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

Abstract

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.

A data fusion approach for estimating traffic distribution characteristics of expressway: A case study of guangdong province, china

“…As can be seen from the comparison results in Figure 20, the maximum difference of rutting depth between the lane Frontiers in Materials frontiersin.org coefficient calculated by the standard value and the presented value in this study is 24% (only the lane with the maximum rutting depth is calculated), because the lane coefficient specified in the standard does not reasonably reflect the characteristics of channelized traffic on the expressway. As a matter of fact, due to the influence of lane division, traffic organization and management and channelized traffic of expressway, the distribution of different vehicle types on different lanes is extremely varied, and the vehicle types on the same lane are also complex (Jasim et al, 2019). Therefore, it is necessary to divide the expressway traffic flow into lanes to obtain the actual lane coefficient, obtain the accurate traffic flow of each lane, and accurately detect the pavement performance of the expressway, so as to guide the maintenance decision and construction.…”

Mechanistic–Empirical Analysis of Pavement Performance Considering Dynamic Axle Load Spectra Due to Longitudinal Unevenness

“…In M-E pavement design, reliability is a combined indicator of probability that each performance measure will be less than a specific level over the analysis period [1]. The reliability level of 90% was considered in the analysis.…”

“…The axle load spectra obtained from weigh in motion (WIM) is utilized as traffic inputs in MEPDG. The WIM system captures gross vehicle weights, individual axle loads, and axle spacing when trucks are passing over the station [1]. In general, raw data collected by the WIM system need to be calibrated by comparing field measurements of static scales to guarantee the required accuracy [2].…”

Asphalt pavement modulus backcalculation using surface deflections under moving loads

“…Since elastic material properties are considered in 2.5D FEM, the effect of multiple loads can be considered using the superposition principle. Most of the truck configurations include single and tandem axles and the range of load magnitudes can vary from 44.5 to 204.6 kN (Jasim, Wang, & Bennert, 2019). The truck at the adjacent lane was assumed traveling parallel to TSD and the rear axle load is 100 kN.…”