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
Intelligent Transportation Society of Connecticut 14th Annual Meeting September 28, 2011
Intelligent Transportation Society of Connecticut 14th Annual Meeting September 28, 2011 Ray Murphy
Weather Technologies… Clarus and the Connected Vehicle Ray Murphy Federal Highway Administration September 28, 2011 Intelligent Transportation Society of Connecticut
Road Weather Information System (RWIS) Environmental Sensor Station (ESS) Existing RWIS installations: East Hartford Middletown Groton New London Lebanon Sikorsky Bridge New Haven Stamford North Canaan Torrington Avon Mountain Seymour Danbury
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.
that can provide near real-time atmospheric and pavement observations from the collective states’ investments in environmental sensor stations (ESS). www.clarusinitiative.org
The Clarus System 2011 National ITS Update www.clarus-system.com 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.
A database management system for all surface transportation weather observations in North America
Participation Status for Clarus as of September, 2011 *1st time showing mobile data sources! * Canadian Participation Over 75% of State DOTs Participate in Clarus Local Participation City of Indianapolis, IN McHenry County, IL City of Oklahoma City, OK Kansas Turnpike Authority Parks Canada Clarus Connection Status Sensor & Station Count 2,253 Sensor Stations (ESS) 52,471 Individual Sensors 81 Vehicles Connected (37 States, 5 Locals, 4 Provinces) Connected plus vehicles (1 state) Pending (4 States, 3 Locals, 1 Province) Considering (3 States, 1 Local) * Intelligent Transportation Society of Connecticut
Clarus Users in 2009 - 314 unique addresses gaining access (59,000+ hits) from 19 countries
Clarus Survey Conducted by ITSA from 15 June - 15 July 2011 Intent was to increase understanding of how Clarus is used by system customers 28 Participants: 13 State DOTs 6 private sector companies 4 academic institutions 3 Federal agencies 1 weather service provider 1 transit agency
Clarus Regional Demonstration 5 Use Case Scenarios Enhanced Road Weather Forecasting Enabled by Clarus Seasonal Load Restriction Tool Non-winter Maintenance Decision Support System Multi-state Control Strategy Tool Enhanced Road Weather Content for Traveler Advisories State Transportation Agency Partners Meridian Team includes ID, MT, WY, ND, SD & MN Scenarios 1, 2, 5 Mixon Hill Team includes IA, IL & IN Scenarios 1, 3, 4
Use Case #1: Enhanced Road Weather Forecasting Enabled by Clarus Primary Objectives
Assess the impact of Clarus road weather observations (RWIS/ESS data) on various weather and road-weather models
Provide Clarus-enabled forecasts to the four use case scenarios that use applications and decision support tools
Use Case #2: Seasonal Load Restriction Tool Web-Client… Restriction Generation Quicker agency deployment Multi-state interaction & awareness
Current Seasonal Load Restriction (SLR) by county Overview A multi-state view of active SLR information
Enables regional restrictions
Uses established state notification formats & procedures
Use Case #3: Non-winter Maintenance and Operations Decision Support Tool Primary Objectives
Continue to bridge the current gap between road weather information and proactive maintenance
Expand decision support beyond snow and ice control
Use Case 4: Multi-state Control Strategy Tool Objectives… To provide data and strategies which will improve the coordination between agencies with respect to the imposition of controls and dissemination of associated advisories. This coordination will assist agencies in:
proactively responding to situations,
allow for timely dissemination of safety-related information, and thus mitigate the impact to travelers.
i.e. chemical spill – tool monitors the weather data – chemical plume – general process graphic
Use Case 5: Enhanced Road Weather Content for Traveler Advisories
Services for current and predicted pavement conditions
Expands traveler planning with future conditions
Use Case #5 Web-Portal Enhanced Road Weather Content for Traveler Advisories Analysis Suggests Road Conditions Observed Road Conditions May Change from Good to Fair Driving Conditions May Change from Good to Difficult Driving Conditions Sensors indicate Conditions May Vary From Observed Road Conditions Good Driving Conditions Fair Driving Conditions Difficult Driving Conditions Road Closed/Blocked Unknown
Connected Vehicles and Weather The Vehicle Data Translator (VDT) Version 3.0 Weather Observations from Connected Vehicles
The Connected VehicleImproving Road Weather Awareness
Obtain a thorough picture of current weather and road conditions by including mobile sources
Higher resolution observations that spatially augment fixed sensors
Take advantage of existing standards and on-board sensors
Improve weather-related decision support tools to mitigate safety and mobility impacts of weather
Based on ability to better detect and forecast road weather and pavement conditions
Weather Observations from Connected Vehicles 29
Vehicle Data Translator (VDT) Objectives… Develop and improve the Connected Vehicle “Anytime, Anywhere Road Weather Information” Better Characterization of current weather and road conditions Accurate Quality Checking and/or Quality Control of vehicle data Development of inferred road segment specific weather and road-weather information for end-user applications
Vehicle Data Translator (VDT) Ancillary: Radar, Satellite, RWIS, Etc. VDT 3.0 Stage I Stage III Stage II Mobile data ingesters Segment module Inference Module Ancillary data ingesters QC Module QC Module Output data handler Output data handler Output data handler QC Module Parsed mobile data Advanced road segment data Basic road segment data Apps and Other Data Environments
Vehicle Data Translator (VDT) – Version 3.0 Stage I Ingest vehicle data from aftermarket sensors Data parsed, sorted/binned Light Quality Control Sorted by time, road segment and grid cell Segments & grids user defined All processed data available for other applications Stage I Mobile dataingesters Output data handler Parsed mobile data Apps
Vehicle Data Translator (VDT) – Version 3.0 Stage II Ingest ancillary data for QC and Stage III Quality Checks From Clarus: Sensor Range, Spatial, Climate Range New Mobile Data Tests: Data Filtering (tunnel, slow speeds), Model Analysis, Neighboring Vehicle, Combined Algorithm Combines point data into basic road segment products Temp range, speed, etc Ancillary: Radar, Satellite, RWIS, Etc. Stage II Statistics module Ancillary data ingesters Output data handler QCh Module QC’d data, Basic road segment data Apps
Vehicle Data Translator (VDT) – Version 3.0 Stage III More sophisticated road impact information Precipitation Type and Intensity: combines basic vehicle (e.g. wiper, temp), weather radar and satellite data Visibility: combines basic vehicle (e.g. headlight, wiper, temp), satellite and fixed weather station data Pavement Condition: combines more vehicle (e.g. ABS, traction, etc) , weather radar and satellite Stage III Inference Module Output data handler Advanced road segment data Apps
What Can You Do With VDT-based Data? There are any number of road weather dynamic applications that could use vehicle-based observations:
Broad Transportation Applications VDT-based data Winter Maintenance – Which roads have been treated? Route Specific Impact Warnings for… Tornado Warning! I70 Denver to Limon Delay Until 3:30pm School Buses EMS Truckers
Weather-related Applications Numerical Weather Modeling Traffic Modeling and Alerting Weather Modeling – complex terrain Other surface transportation users
Integrated Mobile Observing & Dynamic Decision Support State DOT & Private Vehicle Data Connected Vehicle Data Capture VDT (NCAR) Clarus Other Connected Vehicle Applications
FHWA Road Weather Mgmt. Team Paul Pisano, Team Leader Dale Thompson FHWA Office of Operations USDOT RITA, JPO 202-366-1301 202-366-4876 email@example.com firstname.lastname@example.org Roemer Alfelor C.Y. David Yang FHWA Office of Operations FHWA Off. of Operations R&D 202-366-9242 202-493-3284 email@example.com firstname.lastname@example.org Gabriel Guevara Ray Murphy FHWA Office of Operations FHWA Off. of Tech. Services 202-366-0754 708-283-3517 email@example.com firstname.lastname@example.org