01023502

Component

https://doi.org/10.3141/1945-08

http://scholar.google.com...rvey&publication_year=2006

Truck traffic information is one of the key inputs in the design and analysis of pavement structures. Until relatively recently, truck traffic data typically were aggregated into equivalent repetitions of a standard axle load for pavement design. The mechanistic pavement design procedures being developed for the California Department of Transportation by the University of California Pavement Research Center will make use of axle-load spectra and other more detailed truck traffic information when incremental design and incremental–recursive design approaches are used. It is necessary to develop traffic inputs in different regions in the state to support the new pavement design procedures. With the weigh-in-motion data collected in California, the truck traffic characteristics were studied for developing default traffic inputs. Traffic composition, temporal and spatial distribution of truck volume, traffic growth rate, vehicle speed, and axle-load spectra were analyzed by intensive and unbiased sampling. Cluster analysis was applied to multivariate responses (e.g., axle-load spectrum) to extract the structure of highway sections in terms of traffic characteristics, which ensured the preservation of useful information during analysis.

01031684

English

pp 61-72

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099536

Print

Figures (10) ; References (6)

Axle load force; Cluster analysis; Data collection; Pavement design; Statistical sampling; Traffic data; Traffic speed; Traffic volume; Truck traffic; Vehicle mix; Weigh in motion

Mechanistic-Empirical Pavement Design Guide

Traffic growth rates

California

Data and Information Technology; Design; Highways; Operations and Traffic Management; Pavements; Planning and Forecasting; I22: Design of Pavements, Railways and Guideways; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Mar 3 2006 10:20AM

• Accuracy Assessment of Traffic Monitoring Devices Vehicle by Vehicle
• Artificial Neural Network Method for Length-Based Vehicle Classification Using Single-Loop Outputs
• Calculation of Travel Time Variability from Loop Detector Data
• Cellular-Based Data-Extracting Method for Trip Distribution
• Detection of Invalid Loop Detector Data in Urban Areas
• Effect of Sensor Spacing on Performance Measure Calculations
• Evaluation of Quartz Piezoelectric Weigh-in-Motion Sensors
• Identification and Correction of Dual-Loop Sensitivity Problems
• Identifying Lane-Mapping Errors at Freeway Detector Stations
• Sensitivity of NCHRP 1-37A Pavement Design to Traffic Input
• Statistical Investigations of Statutory Holiday Effects on Traffic Volumes
• Using Automatic Vehicle Identification Data to Gain Insight into Travel Time Variability and Its Causes