.aspx

Slide 78: Feedback

• A link to a feedback questionnaire is provided in the chat pod. Please take a few minutes to fill it out – we value your input.
• To receive notifications of upcoming events, send an email to T3@dot.gov with “Add to mailing list” in the subject line.

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