TRC1801 Evaluation of WIM Auto‐Calibration Practices and Parameters Sarah Hernandez Assistant Professor Department of Civil Engineering University of Arkansas 1
Key Objectives 1. Evaluate the Weigh‐in‐Motion (WIM) auto‐calibration practices used by A R DOT. 2. Recommend effective auto‐calibration practices. Fayetteville WIM Site on I‐49 2
Task Overview Task 1 Task 2 Task 3 Task 4 Review WIM Auto‐ Field Data Calibration Implementation Calibration Collection Analyses Activities Methods We are here… 3
Presentation Outline 1. Weigh‐in‐Motion (WIM) Data and Uses 2. WIM Hardware 3. Calibration and Auto‐ Calibration Practices 4. Data Collection and Preliminary Findings Truck traversing load cell WIM sensor (Ref: LTBP Review of WIM, 2016) 4
Weigh‐in‐Motion (WIM) Data and Uses 1.Long term pavement performance (LTPP) 2.Pavement design and maintenance (MEPDG) 3.Bridge design 4.Monitoring highway usage and funding allocation 5.Truck size and weight enforcement FHWA Classification Scheme (Ref: TxDOT, 2017) 5
WIM Hardware: Configuration Quartz piezoelectric sensor with inductive loops Staggered bending plate installation with inductive loops 6 (Ref: WIM Data Analysists Manual)
WIM Hardware: Types Sensor Type Initial Cost Performance Sensitivity Life Span Bending Plate $20,000 +/‐ 5% Minor 6 years Piezoelectric $9,000 +/‐ 10% Temperature 4 years Sensor (Ref: Zhang, L., Evaluation of the Technical and Economic Performance of WIM Sensing Technologies, 2015; 7 FHWA State Practices in WIM, 1997)
WIM Site Locations in Arkansas Number of Functional Class WIM Stations Interstates 11 Freeways 4 Principal Arterials 11 Minor Collector 3 Major Collector 5 Total 34 8
Effects of Poor Calibration Effects of Temperature Effects of Wear and Tear 100 Percent Error in ESAL Value (%) 80 60 40 20 0 ‐60 ‐40 ‐20 0 20 40 60 ‐20 ‐40 ‐60 Calibration Drift (Percent Error, %) Calibration drift shown as shifts in Gross Effects of Calibration Drift on ESAL Values 9 Vehicle Weight (GVW) shifts (Ref: FHWA, 1998)
WIM Calibration and Auto‐Calibration Name Issues Method • Most accurate Test Trucks • Expensive Test Trucks WIM • Time consuming and labor intensive • Must have high volume of trucks Traffic Static WIM Scale Traffic Trucks crossing WIM and static scales • Time consuming • Works well for sites with heavy Traffic Auto‐Calibration WIM tractor‐trailer volumes • Low cost and efficient 10
State of the Practice of Calibration Methods Which calibration method is used? Test Trucks Traffic Trucks Data QC Other Traffic Data 67% 33% 57% 18% Enforcement 6% 59% 29% 6% Both 33% 22% 39% 5% What type of sensors is autocalibration used for? Type I Type II Results from NCHRP Synthesis study “ High Traffic Data 14% 75% Speed Weigh in Motion Enforcement 50% 50% System Calibration” (2008) Both 0% 100% 11
A R DOT Auto‐Calibration Procedure No. of Lane Type of Vehicle Reference Axle Target Value Vehicles 1 (outer lane) Five‐axle semi‐tractor trailers Front axle 10.2 kips 50 2 (inner lane) Passenger Cars Front axle 2.2 kips 50 3 (inner lane) Passenger Cars Front axle 2.3 kips 75 4 (outer lane) Five‐axle semi‐tractor trailers Front axle 10.8 kips 25 12 (Ref: Correspondence with Peek Traffic)
MnDOT Auto‐Calibration Algorithm Adjustment Factors Based Front Axle Weight Reference values on Size of Data Sample stratified by GVW Gross Vehicle Weight (lbs) Desired Front Axle Weight < 32,000 8,500 32,000 – 70,000 9,300 > 70,000 10,400 Percent Deviation Allowed 3.5% Min. Hours Monitored 48 Min. Class 9 Trucks Weighed 250 13 (Ref: NCHRP Synthesis study “ High Speed Weigh in Motion System Calibration”, 2008)
Auto‐Calibration with Truck GPS Data • Truck GPS data available from national “vendors”. • Partnered with • GPS sample used to track same truck across multiple WIM sites. US Coverage Map of Weigh Stations and Agricultural Inspection Stations with Drivewyze coverage 14
GPS Truck Tracking at WIM Sites January February 15
Field Data Collection Lamar Recommended sites based Alma Drivewyze truck GPS: Bald Knob 1.Lamar EB (360009) Dardanelle 2.Lonoke EB (430037) Glen Rose Lonoke 3.Bald Knob NB (730068) 4.Dardanelle SB (750006) 5.Glen Rose SB (301769) 16
Field Data Collection: Procedures Speed‐ Record ID Date Time Array Vehicle No Class mph #‐ Axles 7213 03‐15‐2018 15:42:52 Lane 1 107 6 71 3 7214 03‐15‐2018 15:42:59 Lane 2 198 8 75 4 7215 03‐15‐2018 15:43:40 Lane 1 113 9 68 5 Photo and Static Scale Measurements at Alma Video and WIM Measurements at Lonoke 17
Field Data Collection: Preliminary Findings 18
Field Data Collection: WIM Weight Distributions Lamar Lonoke 19
Field Data Collection: Static Weight Differences Front Axle Weight Front Axle Weight Lamar Lonoke 20
Next Steps • Continue to process field data Alma WIM scale data • Development of traffic stream auto‐calibration parameters • Development of GPS based auto‐calibration Task 1 Task 4 Task 2 Task 3 Review WIM Auto‐ Implementation Field Data Collection Calibration Analyses Calibration Methods Activities 21
Questions? Sarah Hernandez Assistant Professor Department of Civil Engineering University of Arkansas sarahvh@uark.edu 22
Recommend
More recommend
