Truck Traffic and Truck Axle Load Spectra on Interstate Highways for Mechanistic-Empirical Pavement Design

2020 ◽  
Author(s):  
Xiaoqiang Hu ◽  
Jieyi Bao ◽  
Yi Jiang ◽  
Shuo Li ◽  
Tommy Nantung
Keyword(s):  
Pavement Design ◽  
Truck Traffic ◽  
Interstate Highways ◽  
Load Spectra

scholarly journals Creation of Truck Axle Load Spectra Using Weigh-in-Motion Data

2010 ◽  
Vol 47 (4) ◽  
Author(s):  
Yi Jiang ◽  
Shuo Li ◽  
Tommy Nantung ◽  
Kirk Mangold ◽  
Scott A. MacArthur
Keyword(s):  
Design Method ◽  
Pavement Design ◽  
Smooth Transition ◽  
Traffic Information ◽  
Truck Traffic ◽  
Motion Data ◽  
Load Spectra ◽  
Weigh In Motion ◽  
Adjustment Factors ◽  
Axle Loads

To assure a smooth transition from the existing pavement design methods to the new mechanistic-empirical design method in the Indiana Department of Transportation, a study was conducted to create truck traffic inputs and axle load spectra of major interstate and state-owned highways in Indiana. The existing pavement design method is based on the equivalent single-axle loads (ESAL), which converts wheel loads of various magnitudes and repetitions to an equivalent number of "standard" or "equivalent" axle loads. The new design method uses axle load spectra as the measure of vehicle loads on pavements. These spectra represent the percentage of the total axle applications within each load interval for single, tandem, tridem, and quad axles. In this study, the truck traffic and axle load spectra were developed based on the historical traffic data collected at 47 sites with weigh-in-motion technology. The truck traffic information includes hourly, daily, and monthly distributions of various types of vehicles and corresponding adjustment factors, the distributions of the number of axles of each type of truck, the weights of the axles, the spaces between the axles, the proportions of vehicles on roadway lanes, and the proportions of vehicles in driving directions. This paper presents the truck traffic and axle load spectra generated from the weigh-in-motion sites as required by the new pavement design method.

scholarly journals Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide

2020 ◽  
Author(s):  
Jieyi Bao ◽  
Xiaoqiang Hu ◽  
Cheng Peng ◽  
Yi Jiang ◽  
Shuo Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Critical Role ◽  
Fatigue Cracking ◽  
Pavement Design ◽  
Easy Access ◽  
Traffic Data ◽  
Truck Traffic ◽  
Load Spectra ◽  
Design Guide ◽  
Input Module

The Mechanistic-Empirical Pavement Design Guide (MEPDG) has been employed for pavement design by the Indiana Department of Transportation (INDOT) since 2009 and has generated efficient pavement designs with a lower cost. It has been demonstrated that the success of MEPDG implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Vehicular traffic loading is one of the key factors that may cause not only pavement structural failures, such as fatigue cracking and rutting, but also functional surface distresses, including friction and smoothness. In particular, truck load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to update the traffic design input module, and thus to improve the current INDOT pavement design procedures. Efforts were made to reclassify truck traffic categories to accurately account for the specific axle load spectra on two-lane roads with low truck traffic and interstate routes with very high truck traffic. The traffic input module was updated with the most recent data to better reflect the axle load spectra for pavement design. Vehicle platoons were analyzed to better understand the truck traffic characteristics. The unclassified vehicles by traffic recording devices were examined and analyzed to identify possible causes of the inaccurate data collection. Bus traffic in the Indiana urban areas was investigated to provide additional information for highway engineers with respect to city streets as well as highway sections passing through urban areas. New equivalent single axle load (ESAL) values were determined based on the updated traffic data. In addition, a truck traffic data repository and visualization model and a TABLEAU interactive visualization dashboard model were developed for easy access, view, storage, and analysis of MEPDG related traffic data.

Evaluation of Clustered Traffic Inputs for Mechanistic-Empirical Pavement Design: Case Study in New Jersey

2019 ◽  
Vol 2673 (11) ◽  
pp. 332-348
Author(s):  
Abbas F. Jasim ◽  
Hao Wang ◽  
Thomas Bennert
Keyword(s):  
Cluster Analysis ◽  
New Jersey ◽  
Hierarchical Cluster ◽  
Pavement Design ◽  
Traffic Data ◽  
Truck Traffic ◽  
Vehicle Class ◽  
Site Specific ◽  
Load Spectra ◽  
Pavement Structures

Truck traffic is one of the significant inputs in design and analysis of pavement structures. This paper focuses on comprehensive cluster analysis of truck traffic in New Jersey for implementation of mechanistic-empirical pavement design. Multiple year traffic data were collected from a large number of weigh-in-motion stations across New Jersey. Statistical analysis was first conducted to analyze directional and temporal (yearly) variations of traffic data. Hierarchical cluster analysis was conducted and three optimum clusters were found for axle load spectra (single, tandem, tridem), vehicle class distribution, and axle/truck ratio, respectively. Road functional classifications were employed to identify different clusters as no common geographic trend could be perceived. The results illustrate that the predicted performance using the site-specific traffic data is comparable with that using the traffic cluster for the selected 10 sites. Among four different traffic inputs, the cluster traffic inputs generated the closest predictions of pavement life as compared with those using site-specific traffic input and the default traffic inputs yielded the highest error. It is recommended to use traffic clusters in mechanistic-empirical pavement design when site-specific data is unavailable.

Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

Hybrid Approach for Clustering Vehicle Classification Data to Support Regional Implementation of the Mechanistic–Empirical Pavement Design Guide

2013 ◽  
Vol 2339 (1) ◽  
pp. 112-119 ◽  
Author(s):  
Mark Reimer ◽  
Jonathan D. Regehr
Keyword(s):  
Hybrid Approach ◽  
The United States ◽  
Pavement Design ◽  
Vehicle Classification ◽  
Traffic Classification ◽  
Truck Traffic ◽  
Canadian Prairie ◽  
Data Set ◽  
Traffic Characteristics ◽  
Design Guide

This paper develops a hybrid approach for analyzing vehicle classification data and applies the approach to a fused data set from multiple jurisdictions in the Canadian prairie region. Application of the approach results in a set of regional default truck traffic classification groups for use in the Mechanistic–Empirical Pavement Design Guide. The hybrid approach is a conglomeration of three components: statistical clustering procedures, expert judgment, and industry intelligence. By applying the hybrid approach, analysts receive the joint benefits of analytical rigor and industry-oriented pragmatism. Application of this approach results in eight truck traffic classification groups for the Canadian prairie region that exhibit distinct differences from the default distributions developed for national use in the United States. The benefits of applying the hybrid approach on fused data sets include (a) the statistical strength gained from use of additional classification data, (b) the development of truck traffic classification groups that better reflect the diversity of patterns in a region, and (c) the potential for improved ability to capture future shifts in truck traffic characteristics because of experience gained in other jurisdictions. The paper also identifies limitations to the hybrid approach that should be considered. These limitations include varying data quality between jurisdictions, the sensitivity of low-volume sites to changes in industry patterns and the ability to track these changes, and potential shortages of continuous classification sites. When its benefits and limitations are well understood, the hybrid approach can be applied to truck traffic data analyses in any jurisdiction.

Sign in / Sign up

Export Citation Format

Share Document