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Evaluation of Truck Tonnage Estimation Methodologies

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Evaluation of Truck Tonnage Estimation Methodologies Final Report Contract No. BDV27-977-15 Evaluation of Truck Tonnage Estimation Methodologies Prepared for: Florida Department of Transportation, Forecasting & Trends Office 605 Suwannee Street, Tallahassee, Florida 32399 Prepared by: Evangelos I. Kaisar, Ph.D., Professor, & Director Dan Liu, Research Associate Taraneh Ardalan, Research Assistant Freight Mobility Research Institute (FMRI) Department of Civil, Environmental and Geomatics Engineering Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431 [email protected] FDOT Project Manager: Monica Zhong Mobility Measures Program Coordinator (FDOT) [email protected] FDOT Co-Project Manager: Frank Tabatabaee Systems Transportation Modeler (FDOT) [email protected] December 2019 DISCLAIMER The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the State of Florida Department of Transportation. ii SI MODERN METRIC (CONVERSION FACTORS) APPROXIMATE CONVERSIONS TO SI UNITS Symbol When You Know Multiply By To Find Symbol LENGTH in inches 25.4 millimeters mm ft feet 0.305 meters m yd yards 0.914 meters m mi miles 1.61 kilometers km AREA in2 square inches 645.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 yd2 square yards 0.836 square meters m2 ac acres 0.405 hectares ha mi2 square miles 2.59 square kilometers km2 VOLUME fl oz fluid ounces 29.57 milliliters mL gal gallons 3.785 liters L ft3 cubic feet 0.028 cubic meters m3 yd3 cubic yards 0.765 cubic meters m3 NOTE: volumes greater than 1000 L shall be shown in m3 MASS oz ounces 28.35 grams g lb pounds 0.454 kilograms kg T short tons (2000 lb) 0.907 megagrams ("metric ton") Mg FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/in2 poundforce per square inch 6.89 kilopascals kPa iii APPROXIMATE CONVERSIONS FROM SI UNITS LENGTH mm millimeters 0.039 inches in m meters 3.28 feet ft m meters 1.09 yards yd km kilometers 0.621 miles mi AREA mm2 square millimeters 0.0016 square inches in2 m2 square meters 10.764 square feet ft2 m2 square meters 1.195 square yards yd2 ha hectares 2.47 acres ac km2 square kilometers 0.386 square miles mi2 VOLUME mL milliliters 0.034 fluid ounces fl oz L liters 0.264 gallons gal m3 cubic meters 35.314 cubic feet ft3 m3 cubic meters 1.307 cubic yards yd3 MASS g grams 0.035 ounces oz kg kilograms 2.202 pounds lb Mg megagrams ("metric ton") 1.103 short tons (2000 lb) T FORCE and PRESSURE or STRESS N newtons 0.225 poundforce lbf kPa kilopascals 0.145 poundforce per square inch lbf/in2 SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. (Revised March 2003) iv TECHNICAL REPORT DOCUMENTATION PAGE 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 4. Title and Subtitle 5. Report Date Evaluation of Truck Tonnage Estimation Methodologies December 2019 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Evangelos I. Kaisar, Dan Liu, Taraneh Ardalan 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) Freight Mobility Research Institute Florida Atlantic University 11. Contract or Grant No. 777 Glades Rd., Bldg. 36, Boca Raton, FL 33431 BDV27-977-15 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered Florida's Mobility Measures Program (MMP) Final Report Florida Department of Transportation January 2019-December 2019 605 Suwannee Street MS-26 Tallahassee, FL 32399-0450 USA 14. Sponsoring Agency Code 15. Supplementary Notes Ms. Monica Zhong, Mobility Measures Program Coordinator of the Forecasting & Trends Office at the Florida Department of Transportation (FDOT), served as the Project Manager for this project, and Mr. Frank Tabatabaee, Systems Transportation Modeler of the Forecasting & Trends Office at the FDOT, served as the Co-Project Manager for this project 16. Abstract Truck tonnage is an important mobility measure to evaluate transportation system performance, which can identify how much freight is moved. This study develops a methodology for truck tonnage estimation, which consists of three main parts: (i) Weigh in Motion (WIM) sites clustering, (ii) truck volume estimation based on Telemetric Traffic Monitoring sites (TTMS) data, and (iii) average truck tonnage calculation for WIM site. In this methodology, WIM sites were divided into different groups by applying the K-nearest neighbor algorithm, based on the truck tonnage distributions and average truck volumes. A clustering classification was then fitted to the TTMS, based on truck volumes and distances to the WIM sites. To avoid the double-counting issue, strategic TTMS were selected, considering the locations of the sites, the daily factor (commonly known as the D factor), and the truck factor (commonly known as the T factor). Afterwards, vehicle classes in WIM sites were categorized by a K-mean clustering method based on the vehicle loads for calculating the average truck tonnage. Furthermore, the empty vehicle weight for different vehicle classes was estimated, applying Gaussian distribution of gross tonnage. At last, a weighted mean method was applied to calculate the average truck tonnage. The aforementioned methodology was tested in a case study of truck tonnage estimation in Florida using WIM data in 2012 and 2017, and compared with the method using the 2012 data released by the Freight Analysis Framework (FAF). The proposed model might shed light on the statewide performance evaluation of freight mobility. 17. Key Word 18. Distribution Statement Freight Tonnage Estimation, K-Nearest Neighbors Clustering, Weigh in Motion (WIM), Strategic Telemetered Traffic Monitoring Site (TTMS) Selection 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 90 Form DOT F 1700.7 (8-72) Reproduction of completed page authorized v ACKNOWLEDGEMENTS The Florida Atlantic University research team would like to acknowledge the contributions from the Florida Department of Transportation. We are particularly grateful to Ms. Monica Zhong, Mobility Measures Program Coordinator of the FDOT Forecasting & Trends Office and the Project Manager of this project, and Mr. Frank Tabatabaee, Systems Transportation Modeler of the FDOT Forecasting & Trends Office and the Co-Project Manager of this project, for their guidance and support throughout the project. Furthermore, we would like to extend our special thanks to Freight Mobility Research Institute (FMRI) research team for their guidance and support during the entire project. vi EXECUTIVE SUMMARY Truck tonnage is an important mobility metric to determine the quality of the transport system. For freight transport planning, traffic control, weight compliance, and transport infrastructure layout, the estimate of truck tonnage is needed. This paper establishes a truck tonnage estimation methodology consisting of three main components: Weight in Motion (WIM) clustering sites, truck volume estimate based on data from Telemetric Traffic Monitoring Sites (TTMS), and average truck tonnage measurement for WIM sites. First, according to the truck tonnage distribution and average truck volume, WIM sites were divided into different groups based on the application of the K-nearest neighbor algorithm. Then, based on truck volume and distances to the WIM sites, a clustering classification was fitted to the TTMS. In order to avoid the double-counting issue, strategic TTMS was selected taking into account site locations, the D factor and the T factor. Vehicle groups were subsequently classified at WIM sites using a K-mean clustering process based on the vehicle loads to calculate the average tonnage of the truck. In addition, the empty vehicle weight was measured for different vehicle categories, applying Gaussian gross tonnage distribution. Finally, the average truck tonnage was calculated using a weighted mean method. The above methodology was applied to the 2012 and 2017 WIM data as a case study of truck tonnage estimation in Florida. It was validated against FDOT’s current method using the 2012 FAF data. The proposed model could shed light on freight mobility's statewide performance evaluation. vii Table of Contents DISCLAIMER ................................................................................................................................ ii MODERN METRIC (CONVERSION FACTORS) ...................................................................... iii TECHNICAL REPORT DOCUMENTATION PAGE .................................................................. v ACKNOWLEDGEMENTS ........................................................................................................... vi EXECUTIVE SUMMARY .......................................................................................................... vii List of Figures ................................................................................................................................. x List of Tables ................................................................................................................................ xii List of Acronyms ......................................................................................................................... xiii 1. Introduction ......................................................................................................................... 1 1.1. Background .............................................................................................................................
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