01341176

Component

https://doi.org/10.3141/2256-21

http://scholar.google.com...rown&publication_year=2011

Characterizing traffic and developing accurate and desirable traffic inputs for the new "Mechanistic–Empirical Pavement Design Guide" (MEPDG) are critical but challenging activities. The purpose of this study was to develop a process for characterizing traffic inputs in support of the new MEPDG for the state of Michigan. These traffic characteristics include monthly distribution factors, hourly distribution factors, truck traffic classifications, axle groups per vehicle, and axle load distributions for different axle configurations. Axle weight and vehicle classification data were obtained from 44 weigh-in-motion and classification stations located throughout the state of Michigan to develop Level 1 (site-specific) traffic inputs. Cluster analyses were conducted to group sites with similar characteristics for development of Level 2 (regional) inputs. Finally, data from all sites were averaged to establish the statewide Level 3 inputs. The effects of the developed hierarchical traffic inputs on the predicted performance of rigid pavements were investigated with the MEPDG models. An algorithm based on discriminant analysis was developed to acquire the appropriate Level 2 traffic characteristic inputs for pavement design. For pavement analysis and design, it is recognized that site-specific data should be used wherever available. For projects in which site-specific data are not available, it is necessary to know whether Level 2 or Level 3 data are acceptable at a minimum for design. The MEPDG was used to investigate the impact of traffic input levels on predicted pavement performance for rigid pavements. The results of the analysis showed that for pavement design in Michigan, statewide averages should be used instead of MEPDG Level 3 data.

01362484

11-2575

English

pp 179-190

2011

Transportation Research Record: Journal of the Transportation Research Board

9780309222921

Print

Figures (6) ; References (15) ; Tables (9)

Axle load force; Cluster analysis; Pavement design; Pavement performance; Rigid pavements; Traffic characteristics; Traffic data; Truck traffic; Vehicle classification; Weigh in motion

Mechanistic-Empirical Pavement Design Guide

Michigan

Data and Information Technology; Design; Highways; Pavements; I22: Design of Pavements, Railways and Guideways

TRIS, TRB

Feb 17 2011 6:12PM

• Algorithm for Finding Optimal Paths in a Public Transit Network with Real-Time Data
• Analysis of Weigh-in-Motion Data for Truck Weight Grouping in "Mechanistic–Empirical Pavement Design Guide"
• Automated Quality Assurance Methodology for Archived Transit Data from Automatic Vehicle Location and Passenger Counting Systems
• Axle Load Distribution for Mechanistic–Empirical Pavement Design in North Carolina: Multidimensional Clustering Approach and Decision Tree Development
• Capturing Transportation Infrastructure Performance: Data Availability, Needs, and Challenges
• Centrality Characteristics of Road Network Patterns of Traffic Analysis Zones
• Counting Motorcycles and Estimating Motorcycle Vehicle Miles of Travel
• Detection and Correction of Inductive Loop Detector Sensitivity Errors by Using Gaussian Mixture Models
• Development of Traffic Inputs for New "Mechanistic–Empirical Pavement Design Guide": Case Study
• Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings
• Estimating Route Choice and Travel Time Reliability with Field Observations of Bluetooth Probe Vehicles
• Estimation of Dynamic Traffic Densities for Official Statistics: Combined Use of Data from Global Positioning System and Loop Detectors
• Evaluation of Nonintrusive Technologies for Traffic Detection, Phase 3
• Forecasting Traffic Loads for Mechanistic–Empirical Pavement Design
• Future Scenarios for Traffic Information and Management
• Identifying and Correcting Pulse-Breakup Errors from Freeway Loop Detectors
• Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials
• Is Usual Share of Commuting Mode Always Greater Than Its Actual Share?
• License Plate Image Binarization Based on Chromatic Information and Fuzzy C-Mean Approach
• Network Stratification Method by Travel Time Variation
• Travel Time Prediction Using k Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System