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CT DOT Mtg ITS RWIS Clarus 092811

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Overview on RWIS, Clarus with CT DOT on Sept. 28, 2011 Ray Murphy

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CT DOT Mtg ITS RWIS Clarus 092811

  1. 1. Road Weather Information Systems<br />September 28, 2011<br />Ray Murphy, US DOT - FHWA<br />Office of Technical Support<br />
  2. 2. Ray Murphy ray.murphy@dot.gov<br />BSEE – IIT in Chicago<br />FHWA +10 yrs - program support:<br />Road Weather Management<br />Emergency Transportation Operations<br />Real – Time Data Management<br />+ 20 yrs Illinois Dept. of Transportation:<br />Operations, Maintenance, & Construction<br />ITS Project Manager<br />CEC Officer/Seabees & Engineer Mentor<br />2<br />
  3. 3. Agenda<br />Wednesday, September 28, 2011<br />09:30-09:40Welcome<br />09:40-10:30RWIS<br />10:30-10:45BREAK<br />10:45-11:20Clarus<br />11:20-11:30Wrap-up<br />3<br />
  4. 4. Self-Introductions<br />Name<br />Position/Role<br />Any specific area of interest with Road Weather Management?<br />4<br />
  5. 5. Connecticut's Weather Fun Facts<br />Interesting Weather Facts<br /><ul><li>Average winter snowfall in the Northwest Hills is 50 inches
  6. 6. Average winter snowfall along the coast is 30-35 inches </li></ul>Average snowfall:<br /><ul><li>November - 2 inches
  7. 7. December - 10.4 inches
  8. 8. January - 12.3 inches
  9. 9. February - 11.3 inches</li></ul>5<br /><ul><li>March - 9.3 inches </li></li></ul><li>Road Weather Information System<br />Environmental Sensor Station<br />6<br />
  10. 10. History of RWIS Initiatives<br /><ul><li>1994 – Scanning tour to Europe and Japan
  11. 11. 1998 – Establishment of the Snow and Ice</li></ul> Cooperative Fund Program (SICOP)<br /><ul><li>1998+ – Strategic Highway Research Program</li></ul> (SHRP) implemented many of the new<br /> equipment technologies/maintenance <br /> systems observed abroad<br />7<br />
  12. 12. Environmental Sensor Stations<br />2,253 Sensor Stations (ESS) 52,471 Individual Sensors<br />8<br />
  13. 13. 1.<br />2.<br />3.<br />Sites to be upgraded?<br /><ul><li>All existing sites will be upgraded in time....we will start with the sites that have inoperative sensors and work forward from there.</li></ul>Active/non-intrusive sensors?<br /><ul><li>No new sites are planned - but we plan to install active sensors (maybe even nonintrusive) at all sites in time.</li></ul>Guidance on future sites?<br /><ul><li>The existing 13 sites will remain. Future RWIS sites have not yet been identified, but we hope to start that process next year. </li></ul>4.<br />5.<br />6.<br />7.<br />8.<br />9.<br />10.<br />11.<br />12.<br />13.<br />9<br />
  14. 14. 10<br />
  15. 15. Gaps – data/operational<br />Gaps – data/operational<br />The Clarus System (screen shot)<br />11<br />
  16. 16. Definition: Road Weather Information System (RWIS) <br />and its associated Environmental Sensor Stations (ESS) . <br />The term RWIS has a number of diverse definitions ranging from sensing and processing devices in the field to a composite of all weather and pavement information resources available to highway operations and maintenance personnel. <br />For our purposes, RWIS can be defined as the hardware, software programs, and communications interfaces necessary to collect and transfer field observations to a display device at the user’s location. <br />While the original purpose of the RWIS was to address winter weather conditions, applications have been developed to detect and monitor a variety of road weather conditions impacting road operations and maintenance. <br />12<br />
  17. 17. An ESS consists of one or more sensors measuring atmospheric, pavement, soil, and/or water level conditions. <br />13<br />
  18. 18. Examples of ESS Sensors<br />14<br />
  19. 19. As the graphic above illustrates, the RWIS collects, transmits, processes, and disseminates weather and road condition information. <br /><ul><li> The RWIS may consist of several meteorological and pavement condition monitoring stations strategically located near a highway that help transportation managers make more informed operational decisions. Specialized equipment and computer programs monitor weather and pavement condition elements that help users observe how adverse weather is currently affecting the highways and assess future impacts. </li></ul>For example, winter road maintenance managers may benefit from such a system during winter storms by making optimal use of materials and staff, selecting appropriate treatment strategies, utilizing anti-icing techniques, and properly timing maintenance activities. Traffic managers may use road weather observations to modify traffic signal timing, reduce speed limits, and close hazardous roads and bridges.<br />15<br />
  20. 20. ESS Location Relative to Roadway<br />16<br />
  21. 21. Prioritized RWIS Observations<br />Precipitation Type <br />Surface Temperature <br />Surface Status (dry/wet) <br />Precipitation Rate/Intensity <br />Visibility <br />Precipitation Accumulation <br />Chemical Percentage <br />Dew point <br />Air Temperature <br />Ice Percentage <br />Freezing Point Temperature <br />Depth of Water Layer <br />Wind Speed <br />Relative Humidity <br />Wind Direction <br />Barometric Pressure <br />Subsurface Temperature <br />Wind Gusts <br />17<br />
  22. 22. Environmental Sensor Station (ESS) Operational Applications<br />Traffic Managers<br />Maintenance Managers<br />Emergency Managers<br />Dynamic Message Signs & Other Roadside Devices<br />Information Service Providers<br />Public & Private Weather Service Providers<br />Environmental Monitoring Networks<br />ESS data provides many benefits, in addition to improving road safety, mobility, and productivity, by supplying information on roadway conditions essential for traffic operations, traveler information, road maintenance, and emergency response. <br />18<br />
  23. 23. Benefits derived from these<br />applications include:<br /><ul><li> Weather service providers for surface transportation customers use ESS data to develop tailored road weather products (e.g., pavement temperature forecasts).
  24. 24. National Weather Service (NWS), military(public) and private weather service providers use these data to develop weather products, short-range forecasts, and forecast verification, and as input to locally run weather forecast models.
  25. 25. State climatologists can use ESS data for long-term records and climatological analyses.
  26. 26. Local, state, or Federal disaster assessment and response agencies (e.g., Federal Emergency Management Agency and the Department of Homeland Security) may use these data to manage emergencies and related response actions.</li></ul>19<br />
  27. 27. Benefits derived from these<br />applications include:<br /><ul><li>Insurance companies can use these data to helpdetermine risks of potential impactsfrom future weather events.
  28. 28. Forensic meteorologists can use ESS data to better understand and reconstruct roadway crashes.
  29. 29. RWIS ESS data can also be leveraged to support rail, pipeline, and marine operations when such operations are adjacent to or reasonably near the ESS.
  30. 30. Government and university Mesonetscan include these data to support the development of weather and road weather forecast models.</li></ul>20<br />
  31. 31. What is a Mesonet?<br />In meteorology, a mesonet is a regional network of automated observing surface weather stations designed to observe mesoscale (intermediate size) meteorological phenomena (weather features and their associated processes). <br />Due to the space and time scales associated with mesoscale phenomena, weather stations comprising a mesonet will be spaced closer together and report more frequently. The term mesonet refers to the collective group of these weather stations, and are typically owned and operated by a common entity.<br />21<br />
  32. 32. Why Mesonets?<br />Mesoscale phenomena can cause weather conditions in a localized area to be significantly different from that dictated by the ambient large-scale condition. As such, meteorologists need to understand these phenomena in order to improve forecast skill. Observations are critical to understanding the processes by which these phenomena form, evolve, and dissipate.<br />The long-term observing networks (RWIS, ASOS, AWOS), however, are too sparse and report too infrequently for mesoscale research. RWIS, ASOS and AWOS stations are typically spaced 40 to 100 miles apart. "Mesoscale" weather phenomena occur on a spatial scale of hundreds of miles. Thus, an observing network with finer temporal and spatial scales is needed for mesoscale applications. This need led to the development of the mesonet.<br />22<br />
  33. 33. Mesoscale phenomena can cause weather conditions in a localized area to be significantly different from that dictated by the ambient large-scale condition.<br />23<br />
  34. 34. Maximizing Benefits…<br />To maximize these benefits, an attempt should be made during the planning process for siting RWIS ESSs to contact other organizations involved in similar data collection that may help both local transportation agencies and other customers (e.g., NWS; FAA; local TV stations; universities and high schools; and, other city, county, and state agencies). <br /><ul><li> Refer to the Siting Guidelines: in there, it discusses the potential for establishing information partnerships and/or leveraging the data collected by other organizations. The Siting Checklist provides a reminder to the siting team to consider information partnerships during the siting process.</li></ul>24<br />
  35. 35. Diverse Planning Team:<br />The planning team should also include local DOT personnel, especially maintenance personnel. These individuals typically possess a vast knowledge of weather conditions along the road segment they maintain. The maintenance personnel can provide critical input about recurring weather problems such as the locations of frequent slippery pavements, low visibilities, or strong gusty winds that suggest the need for an ESS<br />installation. <br />Additionally, local DOT personnel can often identify areas where an ESS sensor might be vulnerable to large snow drifts, flooding, or pooling water from spring thaws.<br />25<br />
  36. 36. An analysis of the operational requirements<br />Planning the ESS network should include an analysis of the operational requirements for road weather information. This analysis will drive the environmental sensor requirements and lead to decisions regarding sensor selection and siting. <br />Considerations to include:<br />How will the road weather information be used? <br />For example, will the information be used to monitor roadway conditions as input to winter maintenance decisions or road temperature modeling, or to support weather-responsive traffic management, traveler information systems (e.g., 511 systems) or road construction efforts?<br />Will the ESS be used to measure a site-specific condition or to provide information that may represent conditions across a general area? For example, installing a sensor to monitor the visibility along a fog-prone road segment may result in completely different siting decisions than if the requirement is to collect wind and temperature information for input to a road weather model.<br />.<br />26<br />
  37. 37. What needs to be measured at each installation? <br /><ul><li>System designers should keep in mind that several different sensors may be needed in combination to satisfy observing requirements.
  38. 38. For example, if a pavement sensor is to be included in an installation, the DOT may also want to install air temperature, humidity, and precipitation sensors to complement the pavement sensor data.
  39. 39. The precipitation sensor can help identify whether pavement sensor readings are indicative of new or continuing precipitation, while the temperature and humidity sensors will indicate whether conditions support the formation of frost.
  40. 40. DOTs may want to create a prioritized list of the road weather elements and sites they need to fulfill their requirements. Such an approach may help in making tradeoffs when data collection needs exceed available funding or when a phased approach to meeting statewide requirements is desired.
  41. 41. DOTs should also consider other sources of weather and pavement data that may be available to meet road weather information requirements. Developing data-sharing partnerships with other agencies may help satisfy RWIS ESS installation requirements while improving the availability of data to all partners. </li></ul>27<br />
  42. 42. Items to take into consideration <br /><ul><li> Quality and Cost
  43. 43. Added maintenance responsibilities
  44. 44. Utilizing the data
  45. 45. Communications
  46. 46. Varied Users
  47. 47. Liability</li></ul>28<br />
  48. 48. Communications Standards<br />A common communications interface is used for RWIS and other ITS devices from multiple vendors to exchange information. Those NTCIP standards used in RWIS applications are referred to as ESS standards.<br />29<br />
  49. 49. Proactive Planning<br /><ul><li>Anticipate an event to unfold
  50. 50. Specific sequence of actions is planned and</li></ul> executed in advance<br /><ul><li>Produces more effective event management</li></ul>Benefits of Proactive Planning<br /><ul><li>Reduce costs
  51. 51. Increase efficiency
  52. 52. Increase effectiveness
  53. 53. Provide the highest level of service possible</li></ul>30<br />
  54. 54. Road Weather Information System<br />Environmental Sensor Station<br />Siting Guidelines<br />31<br />
  55. 55. RWIS/ESS Siting Guidelines<br /><ul><li>Criteria is based on an analysis of other published research & interviews with state DOT experts, equipment suppliers & consultants</li></ul>Publication No. FHWA-HOP-05-026<br />FHWA-JPO-09-012<br />The Federal Highway Administration, the Aurora RWIS Pooled Fund Program, and the AASHTO Snow and Ice Cooperative Program partnered to produce this RWIS ESS Siting Guidelines. <br /><ul><li>Guidelines are a set of recommend-ations & are not mandates or standards</li></ul>http://ntl.bts.gov/lib/30000/30700/30705/14447.pdf<br />32<br />
  56. 56. Purpose of the Guide<br /><ul><li> Encourage uniform siting criteria
  57. 57. Maximize investments in ESS
  58. 58. Provide instructions for how to select</li></ul> sensors for an ESS<br /><ul><li>Provide insight for the selection of</li></ul> appropriate locations for sensor placement<br /><ul><li>Improve integration of road weather data with other meteorological data sets
  59. 59. Encourage compiling & maintaining metadata</li></ul>33<br />
  60. 60. Implementation and Evaluation in developing Version 2 <br /><ul><li>Evaluated the use & effectiveness of Version 1.0
  61. 61. Update with new technology & metadata information
  62. 62. Three State DOT’s were interviewed for their evaluation of the Guidelines:
  63. 63. Michigan DOT
  64. 64. Idaho Transportation Department
  65. 65. New Hampshire DOT</li></ul>34<br />
  66. 66. Siting Metadata<br /><ul><li>Metadata: “data about data”
  67. 67. Metadata are used to document the characteristics of each sensor and its siting to provide users an understanding of what the sensor data really represent
  68. 68. Standards have been developed for some geospatial metadata, but not for RWIS ESS location and sensor metadata</li></ul>35<br />
  69. 69. Version 2.0 Updates<br />Discussion of bridge anti-icing systems<br />Added a section on “How to use this guide”<br />Updated information on ESS maintenance<br />Information about the ClarusInitiative<br />Included a discussion on archaeological constraints, soil conditions & clear zones<br />Included a reference to the Storm water Guide for storm water management ESS sites<br />36<br />
  70. 70. Additional Siting Tools<br /><ul><li>Thermal mapping
  71. 71. Better defines thermal characteristics
  72. 72. Help identify similar areas
  73. 73. Optimize the number of ESS to be installed
  74. 74. Portable sensor systems help identify:
  75. 75. potential permanent ESS sites
  76. 76. the use of non-intrusive sensors (not requiring implanting in/below pavement)</li></ul>37<br />
  77. 77. Conclusion<br /><ul><li>Collection of road weather information can provide decision support to transportation managers & contribute to more accurate road weather forecasts.
  78. 78. Siting recommendations are designed to satisfy as many road weather monitoring, detecting and predicting requirements as possible.
  79. 79. Siting decisions are best made by a team of transportation operations, road maintenance, and weather experts.
  80. 80. Siting recommendations encourage uniformity in siting, application and participation in the greater community.</li></ul>38<br />
  81. 81. New Hampshire input<br />39<br />
  82. 82. RWIS in Michigan’s Upper Peninsula<br />Dawn Gustafson, P.E., Traffic and Safety Engineer<br />Michigan Department of Transportation<br />(906) 786-1830 ext. 316<br />gustafsond@michigan.gov<br />October 13, 2009<br />40<br />
  83. 83. Non-Intrusive Detection/Applications<br />Disclaimer: FHWA does not endorse any 3rd party vendor products and/or services<br />41<br />
  84. 84. NTCIP Compliant RWIS ESS<br />Permanent Stations<br />NTCIP compliant – V1 & V2<br />Can place in existing networks and poll with NTCIP-compliant software<br />Hardware has no end-of-life<br />Can easily replace existing stations<br />Interfaces with many existing sensors from other vendors<br />Compatible with existing towers & power supplies<br />Lower replacement costs<br />42<br />
  85. 85. Temporary or Seasonal Monitoring<br />NTCIP-compliant<br />Easy assembly and disassembly<br />Any measurement can be made<br />43<br />
  86. 86. 44<br />Road Weather Information Systems<br /> NTCIP 1204 ESS Compliant RPUs<br /> ALERT Protocol based RPU/Data-Loggers & Controllers<br /> Non-Intrusive Road Sensors<br /> Weather Responsive Traffic Management Systems<br /><ul><li>Flooded Roadway Warnings
  87. 87. Wet & Icy Warnings
  88. 88. High Wind Warnings
  89. 89. Low Visibility Warnings</li></ul>Hydrological Monitoring & Flood Warning<br /><ul><li>Flood Warning & Environmental Monitoring
  90. 90. Rain & Stream Gauging
  91. 91. Weather Stations
  92. 92. Dam and Levee Safety</li></ul>Solar Power devices<br />TxDOT Bridge Mount Weather & Stream Monitoring System SH35 at Brazos River<br />Flooded Roadway Warning System with Automatic Barrier Gates<br />W.W. White Rd, San Antonio, TX<br />44<br />44<br />
  93. 93. Road Surface Status at controlled intersection<br />Non-Intrusive Pavement Sensor & MiniRWIS NTCIP RPU reports road surface conditions<br /><ul><li>Provides air temp, surface temp, surface status, surface grip coefficient & soiled lens “clean me” indication.
  94. 94. Flat Plate window & lens guard simplifies lens cleaning.
  95. 95. Wi-Fi access simplifies set-up and calibration from ground level
  96. 96. One-Click Automatic calibration</li></ul>45<br />
  97. 97. Ability To Install an RWIS with minimal investment -- No RPU/Datalogger Needed<br />Digital Interface Protocol<br />Wind Sensor<br />Pavement Sensor<br />Small Compact RWIS with no RPU<br />Modem<br />Radio or Cellular<br />Connection<br />46<br />
  98. 98. Non-Invasive Pavement Condition sensor<br />Radar precipitation sensor<br />Maintenance-free sensor<br />Accumulation calculation (resolution 0.01mm / 0.1mm / 1mm)<br />Measurement of surface conditions such as wetness, ice, snow or frost.<br /><ul><li>FREEZE TEMPERATURE – NON-INVASIVELY
  99. 99. Measurement of water film height
  100. 100. Measurement of ice percentage in water and determination of freeze temperature
  101. 101. Measurement of friction
  102. 102. Fully integrated surface temperature measurement (pyrometer)
  103. 103. Electric Isolation
  104. 104. Easy to mount
  105. 105. Low Maintenance costs by firmware</li></ul>Innovative principle:Microwaves–Doppler Radar <br />Precipitation type (rain, snow, mixed rain, ice rain and hail) / Precipitation intensity (mm/h)<br />Digital data communication with standard protocol and 2 digitale outputs<br />47<br />
  106. 106. Road Weather Information Systems (RWIS) have evolved into complete ITS platforms capable of monitoring any weather or traffic condition.<br />Flash Food<br />Traffic Flow<br />Air Quality<br />Pavement sensors have moved out of the road surface to allow for safer, less expensive maintenance, while also adding surface friction. <br />Non-intrusive Sensors<br />48<br />
  107. 107. Weather on the Go<br />To supplement fixed RWIS mobile weather sensors are becoming increasing popular.<br />When tied to an AVL system you are able to extend your road weather network.<br />Measures:<br />Air temperature<br />Pavement temperature<br />Dew Point<br />Relative Humidity<br />49<br />
  108. 108. 15 minute Break<br />50<br />
  109. 109. Clarus and the Connected Vehicle<br />Ray Murphy<br />Federal Highway Administration<br />September 28, 2011 Meeting with Connecticut DOT<br />
  110. 110. 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.
  111. 111. To do so, FHWA created a robust
  112. 112. data assimilation,
  113. 113. quality checking, and
  114. 114. 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 />
  115. 115. 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
  116. 116. One database removes borders
  117. 117. Provides advanced quality checking for both atmospheric & pavement data
  118. 118. Includes extensive metadata
  119. 119. Easy access via web portal & subscription</li></li></ul><li>Over 75% of State DOTs Participate in Clarus<br />Sensor & Station Count<br />2,253 Sensor Stations (ESS)<br />52,471 Individual Sensors<br />
  120. 120. Participation Status for Clarus<br />as of August 24, 2011<br />*1st time showing mobile data sources!<br />*<br />Canadian<br />Participation<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 />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 />Sensor & Station Count<br />2,253 Sensor Stations (ESS)<br />52,471 Individual Sensors<br /> 81 Vehicles<br />*<br />55<br />
  121. 121. Clarus System Observations<br />
  122. 122. Clarus Users in 2010<br /><ul><li>4993 unique addresses gaining access (3,524,702 hits) from 67 countries
  123. 123. government agencies (federal, state, local)
  124. 124. academic institutions
  125. 125. weather providers
  126. 126. TV stations
  127. 127. private sector firms
  128. 128. unknown sources (Internet providers, etc.)
  129. 129. Clarus Users in 2009 - 314 unique addresses gaining access (59,000+ hits) from 19 countries</li></li></ul><li>Quality Checking Algorithms<br />Sensor Range Test<br />Climate Range Test<br />Observation compared to manufacturer’s published minimum and maximum values<br />Example: <br />Air Temperature: 25 C<br />Specs: -20 C to 50 C<br />Test Passed<br />Observation compared to historical climate minimum and maximum values per month by geographic area – gridded field<br />Example:<br />Air Temperature: 25 C<br />Climate Value for January: -10 C to 20 C<br />Test did not pass<br />58<br />
  130. 130. Quality Checking Algorithms<br />Step Test<br />Like Instrument Test<br />Observation compared to the same observation types from the ESS<br />Observation compared to previous observations over a configured time range to determine if the rate of change (plus or minus) was acceptable<br />Example:<br />Values: 10 C, 12 C, 15 C, 35 C<br />Test did not pass<br />59<br />
  131. 131. Quality Checking Algorithms<br />Persistence Test<br />Dewpoint Test<br />Observation compared to previous observations to determine if the values had changed at all over a period of time<br />Example:<br />Values: 38.6%, 38.6%, 38.7%<br />Test passed<br />Determine the neighbors<br />Calculate a dewpoint value based on the temperature & relative humidity<br />Conduct a spatial test<br />60<br />
  132. 132. Quality Checking Algorithms<br />Barnes Spatial Test<br />IQR Spatial Test<br />Observation compared to neighboring ESS and ASOS/AWOS to determine if they are similar<br />Neighboring ESS and ASOS/AWOS identified<br />Eliminate the neighbors that are +-350 meters<br />Eliminate the highest and lowest neighboring values<br />Observation compared to remaining neighbors to determine if they are similar<br />Requires 5 initial neighbors for the test to run<br />61<br />
  133. 133. Quality Checking Algorithms<br />Sea Level Pressure Test<br />Precipitation Accumulation<br />Calculate a sea level pressure from the station pressure and then conduct a spatial test<br />Conversion based on current 700mb Rawinsonde observations or 30-year average gridded data<br />Applies to:<br />3-hour<br />6-hour<br />12-hour<br />24-hour<br />Uses Stage II & IV precipitation files to accumulate the precipitation for comparison<br />62<br />
  134. 134. Mobile Observations<br />Data Need<br />Elevation<br />Observations are on the map for one hour<br />Used in quality checking<br />63<br />
  135. 135. 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 />
  136. 136. Clarus Data Uses<br />Monitor near real-time weather observations <br />61% use multi-state view<br />54% use in-state view<br />Weather model input: 39%<br />Evaluating maintenance needs on RWIS: 36%<br />Use in other systems (e.g. 511) and weather forecasting: 29%<br />
  137. 137. ClarusAccess Methods<br />Map: 48%<br />On-demand request: 26%<br />Subscription: 22%<br />Other: 4%<br />
  138. 138. New Data Preferences<br />Mobile Data: 81%<br />Air Quality: 50%<br />ASOS/AWOS: 46%<br />Other: 20%<br />Response Frequency<br />67<br />
  139. 139. Clarus System Survey - Summary<br />Clear primary indicators:<br /><ul><li> State DOTs are primary users
  140. 140. Main use of data is monitoring current weather
  141. 141. Map is the main access method
  142. 142. Mobile data most desired of new sources</li></li></ul><li>The Connected VehicleImproving Road Weather Awareness<br />
  143. 143. Connected Vehicle Scenarios<br />70<br />
  144. 144. Connected Vehicle “Anytime, Anywhere Road Weather Data”<br />71<br />
  145. 145. Weather & the Connected Vehicle<br />Obtain a thorough picture of current weather and road conditions by including mobile sources<br /><ul><li>Higher resolution observations that spatially augment fixed sensors
  146. 146. Take advantage of existing standards and on-board sensors</li></ul>Improve weather-related decision support tools to mitigate safety and mobility impacts of weather<br /><ul><li>Based on ability to better detect and forecast road weather and pavement conditions</li></li></ul><li>Vehicle Data Translator (VDT)<br />VDT 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 />
  147. 147. 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 />
  148. 148. 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
  149. 149. Transportation-specific applications
  150. 150. Broad Weather & Transportation applications</li></li></ul><li>State DOT-based Applications<br /><ul><li>Observation assimilation
  151. 151. Fill in the gaps between fixed stations
  152. 152. Collect real-time pavement temperatures</li></ul>VDT-based data<br /><ul><li>Maintenance Decision Support
  153. 153. What are the current roads conditions?
  154. 154. Accurate pavement temperature modeling
  155. 155. Manage Maintenance Actions
  156. 156. End of Shift Reports
  157. 157. Materials Management</li></li></ul><li>Transportation-specific Applications<br />*<br />VDT-based weather alerts:<br /><ul><li> Impending weather hazards
  158. 158. Alerts from other vehicles
  159. 159. Re-routing</li></ul>*Simulated screen – designed to not distract the driver<br />
  160. 160. 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 />
  161. 161. Weather-related Applications<br />Numerical Weather Modeling<br />Traffic Modeling and Alerting<br />Weather Modeling – complex terrain<br />Other surface transportation users<br />
  162. 162. 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 />
  163. 163. 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 />

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