Weather & the connected vehicle 092811

524 views
459 views

Published on


Intelligent Transportation Society of Connecticut Sept. 28, 2011 - Weather Technologies - Clarus and Connected Vehicles - Ray Murphy

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
524
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
14
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • The Federal Highway Administration’s (FHWA) Road Weather Management Program (RWMP) continues to support Intelligent Transportation Systems (ITS) applications that focus on roadway safety and mobility and at the same time promote technology deployments that help balance society’s need to protect the environment and maintain stable economic conditions. The program collaborates with NOAA, departments of transportation in each state, an important and vigorous private sector, the academic community, and professional societies such as the American Meteorological Society and other nongovernmental organizations, including the American Association of State Highways and Transportation Officials. This presentation will provide an overview of the Clarus system, including a review of recent enhancements, and an update on mobile observing of weather and road conditions – including the Connected Vehicle Technology Challenge and the VDT (Vehicle Data Translator).
  • - Customers: who uses the data, how they use it, how they get it, how often they use it, etc- 28 sample size is not very big; doesn’t meet statisticalminimum of 30 especially for such a diverse community
  • Respondents were asked what is their primary means of accessing the data. 48% of respondents use the map service.
  • - Labeled new data “preferences” because survey question did not ask for “needs” per se,but “what data sources would be useful to their organizations”.- Note each response is independent from each other so %’s do not equal 100%.- Mobile data high rating because potential of mobile data getting a lot of “marketing” lately with smart phones, CV program, etc.
  • - Org impact may be the result of some DOTs just deciding to be wx-wise or not.“Clear primary” means the majority or top results had significant margins from results at next level i.e. large difference in % from #1 and #2.
  • Focus of this brief is last two bullets. ITS/JPO Open source guidelines are in draft and should be out mid-August.3 phases – utilize the Clarus system and evaluate2nd bullet Mission tag – ease of integration3rd – main emphasis – FHWA is seeding application development – build a software tool… further Phase 1: Request for Applications (RFA)Team approach/public transportation agenciesCreation of ConOps tailored to agencies’ needsRequires providing ESS data into the Clarus SystemPhase 2: Connection Incentive Program (CIP)Expand public agency participation in ClarusOffset some costs for metadata & data accessPhase 3: Development & DeploymentPrivate sector to build & test ConOps scenariosIndependent evaluation of the Clarus System & developed innovationsGovernment owed off the shelf but slide 3 is mostly bolder plate, this language has been around for years because the CRD isn’t going on that long (yeh-Ray) , really I think those 3 bullets talk to the 3 phases somewhat you know there was (Ray “ I have you on speaker so I can tape”) (ok) but I took out some of phase terminology because that’s all project management, but a big part of the Clarus recent demo was to actually utilize the Clarus System and then even evaluate it, so you might have heard or know about the fact that Merridian as well as the subcontractor on Nixon Hill they all did a system eval (evaluation) that’s that 1st bullet, you don’t need to spend a lot of time on it (got you), it’s all system data base focus, the 2nd one is kind of a general statement that’s almost the mission tag for the road weather program, because we are to obviously intergrade weather aspect into various transportation areas the easier make the data the easier you make the software tools available the easier it is to intergrade it rights so that’s what that 2nd bullet is. (ok), but the 3rd bullet is really were the Clarus regional demo I think it’s main emphasis were just like in the assess the government is seeding the application development and with the idea that it’s providing a base line software capability and the relationship with the state, so they get in bed with the state they learn the requirements, they learn the operation of the state and then they build the software tool 1st generation, 1st version and then hopefully the idea is that that seed further development, further integration of weather services so this CRD brief if focus on that 3rd bullet because we are going to talk about use case (ok) and then I just throw in on the bottom line the fact that the contract does spell out that all the software is government owned and then I’ll actually have the westside at the end of (Is that coss or goos – it’s government owned off the shelve; ok I am not familiar with that term) it the same coss right but its government owned software in this case ok, so that actually in contrast to the baa software development which is not government owned so all those little software enhancement that the various folks are doing out there that’s not federal highway owned software ok, but in the case of Clarus it is, so just mention that it goos (it’s government owned) and it will be opened source
  • WRF (Weather Research and Forecasting) Model: a standard model used by NWS, academia and private weather service providers.Both teams ran their own WRF but it’s basically the same modelAssess the impact of Clarus data on weather and pavement predictions utilizing existing weather models to enhance atmospheric and pavement forecasting for surface transportation.- Typical weather model (like the WRF) ingest weather data from well over 10,000 observation sites throughout the atmosphere each hour. Total surface observation site density is about 3,000. NCAR varied the surface obs sites with 160 ESS points and Meridian varied those by ~400 or 2-4% of total model input (observed weather data)Advanced Research WRF (ARW):Meridian used a 3 1/3 kilometer grid spacing; MHI-NCAR 5 km grid spacingAccounts better for fine-scale terrain induced weather variationsImproves resolution of banded precipitationImproves the resolving of clouds and radiationProvides hourly output to support: Forecaster editing & validation with hourly observationsSeveral sets of …so on slide 5 and again you have probably have seen the objective language in these charts is ______ what the test or the studies did was they use various types of models, weather models, the road weather forecast system is basicly the guts of the NDSS if you didn’t know that’s their diecast system which uses a multi set weather model and then the road model is the metro model which again is also part of the NDSS and then Merridian use the precipitation analysis and blowing snow model to take, so what they did is they took worf weather output and shoved it into their sorry about that they use Clarus data to help analyze the precipitation field so they combine Clarus data with radar and satellite imagery to come up with an enhancer precipitation analysis and through that out put into a blowing snow model , so again focusing on the 2nd bullet both the Merridian and Incar basically just ran their models they didn’t develop any models they just took their existing models and said hey let’s see what our performance with Clarus data and without and that what the study was focus on ok, but I thought I would throw in the last bullet on the slide 5 to give you an idea of the scope the size of the Clarus data so people might think ooh you know you’re adding up all this Clarus data to the model s and really you are not you’re only adding to the 4% of the total observed weather data that‘s in the worf for example there is tons of other observation sources right like I was saying radar, satellite other observation site etc. there is a little background on the worf in the note section ( Ray, I am reading through it now) you don’t have to get into all those details that more in case you get some smart people and they ask questions – (Ray-exactly) there is some example – Merridian use the version of worf called vanceresearch it just has couple more bells and whistles and as you can see by the 1st bullet people might have a question about what’s the resolution what’s the spacing of this model and the grid point and I got that info in there so its 3 1/3 columeters for Merridianworf and 5 columeters for Incarworf, so hit the highlights of basically the bottom line is that you have several set of models and road weather related models and there is including Clarus and excluding Clarus data and seeing what the difference is. Chart 6 just depict the domain or the model area Nixion Hill and Incarr is you have to decide if you want to reference Incar or Nixon Hill you understand the relation there right (Ray – they were collaborating working together on use case 1) well Incarr did the vast majority of the work so Nixon Hill was the prime for use cases 1,3,4 like chart 4 says and they don’t have any meteorologist or weather expertise in Nixon Hill so they just sub that out to Incar I use Nixon Hill in the chart language you might end up like me going back and forth so just be aware of that I kind of generalize using Nixon Hill language so I just quickly show the map to give people a kind of zoom in version you might not even need to show chart 6 because the are information is really the same as chart 4 it show the upper Midwest for both area and then the thing that I do bring under the Nixon Hill 1st bullet there is that they use 5 case studies from 08 to 09 on particular weather events and those I have listed there the heavy rain, high winds scrawl lines cold and warm events you know what a scrawl line is? No – scrawl line is a line of thunder storms, a very strong cold front and a lot of thunder storms are brewed from it you said if I had to cut down t you indicated the chart 4
  • Year round maint tool – familiar with MDSS – expanding to year round activities – the most difficult part…Paint analogy….
  • Provide a coordination tool for events that will affect multiple – i.e. chemical spillPretty aggressive tool – pretty lofty – to do a inter-state - table top exercise DOTs (State & Local)Law EnforcementEmergency ManagementMulti-StateMulti-JurisdictionalPrimary objectives: Provide road conditions and strategies which improve coordination between agencies for the imposition of controls and dissemination of advisories Assist agencies in proactively responding to situations & thus mitigate the impact to travelers and 1st respondersTool helps to coordinate among agencies, define the weather threat, establish control actions, and monitors eventsDefinition of weather events similar to use case 3 methodologyMultiple web conferences can be set-up and monitored
  • Forecasted weather info – the predicatedDid not go public wide – due to testingDid not put out predicted treatment…
  • There may be more value in multi-state and forecast information via a web portal - “a picture is worth a thousand words”With current data availability, the system holds promise for alerting travelers to imminent travel problemsThe concepts have been well-received by both the participating DOTs and public evaluatorsThe system would potentially benefit from more detailed agency data (e.g., IT-equipped snowplows)
  • IntelliDrive applications are grouped into the three main categories safety, mobility, and environmental. Example applications for each are shown. Discussion of what IntelliDrive
  • The Clarus Initiative, established in 2004, is a multi-year program to organize and make available more effective environmental and road condition observation capabilities in support of four primary motivations: Provide a North American resource to collect, quality check, and make available surface transportation weather and road condition observations. Surface transportation-based weather observations will enhance and extend the existing weather data sources that will support general purpose weather forecasting. Collection of real-time surface transportation-based weather observations will support real-time operational responses to weather. Combining Clarus data with existing observation data will permit broader support for the enhancement and creation of models to enhance forecasts in the atmospheric boundary layer and near the earth’s surface.
  • Clarus is one of the “ancillary” data source feeding into Stage II of the process; Clarus and other data are used to perform quality checks on the mobile data and possibly support/enhance the mobile data used to make the inferences/roadway hazard assessments in Stage III
  • Use in combination with the Maintenance decision support systemResubmitted to Paul Pisano of FHWA for “weather” portion of Connected Vehicle programMDSS maintenance decision support system
  • Derive data and communicationsrequirements for weather, road condition, and vehicle status variables from mobile platforms (Using State DOT vehicles as the source)Enhance and expand post-processing algorithms to turn the data into useful observations that are tied to existing mesonets (e.g., Clarus)Explore the use of these and other observations in weather-related decision support systems.Clarus will be transitioning over the next few years to the NWS mesonet as part of a “National Next Gen Network”. Schedule is TBDOther observations may include input from other mesonets and networks or State DOT information
  • Weather & the connected vehicle 092811

    1. 1. Intelligent Transportation Society of Connecticut <br />14th Annual Meeting September 28, 2011<br />
    2. 2. Intelligent Transportation Society of Connecticut <br />14th Annual Meeting September 28, 2011<br />Ray Murphy<br /><ul><li>FHWA +10 yrs - program support:
    3. 3. Road Weather Management
    4. 4. Emergency Transportation Operations
    5. 5. Real – Time Data Management
    6. 6. + 20 yrs Illinois Dept. of Transportation:
    7. 7. Operations, Maintenance, & Construction
    8. 8. ITS Project Manager
    9. 9. CEC Officer/Seabees & Engineer Mentor</li></li></ul><li>Weather Technologies…<br />Clarus and the Connected Vehicle<br />Ray Murphy<br />Federal Highway Administration<br />September 28, 2011 Intelligent Transportation Society of Connecticut <br />
    10. 10. Road Weather Information System (RWIS)<br />Environmental Sensor Station (ESS)<br />Existing RWIS installations:<br />East Hartford<br />Middletown<br />Groton<br />New London<br />Lebanon<br />Sikorsky Bridge<br />New Haven<br />Stamford<br />North Canaan<br />Torrington<br />Avon Mountain<br />Seymour<br />Danbury<br />
    11. 11. 5<br />
    12. 12. Environmental Sensor Stations<br />2,253 Sensor Stations (ESS) 52,471 Individual Sensors<br />6<br />
    13. 13. The Clarus Initiative<br /><ul><li>Clarus is an R&D initiative to demonstrate and evaluate the value of “Anytime, Anywhere Road Weather Information” that is provided by both public agencies and the private weather enterprise to transportation users and operators.
    14. 14. To do so, FHWA created a robust
    15. 15. data assimilation,
    16. 16. quality checking, and
    17. 17. data dissemination system </li></ul> that can provide near real-time atmospheric and pavement observations from the collective states’ investments in environmental sensor stations (ESS).<br />www.clarusinitiative.org<br />
    18. 18. The Clarus System<br />2011 National ITS Update<br />www.clarus-system.com<br />FHWA Road Weather Management Program, in conjunction with the US DOT ITS Joint Program Office established Clarus in 2004 to reduce the impact of adverse weather conditions on surface transportation users. Clarus is the 21st Century’s answer to the need for timely, high-quality road weather information.<br /><ul><li>A database management system for all surface transportation weather observations in North America
    19. 19. One database removes borders
    20. 20. Provides advanced quality checking for both atmospheric & pavement data
    21. 21. Includes extensive metadata
    22. 22. Easy access via web portal & subscription</li></li></ul><li>Participation Status for Clarus<br />as of September, 2011<br />*1st time showing mobile data sources!<br />*<br />Canadian<br />Participation<br />Over 75% of State DOTs Participate in Clarus<br />Local Participation<br />City of Indianapolis, IN<br />McHenry County, IL<br />City of Oklahoma City, OK<br />Kansas Turnpike Authority <br />Parks Canada<br />Clarus Connection Status<br />Sensor & Station Count<br />2,253 Sensor Stations (ESS)<br />52,471 Individual Sensors<br /> 81 Vehicles<br />Connected (37 States, 5 Locals, 4 Provinces)<br />Connected plus vehicles (1 state)<br />Pending (4 States, 3 Locals, 1 Province)<br />Considering (3 States, 1 Local)<br />*<br />Intelligent Transportation Society of Connecticut<br />
    23. 23. Clarus System Observations<br />
    24. 24. Clarus Users in 2010<br /><ul><li>4993 unique addresses gaining access (3,524,702 hits) from 67 countries
    25. 25. government agencies (federal, state, local)
    26. 26. academic institutions
    27. 27. weather providers
    28. 28. TV stations
    29. 29. private sector firms
    30. 30. unknown sources (Internet providers, etc.)
    31. 31. Clarus Users in 2009 - 314 unique addresses gaining access (59,000+ hits) from 19 countries</li></li></ul><li>Clarus Survey<br />Conducted by ITSA from 15 June - 15 July 2011<br />Intent was to increase understanding of how Clarus is used by system customers<br />28 Participants: <br />13 State DOTs<br />6 private sector companies<br />4 academic institutions<br />3 Federal agencies<br />1 weather service provider <br />1 transit agency<br />
    32. 32. ClarusAccess Methods<br />Map: 48%<br />On-demand request: 26%<br />Subscription: 22%<br />Other: 4%<br />
    33. 33. New Data Preferences<br />Mobile Data: 81%<br />Air Quality: 50%<br />ASOS/AWOS: 46%<br />Other: 20%<br />Response Frequency<br />14<br />
    34. 34. Clarus System Survey - Summary<br />Clear primary indicators:<br /><ul><li> State DOTs are primary users
    35. 35. Main use of data is monitoring current weather
    36. 36. Map is the main access method
    37. 37. Mobile data most desired of new sources</li></li></ul><li>Clarus Regional Demonstrations<br />Objectives<br /><ul><li>Ensure the Clarus System works as designed
    38. 38. Demonstrate the ability of the Clarus System to process and provide data from large numbers of Environmental Sensor Stations (ESS)
    39. 39. Promote/educate on metadata collection
    40. 40. Foster proactive transportation system management in response to the weather
    41. 41. Encourage improved private sector services for road weather information enabled with data from the Clarus System
    42. 42. New software GOTS/open source</li></li></ul><li>Clarus Regional Demonstration<br />5 Use Case Scenarios<br />Enhanced Road Weather Forecasting Enabled by Clarus<br />Seasonal Load Restriction Tool<br />Non-winter Maintenance Decision Support System<br />Multi-state Control Strategy Tool<br />Enhanced Road Weather Content for Traveler Advisories<br />State Transportation Agency Partners<br />Meridian Team includes ID, MT, WY, ND, SD & MN<br />Scenarios 1, 2, 5<br />Mixon Hill Team includes IA, IL & IN<br />Scenarios 1, 3, 4<br />
    43. 43. Use Case #1: Enhanced Road Weather Forecasting Enabled by Clarus<br />Primary Objectives<br /><ul><li>Assess the impact of Clarus road weather observations (RWIS/ESS data) on various weather and road-weather models
    44. 44. Provide Clarus-enabled forecasts to the four use case scenarios that use applications and decision support tools </li></li></ul><li>Use Case #2: Seasonal Load Restriction Tool<br />Web-Client…<br />Restriction Generation<br />Quicker agency deployment<br />Multi-state interaction & awareness<br /><ul><li>State participants found value in . . .
    45. 45. Multiple displays of sub-pavement conditions
    46. 46. Site specific sub-pavement profile forecasts
    47. 47. Less ad-hoc approach potential
    48. 48. Multi-state display of restriction status</li></ul>Current Seasonal Load Restriction (SLR) by county Overview A multi-state view of active SLR information <br /><ul><li> Enables regional restrictions
    49. 49. Uses established state notification formats & procedures</li></li></ul><li>Use Case #3: Non-winter Maintenance and Operations Decision Support Tool<br />Primary Objectives<br /><ul><li>Continue to bridge the current gap between road weather information and proactive maintenance
    50. 50. Expand decision support beyond snow and ice control</li></li></ul><li>Use Case 4: <br />Multi-state Control Strategy Tool<br />Objectives…<br />To provide data and strategies which will improve the coordination between agencies with respect to the imposition of controls and dissemination of associated advisories. <br />This coordination will assist agencies in: <br /><ul><li>proactively responding to situations,
    51. 51. allow for timely dissemination of safety-related information, and thus mitigate the impact to travelers.</li></ul>i.e. chemical spill – tool monitors the weather data – chemical plume – general process graphic<br />
    52. 52. Use Case 5: Enhanced Road Weather Content for Traveler Advisories<br /><ul><li>Services for current and predicted pavement conditions
    53. 53. Expands traveler planning with future conditions
    54. 54. Delivered through state DOT website
    55. 55. Generate enhanced pavement condition information for travelers using Clarus data & enhanced forecasts from UC1
    56. 56. Support for multi-state trip planning
    57. 57. Connect state 511 system info</li></li></ul><li>Use Case #5<br />Web-Portal<br />Enhanced Road Weather Content for Traveler Advisories<br />Analysis Suggests Road Conditions<br />Observed Road Conditions<br />May Change from Good to Fair Driving Conditions<br />May Change from Good to Difficult Driving Conditions<br />Sensors indicate Conditions May Vary From Observed Road Conditions<br />Good Driving Conditions<br />Fair Driving Conditions<br />Difficult Driving Conditions<br />Road Closed/Blocked<br />Unknown<br />
    58. 58. Connected Vehicles and Weather <br />The Vehicle Data Translator (VDT) Version 3.0<br />Weather Observations from Connected Vehicles<br />
    59. 59. The Connected VehicleImproving Road Weather Awareness<br />
    60. 60. Connected Vehicle Applications<br /><ul><li>Safety
    61. 61. Collision warning
    62. 62. Traffic signal violation warning
    63. 63. Emergency notification
    64. 64. Environmental
    65. 65. Eco-routing
    66. 66. Multi-modal routing
    67. 67. Adaptive roadway lighting
    68. 68. Smart intersections
    69. 69. Mobility
    70. 70. Adaptive traffic signals
    71. 71. Intermodal transfers
    72. 72. Event and emergency planning/response
    73. 73. Parking location assistance</li></ul>26<br />
    74. 74. Connected Vehicle Scenarios<br />27<br />
    75. 75. Weather & the Connected Vehicle<br />Objectives…<br /><ul><li>Obtain a thorough picture of current weather and road conditions by including mobile sources
    76. 76. Higher resolution observations that spatially augment fixed sensors
    77. 77. Take advantage of existing standards and on-board sensors
    78. 78. Improve weather-related decision support tools to mitigate safety and mobility impacts of weather
    79. 79. Based on ability to better detect and forecast road weather and pavement conditions</li></li></ul><li>Weather Observations from Connected Vehicles<br />29<br />
    80. 80. Vehicle Data Translator (VDT)<br />Objectives…<br />Develop and improve the Connected Vehicle “Anytime, Anywhere Road Weather Information” <br />Better Characterization of current weather and road conditions<br />Accurate Quality Checking and/or Quality Control of vehicle data<br />Development of inferred road segment specific weather and road-weather information for end-user applications<br />
    81. 81. Vehicle Data Translator (VDT)<br />Ancillary: Radar, Satellite, RWIS, Etc.<br />VDT 3.0<br />Stage I<br />Stage III<br />Stage II<br />Mobile data ingesters<br />Segment module<br />Inference Module<br />Ancillary data ingesters<br />QC Module<br />QC Module<br />Output data handler<br />Output data handler<br />Output data handler<br />QC Module<br />Parsed mobile data<br />Advanced road segment data<br />Basic road segment data<br />Apps and Other Data Environments<br />
    82. 82. Vehicle Data Translator (VDT) – Version 3.0<br />Stage I<br />Ingest vehicle data from aftermarket sensors<br />Data parsed, sorted/binned<br />Light Quality Control<br />Sorted by time, road segment and grid cell<br />Segments & grids user defined<br />All processed data available for other applications<br />Stage I<br />Mobile dataingesters<br />Output data handler<br />Parsed mobile data<br />Apps<br />
    83. 83. Vehicle Data Translator (VDT) – Version 3.0<br />Stage II<br />Ingest ancillary data for QC and Stage III<br />Quality Checks<br />From Clarus: Sensor Range, Spatial, Climate Range<br />New Mobile Data Tests: Data Filtering (tunnel, slow speeds), Model Analysis, Neighboring Vehicle, Combined Algorithm<br />Combines point data into basic road segment products<br />Temp range, speed, etc<br />Ancillary: Radar, Satellite, RWIS, Etc.<br />Stage II<br />Statistics module<br />Ancillary data ingesters<br />Output data handler<br />QCh Module<br />QC’d data, Basic road segment data<br />Apps<br />
    84. 84. Vehicle Data Translator (VDT) – Version 3.0<br />Stage III<br />More sophisticated road impact information<br />Precipitation Type and Intensity: combines basic vehicle (e.g. wiper, temp), weather radar and satellite data<br />Visibility: combines basic vehicle (e.g. headlight, wiper, temp), satellite and fixed weather station data <br />Pavement Condition: combines more vehicle (e.g. ABS, traction, etc) , weather radar and satellite<br />Stage III<br />Inference Module<br />Output data handler<br />Advanced road segment data<br />Apps<br />
    85. 85. What Can You Do With VDT-based Data?<br />There are any number of road weather dynamic applications that could use vehicle-based observations:<br /><ul><li>State DOT-based applications
    86. 86. Transportation-specific applications
    87. 87. Broad Weather & Transportation applications</li></li></ul><li>State DOT-based Applications<br /><ul><li>Observation assimilation
    88. 88. Fill in the gaps between fixed stations
    89. 89. Collect real-time pavement temperatures</li></ul>VDT-based data<br /><ul><li>Maintenance Decision Support
    90. 90. What are the current roads conditions?
    91. 91. Accurate pavement temperature modeling
    92. 92. Manage Maintenance Actions
    93. 93. End of Shift Reports
    94. 94. Materials Management</li></li></ul><li>Transportation-specificApplications<br />*<br />*Simulated screen – designed to not distract the driver<br />VDT-based weather alerts:<br /><ul><li> Impending weather hazards
    95. 95. Alerts from other vehicles
    96. 96. Re-routing</li></li></ul><li>Broad Transportation Applications<br />VDT-based data<br />Winter Maintenance – <br /> Which roads have been treated?<br />Route Specific Impact Warnings for…<br />Tornado Warning! <br />I70 Denver to Limon<br />Delay Until 3:30pm<br />School Buses<br />EMS<br />Truckers<br />
    97. 97. Weather-related Applications<br />Numerical Weather Modeling<br />Traffic Modeling and Alerting<br />Weather Modeling – complex terrain<br />Other surface transportation users<br />
    98. 98. Future - Optimized Winter Maintenance<br /><ul><li>Use of Connected Vehicle network and data allows more effective and efficient deployment of pre-treatment, treatment, and plowing operations
    99. 99. Local weather information from Connected Vehicle network
    100. 100. From vehicles:
    101. 101. Temperature, barometric pressure, precipitation sensors, head lights
    102. 102. Activation of ABS, Stability control, traction control
    103. 103. From roadside equipment:
    104. 104. Pavement temperatures, humidity, etc.
    105. 105. From on-board equipment in maintenance vehicles
    106. 106. Application rates
    107. 107. GPS, Time</li></ul>40<br />
    108. 108. Integrated Mobile Observing & Dynamic Decision Support<br />State DOT & Private Vehicle Data<br />Connected Vehicle Data Capture<br />VDT<br />(NCAR)<br />Clarus<br />Other Connected Vehicle Applications<br />
    109. 109. FHWA Road Weather Mgmt. Team<br />Paul Pisano, Team Leader Dale Thompson<br />FHWA Office of Operations USDOT RITA, JPO<br />202-366-1301 202-366-4876<br />paul.pisano@dot.gov dale.thompson@dot.gov<br />Roemer Alfelor C.Y. David Yang<br />FHWA Office of Operations FHWA Off. of Operations R&D<br />202-366-9242 202-493-3284<br />roemer.alfelor@dot.gov david.yang@dot.gov<br />Gabriel Guevara Ray Murphy<br />FHWA Office of Operations FHWA Off. of Tech. Services<br />202-366-0754 708-283-3517<br />gabriel.guevara@dot.gov ray.murphy@dot.gov<br />

    ×