Soheil Sajjadi

NCDOT Quality Control Methods for Weigh in Motion Data

At hundreds of weigh-in-motion (WIM) stations, state departments of transportation (DOTs) collect WIM data 24/7 to meet federal traffic reporting requirements. The North Carolina Department of Transportation (NCDOT) collects WIM data using procedures consistent with recommended industry practices to estimate static vehicle axle weights based on dynamic traffic measurements. Data errors and poor quality data are captured regardless of the technology used, and a quality control (QC) process is an important part of all WIM data systems. WIM data must undergo a series of sequential and well-defined QC procedures to ensure that the data meet the federal requirements and new standards for the Mechanistic Empirical Pavement Design Guide (MEPDG) process. This article documents the NCDOT WIM QC procedures. The results of the QC analysis provide reliable data sets for use in developing Levels 1, 2, and 3 traffic data inputs for the North Carolina MEPDG models.

Axle Load Distribution for Mechanistic–Empirical Pavement Design in North Carolina: Multidimensional Clustering Approach and Decision Tree Development

A multidimensional clustering approach to generate regional average truck axle load distribution factors (ALDFs) for North Carolina is presented. The results support the Mechanistic–Empirical Pavement Design Guide (MEPDG). Weigh-in-motion data collected on North Carolina roadways are used in the analysis. A multidimensional clustering analysis based on ALDF data develops representative clusters for different highway functional classifications. Findings show that ALDF clusters have distinct characteristics for primary roads, secondary roads, collectors, and local roads. An easy-to-use decision tree based on available traffic parameters and local knowledge helps the pavement designer select the proper ALDF input. Specific contributions include a multidimensional clustering analysis that is guided by MEPDG damage-based analysis, well-defined ALDF clusters that represent specific traffic patterns in North Carolina, and a decision tree characterized by its simplicity to help pavement designers select ALDF inputs.

Enhancements to the Freeway Facilities Method in the Highway Capacity Manual to Enable Reliability Analysis

The Highway Capacity Manual (HCM) methodology provides a fast and reliable approach for the analysis of freeway traffic operations; research is ongoing to add a reliability analysis component to the method. Several key methodological enhancements are needed to make the method ready for reliability analysis; these enhancements are described and illustrated in this paper and include (a) the incorporation of the drop in capacity in the queue discharge flow compared with the prebreakdown flow; (b) the addition of a speed adjustment factor (SAF) as a new calibration factor for the modeling of nonrecurrent congestion sources, such as weather and incidents; (c) the explicit incorporation of SAFs and capacity adjustment factors (CAFs) into the HCM methodologies for merge, diverge, and weave segments; (d) the development of new SAF and CAF default values for freeways; and (e) the addition of new congestion performance measures. The enhancements overcome key limitations in the existing HCM 2010 freeway facility method, which assumes a fixed capacity throughout undersaturated and congested regimes, ignores the free-flow speed–reducing effects of inclement weather, and treats weave and merge–diverge segments as basic freeway segments for CAFs less than 1.0. In addition to the existing performance measures generated by the computational engine, some new output variables are proposed: the travel time index and a denied entry queue length measure. The various enhancements are illustrated with computational examples, and a discussion is offered as to the effects of the enhancements on conventional freeway facilities analysis, even without the evaluation of reliability.

Proposed Chapters for Incorporating Travel Time Reliability into the Highway Capacity Manual

This document contains two proposed chapters for the Highway Capacity Manual (HCM) that introduce the concept of travel time reliability and offer new analytic methods. Proposed Chapters 36 and 37 set out methodologies for incorporating reliability into the HCM analytic procedures for freeway facilities and urban streets. The approach is to generate many freeway and urban street scenarios involving various causes of nonrecurring congestion, such as incidents, weather, and work zones, and use the scenarios as input to a computational engine to calculate travel time over a segment. The travel times for each scenario are used to construct a distribution of travel time from which reliability performance measures can be derived. Chapter 37 supplements Chapter 36. It provides reliability values for selected U.S. facilities, offers an alternative freeway incident prediction method, elaborates on the freeway and urban street scenario generators, explains how to measure reliability in the field, and gives an example problem. The chapters were prepared under the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) Project L08, Incorporation of Travel Time Reliability into the Highway Capacity Manual, but they have not been officially accepted by the Highway Capacity and Quality of Service (HCQS) Committee of the Transportation Research Board.

Incorporating Travel Time Reliability into the Highway Capacity Manual

This publication presents a summary of the work conducted during the development of two proposed new chapters for the Highway Capacity Manual 2010 (HCM2010). These chapters demonstrated how to apply travel time reliability methods to the analysis of freeways and urban streets. The two proposed HCM chapters, numbers 36 and 37, introduce the concept of travel time reliability and offer new analytic methods. The prospective Chapter 36 for HCM2010 concerns freeway facilities and urban streets, and the prospective supplemental Chapter 37 elaborates on the methodologies and provides an example calculation. The chapters are proposed; they have not yet been accepted by the Transportation Research Boards Highway Capacity and Quality of Service (HCQS) Committee. The HCQS Committee has responsibility for approving the content of HCM2010. The second Strategic Highway Research Program (SHRP 2) Reliability Project L08 has also released the FREEVAL and STREETVAL computational engines. The FREEVAL-RL computational engine employs a scenario generator that feeds the Freeway Highway Capacity Analysis methodology in order to generate a travel time distribution from which reliability metrics can be derived. The STREETVAL-RL computational engine employs a scenario generator that feeds the Urban Streets Highway Capacity Analysis methodology in order to generate a travel time distribution from which reliability metrics can be derived.