TRANSPORTATION RESEARCH BOARD Spatial Modeling for Highway Performance Monitoring System Data: Part 2 Tuesday, March 6, 2018 1:00-3:00 PM ET
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Purpose Discuss how to use spatial modeling and statistical tools to enhance the quality and productivity of travel monitoring data. Learning Objectives At the end of this webinar, you will be able to: Describe the HPMS travel data validation rules and create pseudo-code queries that incorporate HPMS based validation rules Understand how to apply standard visualizing tools for the purpose of highlighting and identification of potential problems Understand the need to balance multimodal solutions within the system Understand how to create HPMS data items and summary data using a relational database
TRB WEBINAR: SPATIAL MODELING FOR HIGHWAY PERFORMANCE MONITORING SYSTEM DATA PART 2 - MARCH 6, 2018 Maaza Christos Mekuria, PhD, PE, PTOE Dan P. Seedah, PhD Stephen Cropley 1
2 WEBINAR OUTLINE Spatio-temporal Relationships Spatial Clustering Applications to HPMS Data Items Generation of Summary Tables Ramp Balancing Opportunities and challenges for Probe data Q & A session
RECAP FROM PART 1 WEBINAR 3 Overview of HPMS Description of HPMS Travel Data Items Relationship between HPMS Data items Linear Referencing System Sample Panels and HPMS items Leveraging Spatial Analysis
4 SPATIAL RELATIONSHIPS Dimensionality Adjacency Connectivity Containment B A
TEMPORAL RELATIONSHIPS 5 Time of Day Day of Week Month Year
HIGHWAY PERFORMANCE MONITORING SYSTEM (HPMS) 6 Sample Panels (Sections/Links/Segments) Assign Counts to Monitored Sections Expand to Unmonitored Sections Generate Summaries [Ref. FHWA]
HPMS SAMPLE PANEL GENERATION 7 FHWA HPMS Tool ArcGIS Spatial Oracle PL/SQL QGIS/Python Spatialite/Python/C++ etc. Sample Panels [Adapted from FHWA - CPI Manual 2001]
TOPS GROUP SECTIONS SAMPLE CODES 8 CASE WHEN "aadt"/"thrulanes" <= 2500 THEN 'Very Low' WHEN "aadt"/"thrulanes" <= 5000 THEN 'Low' WHEN "aadt"/"thrulanes" <= 10000 THEN 'Moderate' WHEN "aadt"/"thrulanes" <= 20000 THEN 'High' WHEN "aadt"/"thrulanes" <= 50000 THEN 'Very High' WHEN "aadt"/"thrulanes" > 50000 THEN 'Super High' END
TOPS GROUP SECTIONS 9 QUERY Group all roadways by functional class and order by number of records in each county
TOPS GROUP SECTIONS 10 3500 3000 Number of Segments 2500 2000 1500 1000 500 0 Very Low Low Moderate High Very High Super High Volume Group Per Lane
TOPS GROUP SECTIONS 11 FC_FacilityType_UrbanCode_Low_SampleCode
HPMS VOLUME GROUPS 12 CASE WHEN "aadt" <= 500 THEN 1 WHEN "aadt" <= 1000 THEN 2 WHEN "aadt" <= 2500 THEN 3 WHEN "aadt" <= 5000 THEN 4 WHEN "aadt" <= 10000 THEN 5 WHEN "aadt" <= 17500 THEN 6 WHEN "aadt" <= 27500 THEN 7 WHEN "aadt" <= 42500 THEN 8 WHEN "aadt" <= 62500 THEN 9 WHEN "aadt" <= 87500 THEN 10 WHEN "aadt" <= 125000 THEN 11 WHEN "aadt" > 125000 THEN 12 END
13 HPMS VOLUME GROUPS 2500 Number of HPMS Volume Group Segments 2000 Number of Segments 1500 1000 500 0 0 2 4 6 8 10 12 14 HPMS Volume Group
HPMS VOLUME GROUPS ESTIMATION FORMUAR 14 Table 6.2 Confidence Level Z Z Squared 90 Percent 1.645 2.706 80 Percent 1.282 1.644 70 Percent 1.04 1.082 [Ref. FHWA Field manual 2016]
HPMS TOPS CLUSTERS 15 700 Number of TOPS Segment Groups 600 500 400 300 200 100 0 0 20 40 60 80 100 120 140
HPMS LINK TRAVEL DATA ASSIGNMENT 16
COUNTED HPMS SECTIONS FOR A GIVEN YEAR 24/7 continuous counters Short Term Counters? No Counters 17 Section Boundaries?????
18 2016 HPMS FIELD MANUAL GUIDANCE - VOLUME DATA For AADT (Pages 4-51 and 4-52) For two-way facilities, provide the bidirectional AADT; for one-way roadways, and ramps, provide the directional AADT. All AADTs shall reflect application of day of week, seasonal, and axle correction factors, as necessary; no other adjustment factors shall be used. Growth factors shall be applied if the AADT is not derived from current year counts. AADTs for the NHS, Interstate, Principal Arterial (OFE, OPA) roadway sections shall be based on traffic counts taken on a minimum threeyear cycle. AADTs for the non-principal Arterial System (i.e., Minor Arterials, Major Collectors, and Urban Minor Collectors) can be based on a minimum six-year counting cycle.
TRAVEL MONITORING : AADT LINK ASSIGNMENT -- Year 19 Sample Panels Data Attr./ Spatial Merge Volume Stations Are all Sections Assigned? No Earlier year meets FC criterion? Yes Yes No END Section to be revised for HPMS Submission
HPMS TRAFFIC STATIONS ASSIGNMENT RESULTS 20
AADT COUNT ASSIGNMENT - MAUI 21
AADT COUNT ASSIGNMENT - KAUAI 22
AADT COUNT ASSIGNMENT - KAUAI 23
AADT COUNT ASSIGNMENT - KAUAI 24
2016 HPMS FIELD MANUAL GUIDANCE CLASS DATA 25 For Single Unit and Combination Unit AADT (Pages 4-52 to 4-57) AADT values shall be updated annually to represent current year data. Section specific measured values are requested based on traffic counts taken on a minimum three-year cycle. If these data are not available, values derived from classification station data on the same route, or on a similar route with similar traffic characteristics in the same area can be used
TRAVEL MONITORING : CLASS LINK ASSIGNMENT 26 Find the adjacent section on same route with similar FC, thru lanes, and urban code, and assign class data* * Preference is given to permanent stations over shortterm stations
AADT CLASS COUNT ASSIGNMENT 27
CONSIDERATIONS FOR APPLYING GROWTH RATES 28 Growth rates are applied on a statewide level Further investigation required on applying growth rates based on: Urban area codes Rate computed from just the permanent stations Considerations for: Low volume rural roadways Large area vs. small area Sections that traverse multiple boundaries e.g. interstates
CONSIDERATIONS FOR USING JOINS 29 Attribute Joins Attribute joins can use MILEPOINT and ROUTE information with respect to Section start and end mile points. More accurate, if and only if, data attributes are correctly set in both the Station and Link records. Spatial Joins Point is typically matched to a target line which is closest/intersects/contains the point Uses a search radius or tolerance Issues include wrongly assigned joins e.g. ramp counts with coordinates placed close to the interstate Best compromise is a spatial join with attribute join
AADT CLASS COUNT ASSIGNMENT 30 The result of the processing is HPMS Network with AADT, Class, Peak Hour Count Data assigned to links
AADT ASSIGNMENT COMPARISON 31
AADT ASSIGNMENT COMPARISON 32
TRUCK AADT COMPUTATION TRUCK AADT for each section 33 Using the Permanent Stations Adjust Short Term Class Stations Truck Single Unit = (Daily Single Unit Trucks (Class 5-7) + Bus ) Percent Peak Combination = (Daily Combination Unit Trucks, FHWA class 8-13 )
AADT K, D FACTOR ASSIGNMENT 34 Both K-factor ( the design hour volume - 30 th largest hourly volume for a given calendar year) as a percentage of AADT) and the Directional Factor (D) ( the percent of design hour volume - 30th largest hourly volume for a given calendar year flowing in the higher volume direction) are assigned to AADT links via permanent station factor groups.
VALIDATION FOR TRAFFIC DATA ITEMS 35 FHWA Validation Queries are heavily dependent on user table structure, yet they are as simple as a single query once the table data are generated. Samples will be provided when requested.
PEAK HOUR ASSIGNMENT Peak Hour AADT for each section 36 Using the Permanent Stations Adjust Short Term Class Stations Percent Peak Single = (Peak Hour Daily Single Unit Trucks + Bus ) / (AADT ) Percent Peak Combination = (Peak Hour Daily Combination Unit Trucks ) / (AADT )
VEHICLE SUMMARY 37 Step 1 - Create Average View of the AADT Link Class data table by FHWA Highway System Group create view vwhpms2016avgvehicleclass as Select Year,t2.State_Code, t2.fs_group, Round(avg(t1.cycles),2) Pct_MC, round(avg(t1.pc),2) Pct_Cars, round(avg(t1.vcls3),2) Pct_Lgt_Trucks, round(avg(t1.bus),2) Pct_Buses,round(avg(t1.SU),2) Pct_SU_Trucks, round(avg(t1.cu),2) Pct_CU_Trucks from HPMS2016VEHICLECLASS t1, FHWA_HIGHWAY_Class_Group t2 where t1.func_class = t2.func_class group by t1.year,t2.state_code, t2.fs_group Order by t2.year, t2.state_code, t2. FS_GROUP ;
VEHICLE SUMMARY 38 Step 2 - Create Summary View of the AADT Link Class data by FHWA Highway System Group create view vwhpms2016sumvehicleclass as t2.year_record, t2.state_code, t2.fs_group, Round((t2.Pct_MC+ t2.pct_cars + t2.pct_lgt_trucks+ t2.pct_buses + t2.pct_su_trucks + t2.pct_cu_trucks),2) TotPct from vwhpms2016avgvehicleclass t2 Order by t2.year_record,t2.state_code,t2.fs_group;
VEHICLE SUMMARY 39 Step 3 - Create Export for FHWA by FHWA Highway System Group select t2.year_record, t2.state_code, t2.fs_group, Round((t2.Pct_MC/t1.TotPct*100),2) Pct_MC, Round((t2.Pct_Cars/t1.TotPct*100),2) Pct_Cars, Round((t2.Pct_Lgt_Trucks/t1.TotPct*100),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.TotPct*100),2) Pct_Buses, Round((t2.Pct_SU_Trucks/t1.TotPct*100),2) Pct_SU_Trucks, Round((t2.Pct_CU_Trucks/t1.TotPct*100),2) Pct_CU_Trucks from vwhpms2016avgvehicleclass t2 inner join vwhpms2016sumvehicleclass t1 on t1.fs_group = t2.fs_group Order by t2.year_record, t2.state_code, t2.fs_group;
VEHICLE SUMMARY TO BE EXPORTED TO FHWA 40
VEHICLE SUMMARY VMT BASED 41 Step 1 - Create Summary View of the Link VMT Class data by FHWA Highway System create view vwhpms2016vehicleclassvmtsum as select t2.year_record, '15' State_Code, t2.fs_group, Round(sum(t1.cycles*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_MC, round(sum(t1.pc*(t2.emp-t2.bmp)*t2.aadt),2) Pct_Cars, Round(sum(t1.VCLS3*(t2.EMP-t2.BMP)*t2.AADT),2) Pct_Lgt_Trucks, round(sum(t1.bus*(t2.emp-t2.bmp)*t2.aadt),2) Pct_Buses, round(sum((t1.su)*(t2.emp-t2.bmp)*t2.aadt),2) Pct_SU_Trucks, round(sum(t1.cu*(t2.emp-t2.bmp)*t2.aadt),2) Pct_CU_Trucks from HPMS2016VEHICLECLASS t1, HPMS2016AADT t2, FHWAClass t3 where t2.route = t1.route_id and t2.bmp = t1.bmp and t2.emp = t1.emp and t2.fs_group = t3. FS_GROUP group by t2.year_record, t3. FS_GROUP Order by t2.year_record, t3. FS_GROUP ;
VEHICLE SUMMARY VMT BASED 42 Step 2 - Create Summary View of the Total VMT Class data create view vwhpms2016vmtsum as select t2.year_record, '15' State_Code, t3. FS_GROUP, Round(sum((t2.EMPt2.BMP)*t2.AADT),2) VMT from HPMS2016AADT t2, FHWAClass t3 where t2.fs_group = t3. FS_GROUP group by t2.year_record, t3. FS_GROUP Order by t2.year_record, t3. FS_GROUP;
VEHICLE SUMMARY VMT BASED 43 Step 3 Divide Link VMT (Step 1) by VMT Totals (Step 2) create view vwhpms2016vmtvehicleclasssum as select t2.year_record, t2.state_code, t2.fs_group, Round((t2.Pct_MC/t1.VMT),2) Pct_MC, Round((t2.Pct_Cars/t1.VMT),2) Pct_Cars, Round((t2.Pct_Lgt_Trucks/t1.VMT),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.VMT),2) Pct_Buses, Round((t2.Pct_SU_Trucks/t1.VMT),2) Pct_SU_Trucks, Round((t2.Pct_CU_Trucks/t1.VMT),2) Pct_CU_Trucks from vwhpms2016vehicleclassvmtsum t2 inner join vwhpms2016vmtsum t1 on t1.fs_group = t2.fs_group Order by t2.year_record, t2.state_code, t2.fs_group ;
VEHICLE SUMMARY VMT BASED Step 4 - Create Total view of the AADT Link Class data by FHWA Highway System Group from Step 3 44 create view vwhpms2016sumvehicleclass as t2.year_record, t2.state_code, t2.fs_group, Round((t2.Pct_MC+ t2.pct_cars + t2.pct_lgt_trucks+ t2.pct_buses + t2.pct_su_trucks + t2.pct_cu_trucks),2) TotPct from vwhpms2016vmtvehicleclasssum t2 Order by t2.year_record,t2.state_code,t2.fs_group;
VEHICLE SUMMARY VMT BASED 45 Step 5 - Create Export for FHWA Step 3 / Step 4 select t2.year_record, t2.state_code, t2.fs_group, Round((t2.Pct_MC/t1.TotPct*100),2) Pct_MC, Round((t2.Pct_Cars/t1.TotPct*100),2) Pct_Cars, Round((t2.Pct_Lgt_Trucks/t1.TotPct*100),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.TotPct*100),2) Pct_Buses, Round((t2.Pct_SU_Trucks/t1.TotPct*100),2) Pct_SU_Trucks, Round((t2.Pct_CU_Trucks/t1.TotPct*100),2) Pct_CU_Trucks from vwhpms2016avgvehicleclass t2 inner join vwhpms2016sumvehicleclass t1 on t1.fs_group = t2.fs_group Order by t2.year_record, t2.state_code, t2.fs_group ;
VEHICLE SUMMARY VMT BASED Step 5 - Create Export for FHWA Step 3 / Step 4 46 select t2.year_record, t2.state_code, t2.fs_group, Round((t2.Pct_MC/t1.TotPct*100),2) Pct_MC, Round((t2.Pct_Cars/t1.TotPct*100),2) Pct_Cars, Round((t2.Pct_Lgt_Trucks/t1.TotPct*100),2) Pct_Lgt_Trucks, Round((t2.Pct_Buses/t1.TotPct*100),2) Pct_Buses, Round((t2.Pct_SU_Trucks/t1.TotPct*100),2) Pct_SU_Trucks, Round((t2.Pct_CU_Trucks/t1.TotPct*100),2) Pct_CU_Trucks from vwhpms2016avgvehicleclass t2 inner join vwhpms2016sumvehicleclass t1 on t1.fs_group = t2.fs_group Order by t2.year_record, t2.state_code, t2.fs_group ;
VEHICLE SUMMARY TO BE EXPORTED TO FHWA 47 Vehicle Summaries Simple Average Year_Record State_Code FS_GROUP Pct_MC Pct_Cars Pct_Lgt_Trucks Pct_Buses Pct_SU_Trucks Pct_CU_Trucks TotPct 2016 15 110 1.89 70.16 22.47 0.99 3.62 0.87 100 2016 15 200 0.82 69.22 25.22 1.07 2.86 0.81 100 2016 15 210 2.68 72.41 19.46 1.39 2.78 1.28 100 2016 15 300 1.23 67.03 28.16 0.52 2.70 0.36 100 2016 15 310 2.17 71.65 23.15 0.69 1.82 0.52 100 VMT Based Year_Record State_Code FS_GROUP Pct_MC Pct_Cars Pct_Lgt_Trucks Pct_Buses Pct_SU_Trucks Pct_CU_Trucks TotPct 2016 15 110 1.36 66.83 24.80 1.37 4.82 0.82 100 2016 15 200 0.97 68.49 25.74 1.09 2.81 0.90 100 2016 15 210 3.05 71.23 20.14 1.66 2.68 1.24 100 2016 15 300 0.87 69.97 27.11 0.34 1.46 0.25 100 2016 15 310 2.27 71.66 23.41 0.65 1.50 0.51 100
CLUSTER PROCESSING 48
HPMS SAMPLE PANEL CLUSTER SIZE = 50 49
HPMS SAMPLE PANEL CLUSTER SIZE = 25 50
HPMS COUNT STATION SELECTION 51
RAMP BALANCING PROCEDURES 52
PURPOSE OF RAMP BALANCING Need to report traffic (AADT) on all interstate mainline sections. Hard to count some sections, so Ramp balancing can be used to estimate traffic volumes on roads where portable counts cannot be performed safely (TMG 2017) Its called ramp balancing because AADT values are adjusted so they balance along the computed sections. 53
DIFFERENCE BETWEEN INTERCHANGE AND MAINLINE RAMP BALANCING 54 This needs to be spelled out! WE ARE DISCUSSING THIS!! Interchange Ramp Balancing (Computes ramp volumes at interchanges) Mainline Ramp Balancing
A TYPICAL SETUP 55 Anchor Points (Continuous, Known) Ramp On (Known) Ramp Off (Known) Computed Mainline
SETTING UP FROM SPATIAL ANALYSIS 56 Continuous counters are called Anchor points. They are not changed by the computation. Determine which ramps are part of a given computation. Some ramps are actually connectors. They are On for computing one highway segment but Off for another. The order in which On and Off ramps appear in the computation is essential. Use mile points and look closely at the map. The compass directions (N, S, E, W) that ramps flow towards are essential in computation
EXAMPLE: COMPUTING VOLUMES 57 Point Counted COMPUTED CHANGE CCS 1 START Ramp 1 ON MID POINT Ramp2 OFF Ramp3 ON CCS2 END 11,995 923 12,918-134 (-)1,053 11,865-153 786 12,651-115 13,053 DIFF -402 * The Ramp Off below ramp1 is ignored in this example for simplicity. It would normally be included.
58 EXAMPLE: COMPUTING VOLUMES Point Counted Adjusted GEH Accuracy CCS 1 START 11,995 Ramp 1 ON 923 1,057 1.35 MID POINT 13,052 Ramp2 OFF Ramp3 ON CCS2 END (-)1,053 900 1.55 786 901 1.25 13,053 GEH Statistic 1.0 Excellent 2.0 Good 5.0 Acceptable 10.0 Rubbish
CONSIDERATIONS FROM SPATIAL ANALYSIS 59 - Direction of traffic - Order of ramps - Ramp counts may have happened on different days - Missing ramp values must be imputed: They are not calculated here 2017 Highway Computation Points Georgia DOT
RESOURCES USED IN EXAMPLES 60 TMG 6.2.2 AADT Reporting on Mainlines and Ramps Source: http://geocounts.com/visual/rampbalancing (Example 4 is from the 2001 TMG)
OPPORTUNITIES AND CHALLENGES FOR PROBE DATA 61
TRAVEL MONITORING: THE IDEAL ENVIRONMENT 62
TRAVEL MONITORING: THE IDEAL ENVIRONMENT 24/7 continuous counters 63
64 TRAVEL MONITORING: AN EVEN BETTER ENVIRONMENT Autonomous Self-Reporting Vehicles
TRAVEL MONITORING: PROBE DATA 65
PROBE DATA OPPORTUNITIES 66 Cost Coverage Frequency Origin/Destination
PROBE DATA CHALLENGES 67 Cell tower vs. GPS vs. Location-Based Data Sample sizes for different functional classes Urban vs. rural settings Black box vs. open estimation algorithms HPMS six vehicle class data requirement Validation
REFERENCES 68 HPMS 2016 Field Manual Turner, Shawn, and Pete Koeneman. Using Mobile Device Samples to Estimate Traffic Volumes. No. MN/RC 2017-49. Minnesota. Dept. of Transportation. Research Services & Library, 2017. I-95 Corridor Coalition s Vehicle Probe Project (VPP)
CONTACT INFORMATION 69 Maaza Christos Mekuria, PhD, PE, PTOE Hawaii Department of Transportation maaza.c.mekuria@hawaii.gov Dan P. Seedah, PhD Asst. Research Scientist, Texas A&M Transportation Institute d-seedah@tti.tamu.edu Stephen Cropley Director, Technology Transmetric America Inc. stephen.cropley@transmetric.com
70 APPENDIX A : HPMS NUMBER OF SAMPLES SELECTION 2500 Number of HPMS Volume Group Segments 2000 Number of Segments 1500 1000 500 0 0 2 4 6 8 10 12 14 HPMS Volume Group
HPMS NUMBER OF SAMPLES SELECTION 71
HPMS NUMBER OF SAMPLES SELECTION SELECT t1.yr, t1.hpmsvgrp, Count(t1.countycode) NumRecs, Round(stddev_samp(t1.AADT)/avg(t1.AADT),3) CV, ceil((pow(1.64,2) * pow(stddev_samp(t1.aadt)/avg(t1.aadt),2)/(pow(0.05,2)))/ (1+(1/count(t1.Id))*(((pow(1.64,2) * pow(stddev_samp(t1.aadt)/avg(t1.aadt),2)/(pow(0.05,2))-1))))) NumSamp FROM HiDOTRtesHPMS2016BIKEFLDSMDS t1 GROUP BY t1.yr, t1.hpmsvgrp ORDER BY t1.yr,t1.hpmsvgrp,stddev_samp(t1.aadt)/avg(t1.aadt) desc,count(t1.countycode) Desc 72 Year HPMSVGrp NumRecs CV NumSamp 2016 1 68 0.503 273 2016 2 138 0.157 27 2016 3 1165 0.217 51 2016 4 928 0.185 37 2016 5 1837 0.183 37 2016 6 2249 0.156 27 2016 7 1328 0.141 22 2016 8 924 0.124 17 2016 9 397 0.112 14 2016 10 118 0.085 8 2016 11 89 0.073 6 2016 12 88 0.195 41
HPMS NUMBER OF SAMPLES SELECTION RURAL AREAS SELECT t1.yr,t1.hpmsvgrp, floor(avg(t1.funclass)) FunClsAvg, Count(t1.countycode) NumRecs, Round(stddev_samp(t1.AADT)/avg(t1.AADT),3) CV, ceil((pow(1.28,2) * pow(stddev_samp(t1.aadt)/avg(t1.aadt),2)/(pow(0.1,2)))/ (1+(1/count(t1.Id))*(((pow(1.28,2) * pow(stddev_samp(t1.aadt)/avg(t1.aadt),2)/(pow(0.1,2))-1))))) NumSamp FROM HiDOTRtesHPMS2016BIKEFLDSMDS t1 where t1.urbcode = 99999 GROUP BY t1.yr, t1.hpmsvgrp ORDER BY t1.yr,t1.hpmsvgrp,stddev_samp(t1.aadt)/avg(t1.aadt) desc,count(t1.countycode) Desc 73 yr HPMSVGrp FunClsAvg NumRecs CV NumSamp 2016 1 5 42 0.508 43 2016 2 5 68 0.147 4 2016 3 5 666 0.184 6 2016 4 4 181 0.187 6 2016 5 3 284 0.172 5 2016 6 3 134 0.15 4 2016 7 3 88 0.108 2 2016 8 4 7 0 0
HPMS NUMBER OF SAMPLES SELECTION RURAL + SMALL URBAN COLLECTOR AND ABOVE SELECT t1.yr,t1.hpmsvgrp, floor(avg(t1.funclass)) FunClsAvg, Count(t1.countycode) NumRecs, Round(stddev_samp(t1.AADT)/avg(t1.AADT),3) CV, ceil((pow(1.28,2) * pow(stddev_samp(t1.aadt)/avg(t1.aadt),2)/(pow(0.1,2)))/ (1+(1/count(t1.Id))*(((pow(1.28,2) * pow(stddev_samp(t1.aadt)/avg(t1.aadt),2)/(pow(0.1,2))-1))))) NumSamp FROM HiDOTRtesHPMS2016BIKEFLDSMDS t1 where t1.urbcode in ( 99999,99998) and t1.funclass >= 5 GROUP BY t1.yr, t1.hpmsvgrp ORDER BY t1.yr,t1.hpmsvgrp,stddev_samp(t1.aadt)/avg(t1.aadt) desc,count(t1.countycode) Desc 74 Yr HPMSVGrp FunClsAvg NumRecs CV NumSamp 2016 1 5 60 0.463 36 2016 2 5 100 0.103 2 2016 3 5 779 0.173 5 2016 4 5 367 0.186 6 2016 5 5 395 0.19 6 2016 6 5 238 0.12 3 2016 7 6 10 0.172 5 2016 8 7 4 0 0
Today s Participants Coco Briseno, California Department of Transportation, coco.briseno@dot.ca.gov Maaza Mekuria, Hawaii Department of Transportation, maaza.c.mekuria@hawaii.gov Daniel Seedah, Texas A&M Transportation Institute, d-seedah@tti.tamu.edu Stephen Cropley, Transmetric America, Inc., stephen.cropley@transmetric.com
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