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Crowdsourcing Course (Part 4 of 5):
Road Weather and Arterial Management
(August 15, 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, the Every Day Counts (EDC) logo, the Ohio DOT (ODOT) logo, the Maricopa Association of Governments logo, and/or the Florida DOT (FDOT) logo.

JPO Opening

Presenter: Charity Coleman, JPO

Slide 1: Talking Transportation Technology (T3) Webinars

Road Weather Management and Arterial Management: Part 4 of 5 in the Crowdsourcing for Operations Course via Webinar
Course developed by the Federal Highway Administration (FHWA) Every Day Counts (EDC)
Tuesday, August 15, 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: ITS 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: https://www.its.dot.gov/pcb/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: Road Weather Management and Arterial Management: Part 4 of 5 in the Crowdsourcing for Operations Course via Webinar

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

August 15, 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).

Except for the statutes and regulations cited, the contents of this document do not have the force and effect of law and are not meant to bind the States or the public in any way. This document is intended only to provide information regarding existing requirements under the law or agency policies.

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Road Weather Management and Arterial Management
Part 4 of 5 in the Crowdsourcing for Operations Course via Webinar

Host: James Colyar, FHWA EDC-6 Crowdsourcing Program Co-Lead, FHWA Office of Operations

Slide 7: Today’s Host and Presenters

James Colyar, Host
EDC-6 Crowdsourcing Co-lead
FHWA Office of Operations

Stephanie Marik
Transportation Systems Performance Engineer
Ohio Department of Transportation (DOT)

Dr. Wang Zhang
Transportation Data Program
Manager, Maricopa Association of Governments (MAG)

Jeremy Dilmore
Transportation Systems Management and Operation Engineer
Florida DOT

[This slide contains photos of James Colyar, Stephanie Marik, Dr. Wang Zhang, and Jeremy Dilmore.]

Slide 8: Webinar Agenda

• 1:05 p.m. FHWA EDC-6 Crowdsourcing Innovation and Course Background
• 1:15 p.m. Road Weather Management
• 1:35 p.m. Arterial Management
• 2:10 p.m. Question and Answer
• 2:30 p.m. Webinar Close

*EDT Time Zone

[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 innovation deployment model
• Proven but underutilized innovations
• 2-year cycles
• https://www.fhwa.dot.gov/innovation/everydaycounts/

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

• Adding data sources and applications
• Improving data management
• Improving archived data usage
• Sharing and integrating 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 systems management and operations (TSMO)
• 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 administrators
• Operations
• Maintenance
• Public works departments
• Emergency planning
• Work zone managers
• 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	Course Lessons and Modules
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 3 Modules

Traveler Information

• Crowdsourced data can deliver quantitative predictive travel times and offer greater details on issues affecting roadways.
• Crowdsourced data can improve traveler information timeliness.
• For traveler information, traffic incident management, and other TSMO strategies, crowdsourced data can expand geographic coverage and resolution.

Traffic Incident Management

• Crowdsourced data help detect incidents and queues quickly, reduce operator workload, and support after-action reviews.
• Crowdsource data improve responder and traveler safety.

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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Road Weather Management

Presenter: Stephanie Marik, Ohio DOT

Slide 17: Lesson: Road Weather Management

Instructor: Stephanie Marik, Ohio DOT

[This slide contains a photo of cars trying to go through a flooded two-lane roadway.]

Slide 18: Lesson Objectives

Describe how crowdsourcing data can improve key aspects of road weather management

[This slide contains a photo of a road disappearing into thick fog.]

Slide 19: Road Weather Management Challenges

• Timely and accurate road-specific weather data
• Understanding the safety and mobility impacts of weather
• Weather-responsive decisions and outcomes

“More timely, accurate and relevant information about weather-related impacts to the roads enables transportation managers and travelers to make more effective decisions.”
FHWA Office of Operations, Road Weather Management Program

Slide 20: Crowdsourcing Applications for Road Weather Management

• Expand weather-reporting geography and timeliness
• Reduce operator workload
• Facilitate real-time weather responsive strategies
• Facilitate post-weather response studies

[This slide contains a vehicles on a two-lane road following a snow plow that is throwing a large cloud of snow into the air.]

Slide 21: Road Weather Management Crowdsourcing Examples

https://www.fhwa.dot.gov/innovation/everydaycounts/edc_5/docs/crowdsourcing_applications.pdf

Slide 22: Example: Utah DOT (UDOT) Citizen Reporter Program

• Provided a consistent way for the public and DOT workers to report road weather
• Short training program promotes consistent reporting
• Reports improve web, 511, and UDOT Traffic app traveler information

[This slide contains two images: (1) a screenshot of the Report Form screen in the UDOT Citizen Reporter app and (2) an ad for the Utah DOT Citizen Reporter Program, which states “Are the roads slick? Let us know. Become a UDOT Citizen Reporter. Download the free UDOT Citizen Report app, available for Android or iPhone.”

Slide 23: Example: City of Frisco, Texas Crowdsources Road Weather Detection

• Developed a Waze^® interface
• Use public reports to respond to road weather events
• Post Waze^® events on Public Safety Computer Aided Dispatch Maps

[This slide contains a photo of a roadway with a patch of ice on it. The ice is circled in yellow and an arrow points to it from a smaller image of a Waze map of the roadway. The Waze map has a warning symbol at the location of the ice patch.]

Slide 24: Example: Maine DOT Crowdsources Road Weather Detection within Workforce

• App for road crews to report pavement, weather, and temperature (PWT) conditions
• Data automated into the State’s Traffic Management Center (TMC) software
• Saved TMC and Road Crew time, while standardizing reports and improving location accuracy

[This slide contains a multi-color RWT map of Maine. Each color reflects a different driving condition.]

Slide 25: TMSO

Ohio DOT Road Weather Reporting
Crowdsourcing for Operations Course, August 15, 2023

Stephanie Marik, P.E.
Transportation Systems Performance Engineer
stephanie.marik@dot.ohio.gov

Slide 26: Snow & Ice – Performance Evaluator (SNIPE)

Snow and Ice Spotters Program
During the 1997-98 snow season, ODOT began the use of the Snow and Ices Spotters program. Residents living in each of the counties throughout Ohio have been recruited as observers, with the task of noting how well ODOT snow crews clear the roads after a snow event.

[This slide contains a timeline showing the start of the program as 1997-98 through 2013-14 when RWIS and INDIX were incorporated.]

Slide 27: Snow & Ice – Performance Evaluator (SNIPE)

[This slide contains an RWIS map of Ohio and an INRIX map of Ohio.]

Slide 28: 1 – Event Begins

• ≥ 40% RWIS
  Detect Winter Precipitation
• ≥ 25% INRIX
  Routes with avg. speeds at least 10 mph below expected speeds

[This slide contains a map graphic representing the event begins step of a snow event with three blue triangles, two gray triangles, and light blue lines.]

Slide 29: 2 – Event Ends

• > 60% RWIS
  Stops snowing/freezing | Winds below matrix**
• No New Event
  begins within 2 hours

[This slide contains a map graphic representing the event ends step of a snow event with a blue triangle, four gray triangles, and light blue lines.]

Slide 30: Weather Matrices

*Winter Precipitation by Temp

Precip Type	Air Temperature
Snow or Freezing	≤ 37
Unknown Precipitation	≤ 34
Rain	≤ 32

** Wind Matrix for Drifting Snow

Wind Type	Speed (mph)	Air Temperature
Avg Sustained	≥ 12	≤ 20
Gusts	≥ 17	≤ 20
Avg Sustained	≥ 15	20 < T ≤ 34
Gusts	≥ 22	20 < T ≤ 34

Slide 31: 3 – Route Recovery

Average difference between expected and real-time speeds ≤ 10 mph

[This slide contains an INRIX map of Ohio and a map graphic representing the route recovery step of a snow event with five gray triangles and a green line.]

Slide 32: Route Level of Service

Recovery Goals

• Primary = 2 hours
• Secondary = 4 hours*

*Reduced service 11p-5a

[This slide contains a map of Ohio with primary and secondary recovery goals colored red and yellow, respectively.]

Slide 33: Snow & Ice – Reporting

[This slide contains four images: (1) an example of a recovery overview chart, showing event start time and date, event end time and date, recovery goal time and date, and recovery time and date (reproduced in the table below), (2) a button that says “Open Route Map,” and (3) a route recovery map with a road marked with a multi-color line across it, and (4) the route recovery map legend showing what each color in the line represents (time down in minutes).]

Recovery Overview for SCLESR00028**C-2
Event Start	Dec 26, 2022, 10:50:00 AM
Event End	Dec 26, 2022, 12:50:00 PM
Recovery Goal	Dec 26, 2022, 4:50:00 PM
Recovery Time	Dec 26, 2022, 7:45:00 PM

Slide 34: Real-Time Snow and Ice

How do we communicate snow event data in real-time to better help maintenance managers?

Problems:

• SNIPE looks backwards AND forwards in time
• Mapping segments in real-time is not performant

Solution:

• Show simplified versions of input data on a map using TSMO API & GeoEvent Server

[This slide contains three images: (1) a photo of a snowman wearing a hat with an ODOT logo and a short with an ODOT logo, (2) a hand holding a glass ball, and (3) a graphic of three devices (a tablet, computer, and cell phone) connected to a cloud labeled “API.”]

Slide 35: Real-Time Snow and Ice

[This slide contains a SNIPE screenshot that contains a map of northern Ohio, a legend, an overlay that says “Snow and Ice Priority Routes,” and a red circle around two blue dots representing snow/freezing rain conditions.]

Slide 36: Making the Business Case

Benefits of purchasing crowdsourced data

Use Case	Benefit	Details
Snow & Ice Performance Report	Time Savings	Through automation (APIs & Python Scripts) reduced report processing time and resources from 2-3 people for 3-4 days to a background process that runs for ~2 hours.
Real Time Snow & Ice	Resource Allocation	Allows managers to see in real time which routes are experiencing slowdowns according to the Snow & Ice Performance metric to adjust resources where needed.

Slide 37: TOAST

Traffic Operations Assessment Systems Tool

• Bottlenecks
• Travel Time
• TSMO Safety
• Traffic Incident Management
• Traffic Volume Data

[This slide contains a map of Ohio showing major roads, most of the them colored in blue, but some “hotspots” of traffic colored in red, orange, and yellow.]

Slide 38: Knowledge Check

How does crowdsourcing data improve key aspects of road weather management?

1. Expand weather-reporting geography and timeliness
2. Facilitate real-time weather responsive strategies
3. Facilitate post-weather response studies
4. All of the above

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

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Arterial Management

Presenters: Dr. Wang Zhang, MAG
Jeremy Dilmore, Florida DOT

Slide 39: Arterial Management

Presenters: Dr. Wang Zhang, MAG and Jeremy Dilmore, Florida DOT

[This slide contains a photo of a city street with an intersection and traffic lights in the foreground.]

Slide 40: Lesson Objective

Describe how crowdsourcing data can improve key aspects of arterial management.

[This slide contains a photo of motorcyclist on a city street.]

Slide 41: Arterial Management Challenges

• Knowing when traffic signal timing plans require updating
• Measuring impacts from traffic signal maintenance or new arterial infrastructure
• Adapting signal control to real-time traffic needs

“Advance the use of objectives and performance-based approaches to traffic signal management, to improve design, operations and maintenance practices, resulting in increased safety, mobility and efficiency for all users.”
Federal Highway Administration Office of Operations, Arterial Management Program

Slide 42: Crowdsourcing Applications for Arterial Management

• Performance-based rather than fixed calendar-based retiming.
• Continuous monitoring rather than sampling for performance.
• Measuring improvement effects.
• Proactive ​signal response.

[This slide contains a photo of two lanes of traffic approaching on a city street with sets of traffic lights from the foreground to the background.]

Slide 43: Crowdsourced Data Uses for Arterial Management

Dr. Wang Zhang, Transportation Data Program Manager, Maricopa Association of Governments (MAG)

[This slide contains a photo of Dr. Wang Zhang.]

Slide 44: New Mobility Data – Connected Vehicle (CV) Data

• From connected vehicle sensors
• Vehicle trajectory updated every 3-sec with high precision:
  • Reporting: GPS position, speed, heading direction
  • Derivatives:
    • Origin-Destination
    • Path choice
    • Acceleration/deceleration
    • Intersection measures such as control delay, arrival on green, and split failure
• Passenger cars only (Sedans, SUVs, Pickups), newer cars (2015 and later), from certain OEM
• Penetration rate varies by region
• Short-term future unclear

[This slide contains three images: (1) a graphic of Data and the Connected Car, (2) a map showing mobile device data in blue dots and connected vehicle data in yellow lines, and (3) a time-lapse video of a map with different color dots moving with the traffic.]

Slide 45: How MAG Uses Connected Vehicle (CV) Data in Arterial Management

• 60% of VMT in the region travels on arterial network
• Compared CV data application with floating car method in measuring arterial congestion
• Identified values in CV data to help monitor arterial traffic at intersection and corridor level
• Piloting INRIX signal analytics with MAG member agencies, monitoring intersection delay and optimizing traffic signal operation

[This slide contains two images: (1) a map of Arizona with yellow, orange, red, and green dots in different locations and (2) a map with a particular street highlighted. A yellow line points in one direction while a red line points in the other direction.]

Slide 46: Floating Car vs. Connected Vehicle Data

[This slide contains two images: (1) a time-lapse video showing floating car trajectory data on a map and (2) a time-lapse video showing connected vehicle trajectory data on a map.]

Slide 47: Intersection Analysis

Vehicle Movement Data

• 3-sec resolution, 24/7 coverage in the region
• High-resolution vehicle trajectory: speed, location, travel direction
• Sample rate: 4-6% of total traffic

Converting Data to Intersection Measurement

• Turning movement count (TMC) ratio
• Travel delay (control delay and stop delay) by turning movement
• Level of Service (LOS)
• Queue length, percent arrivals on green (POG)
• Intersection congestion profile by time of day and by date

Technical Advantage

• High consistency to data collected by traditional methods
• Broader spatial-temporal coverage
• Continuous monitoring
• Lower cost

[This slide contains five images: (1) a aerial view street photo marked with yellow, orange, red, and white dots, (2) a map showing TMC ratio, (3) a bar graph titled “Weekday Intersection Level of Service by Time of the Day Baseline Road and 48th street,” (4) a line graph titled “Wejo TMC Ratio v.s. 2019 Broadway Curve Data Collection TMC Ratio Based on 2019 Wejo and Broadway Curve Data,” and (5) a map showing delay (in seconds).]

Slide 48: INRIX Signal Analytics Pilot

[This slide contains three images: (1) a POG bar graph, (2) a Vehicle Count and Average Travel Time bar graph, and (3) five Vehicle Count INRIX vs. MAG scatter plots.]

Slide 49: How MAG Uses Connected Vehicle (CV) Data on other Applications

• Bottleneck study - queue, select link analysis
• Trend analysis
• Benchmark other mobility datasets
• Model calibration – Macroscopic/Microscopic
• Event data

[This slide contains four images: (1) a bar graph of Average Weekday Daily Wejo Journeys, (2) a line graph of 110 at 83rd Avenue Eastbound data, (3) a map with pink dots along a few of the roadways, and (4) a map with yellow and blue dots.]

Slide 50: MAG Embraces Connected Vehicle Data and Other Crowdsourcing Technologies

• Explore CV Data from other sources
• Improve data processing efficiency
• Truck GPS data/analytics from multiple sources
• Other pilot efforts under MAG emerging tech program
  • Virtual camera for inspection, pavement conditions
  • Lidar for roadway inventory
  • Tire pressure sensor on pavement conditions

[This slide contains four images: (1) a cell phone mounted on a car dashboard with an arrow pointing to two computer screens, (2) a car with a camera on its roof, (3) a map, a road from the perspective of a dashcam, and another roadway, and a close-up of a vehicle’s front passenger side corner. Next to the tire it reads “pothole protection.” Another view of just the tire on a dirt road says “friction level.”]

Slide 51: Contact

Wang Zhang, Ph.D., Transportation Data Program Manager
Maricopa Association of Governments (www.azmag.gov)
Phoenix, AZ
wzhang@azmag.gov

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Integrating Crowdsourced and Sensor-Based Data for Arterial Operations

Presenter: Jeremy Dilmore, FDOT

Slide 52: Integrating Crowdsourced and Sensor-Based Data for Arterial Operations

Crowdsourcing for Operations Course, August 15, 2023

Jeremy Dilmore, Transportation Systems Management and Operation Engineer, Florida DOT

[This slide contains a photo of Jeremy Dilmore.]

Slide 53: Arterial Data Sources

• Florida District 5 arterial roadways have 1600 signalized intersections
  • 900+ reporting ATSPM (2-minute frequency)
  • 200+ have CCTV reporting turning movement counts
• Crowd sourced data
  • GPS based subset of instrumented and reporting

[This slide contains a flowchart that starts with a traffic light and an IMC at the left. From the traffic light is a block that says ATSPM, then Approach Volume, and then Right Turn Table. From the IMC is Per Lane Distribution and then right turn table. The last step for both pathways is Turning Movement Output.]

Slide 54: Improving Arterial Awareness

• Fusing data provides coverage unobtainable with only instrumentation
• Fused data then used manage whole system
  • Crashes, event driven demand
  • Retiming based on need

[This slide contains three images: (1) a flowchart starting with Turning Movement Output and Crowd Source Data at the left. From Turning Movement Output is QC for Known Errors, then Spatial Model, and then Regional Growth Factors Determined and Applied. The Crowd Source Data path goes right to Regional Growth Factors Determined and Applied. Both paths end at Turning Movement, Queues, Delay, (2) a flowchart beginning at Turning Movement, Queues, Delay with three paths. One goes to Mesoscopic Model Real Time Evaluation Alternative Timings and then to Recommended Pattern. Another goes to Historic Demand, then Time of Day Grouping Corridor Based Grouping, then HCS Based Optimization, then Comparison to Existing Pattern, and ends at Prioritized Timing Development. The third path goes directly to Prioritized Timing Development, and (3) the Regional Integrated Corridor Management System (RICMS) logo.]

Slide 55: Improving Arterial Operations

Two Way Communication - Using applications to inform drivers during events such as rocket launches, Orlando venues, hurricanes, etc.

[This slide contains four images: (1) and (2) in-app wildfire warnings, (3) an event routing, and (4) a major delay warning.]

Slide 56: Arterial Management Crowdsourcing Examples

Agency	How Data is Used	Data Source
Austin, TX	Performance-based retiming	INRIX®
Louisville, KY	Performance-based retiming Measuring improvement effects	Waze®
Lake County, IL	Continuous monitoring Proactive response Performance-based retiming	Waze® and ATSPM
Washington, DC	Performance-based retiming	INRIX®, Waze®, and ITS sensor data

https://www.fhwa.dot.gov/innovation/everydaycounts/edc_5/docs/crowdsourcing_applications.pdf

Slide 57: Example: City of Austin Shifts to Performance-Based Corridor Retiming

• Previously retiming on rotating three-year schedule among ninety corridors.
• Historic vehicle probe data used to prioritize annual retiming of approximately 30 percent of city signals.
• Benefits of retiming shared with public.

• Travel Time Reduced: 12.1%
  FY2016 Goal: 5.0%
• Travel Time Reduced: 7.7%
  FY2017 Goal: 5.0%
• Travel Time Reduced: 2.1%
  FY2018 Goal: 5.0%
• Travel Time Reduced: 3.4%
  FY2019 Goal: 5.0%
• Travel Time Reduced: 5.0%
  FY2020 Goal: 5.0%

[This slide contains a map of Austin, Texas with red lines indicating probe vehicle data segments and black lines indicating city of Austin corridors.]

Slide 58: Example: Louisville Metro, Kentucky Crowdsources Signal Retiming Impacts

• Archive and analyze Waze^® jams data using PowerBI^®.
• Compares data before and after retiming rather than through a paid study.
• Also use data for hot-spot analysis and detecting faulty intersection equipment.

[This slide contains a Waze map of Louisville, Kentucky, showing visual and quantitative jams data.]

Slide 59: Example: Lake County Integrates Navigation Application Data for Signal Responsiveness

• From manual, infrequent to automated, continuous data collection
• Proactively implements alternate signal timing for crashes or adverse weather
• Significant savings on signal coordination and timing studies

[This slide contains two images: (1) a graphic of an arrow with three blocks of text. First is “1-2 days per year,” then “1-2 weeks per year,” and then “continuous” and (2) a chart of Travel Time, Delay, and Speed Data from Waze, Stops from Automated Traffic Signal Performance Measures.]

Slide 60: Example: Washington D.C. Uses Multiple Data for Corridor Retiming

• 600+ signal grid network with auto, bus, pedestrian, and bicycle considerations.
• Used vehicle probe data through RITIS, Google^® Traffic^®, Waze^®, floating car/GPS, bicycle travel time, and other data with a Synchro^® simulation model to retime network.
• Resulted in annual $2.4M mainline traffic delay savings, and annual $5.8M savings considering all traffic approaches.

[This slide contains a map showing the Washington, D.C. traffic signal network.]

Slide 61: Knowledge Check

How does crowdsourced data improve arterial management?

1. Detection of faulty traffic signals
2. Performance-based corridor retiming
3. Assess impact of signal retiming
4. All of the above

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

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Question, Answer, Discussion, and Wrapup

Host: James Colyar, FHWA EDC-6 Crowdsourcing Program Co-Lead, FHWA Office of Operations

Slide 62: 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 63: Road Weather Crowdsourcing Resources

• Adventures in Crowdsourcing webinars:
  • Social Media for Improved Operations
  • Engaging Navigation Providers
  • Using Crowdsourced Data for Traveler Information
  • Business Case for Crowdsourced Data

[This slide contains a a screenshot of the Crowdsourcing for Advancing Operation website.]

Slide 64: Arterial Crowdsourcing Resources

Adventures in Crowdsourcing webinars with arterial management content:

• Traffic Signal Applications
• Validating Crowdsourced Data

Eastern Transportation Coalition webinar:

• Using RITIS for Arterial Performance Measures (Briefing)

FHWA Arterial Management Program

[This slide contains a a screenshot of the Crowdsourcing for Advancing Operation website.]

Slide 65: Crowdsourcing Beyond Every Day Counts Round Six

• New website 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 the Crowdsourcing for Advanced Operations homepage.]

Slide 66: 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 67: Upcoming T3 Webinars

Webinar	Date	Time
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: https://www.its.dot.gov/pcb/t3_webinars.aspx

To access the recording and past T3 webinars, visit: https://www.its.dot.gov/pcb/t3_archives.aspx.

Slide 68: 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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