This article discusses the geogrid’s potential to reduce granular base shear flow. Researchers have previously acknowledged the geogrid’s potential to enhance the performance of roadways, but this study goes further to (1) investigate the effectiveness of a geogrid when it is used to reinforce low-volume conventional flexible pavements constructed on low-strength subgrade and (2) to specify the optimal location for installing a geogrid in a low-volume pavement system. To quantify the effectiveness of a geogrid in flexible pavements, nine low-volume pavement design sections were constructed at the University of Illinois Advanced Transportation Research and Engineering Laboratory (ATREL). A total of 173 instruments were embedded in the pavement layers during construction to measure pavement responses. The test sections were then loaded using an accelerated loading system, and the pavements’ responses to conventional dual-tire loading were measured. This paper reveals how various pavement designs responded when exposed to the same loading conditions, specifically that the geogrid contribution to enhancing low-volume pavements is through reducing the longitudinal and transverse shear deformation in unbound layers. The study also determined the optimal location for installing geogrid in pavements.

Several tests are used for the characterization of unbound materials for pavement applications. The resilient modulus has been one of the most common tests for design specification of unbound materials. The California bearing ratio (CBR) is another laboratory test that is frequently used. The dynamic cone penetrometer (DCP) test is a more common test for in situ quality assessment/quality control of unbound materials. For better connection between design and quality assurance (QA)/quality control (QC), it would be helpful to have a reliable, mechanistic method for correlating test results. This is particularly true for the use of new materials, for which there is not an extensive body of data to empirically draw such connections. This paper presents a framework for a unified approach for modeling these tests. A discrete-element method (DEM) is used to simulate the CBR test, the DCP test, and the resilient modulus test. An initial evaluation demonstrated that the simulations can account for the effect of aggregate shape, size, gradation, friction, and stiffness. As such, this methodology shows promise for the development of mechanistic-based correlation between test results. These results are presented, along with some limitations of the current model and challenges for the future.

This study presents the regression relationships between two commonly used indicators of longitudinal road unevenness, the international roughness index (IRI) and the straightedge index. A total of 290 real longitudinal road-profile records were processed. The asphalt-concrete and cement-concrete road surfaces were analyzed separately. Five differently defined straightedge indexes, i.e., the deviations between straightedge and profiles, were considered. The one-parameter linear approximation without an absolute term seems to be an appropriate model for fitting the unevenness indexes relation. The conversion relationships between five differently defined straightedge indexes were derived. The results were calculated as a function of a straightedge length in the range of 3 to 7.8 m. The two-parameter power function was shown to be the most suitable approximation model of the straightedge indexes dependence on the straightedge length. The analyzed influence of the different slab lengths of cement-concrete profiles on the conversion relationships indicated the marginal impact for the typical slab lengths.

IntelliDrive, the integration of vehicles and the infrastructure through wireless communication, has generated great interest in the transportation community. It is widely expected that IntelliDrive will support significantly improved transportation operations. However, there have been few studies investigating (1) how IntelliDrive will be used to improve operations, and (2) how to estimate expected benefits of IntelliDrive operations applications. In this study, the research team investigated the application of IntelliDrive to address merging conflicts created by freeway on-ramps. Although merging conflicts contribute significantly to freeway congestion, transportation engineers have limited options with existing traffic surveillance and management tools. IntelliDrive offers the potential for more active management of the surface transportation system, providing capabilities that may be of significant benefit in ramp management. In this research, a lane changing advisory algorithm was developed to explore the potential of IntelliDrive in improving freeway ramp management. Utilizing new IntelliDrive capabilities (highly detailed individual vehicular data and personalized advisory information provision), the proposed algorithm attempts to reduce merging conflicts by encouraging early mainline freeway lane changes to create more space in ramp merging areas. An evaluation of the algorithm was conducted by using a PARAMICS microscopic simulation model of a heavily traveled freeway network in Orange County, California for morning peak hours. The results have revealed that IntelliDrive has the potential to increase vehicle miles traveled by up to 4.3% and reduce vehicle hours traveled by up to 4.6%, which resulted in up to 9.3% higher average speeds, in the best case. However, the results also indicated that, to achieve the greatest possible benefits, the proposed algorithm requires very high compliance of drivers and near full deployment of IntelliDrive.

The Highway Capacity Manual (HCM) and other analytical methods provide a point estimate of average vehicle delay for a specified lane group over a defined time period (e.g., peak hour). The estimated delay is a function of a number of factors, including geometrAy, signal timings, and approach volumes. Some of these factors, particularly, peak hour volume, can be considered to be random variable, and therefore the distribution of intersection delay is a function of the distribution of these random factors. This paper presents findings of a study that addresses the variability in estimated HCM delay as a function of the variation of peak hour volumes. For protected movements, the distribution of delay is analytically determined as a function of the distribution of the peak hour volume and the HCM delay equation characteristics. A numerical method is proposed to find the distribution of delay for permitted movements for which capacity changes as a result of variability in the opposing volume. The proposed methods are demonstrated through application to a hypothetical intersection. The results indicate that when the degree of saturation for the average peak hour volume is not far from 1, the HCM point estimate of average delay is not sufficient for evaluating the quality of service or designing the intersection signal timing plan. For example, the results from the hypothetical intersection examined in this paper suggest that for a permitted left turn with an average degree of saturation of 0.77 and an average delay of $37.8\mathrm{s}/\mathrm{vehicle}$ ($\text{level}\text{of}\text{service}=D$), there is approximately 20% probability that the lane group operates at a level of service of $E$ or worse (i.e., $\text{delay}>55\mathrm{s}/\text{vehicle}$).

This research is aimed at finding the throughput rate of convoy movement in a transportation network. The primary goal of the study is first to find the throughput rate that will facilitate an estimate of the time to logistically deploy one unit, omnidirectionally (radially), to different localities during different elapsed times. The concept of average speed on a linked road is used to estimate the throughput rate of the vehicles. Several models for physical boundary conditions have been established to estimate the turning delay time in a convoy movement through streets in an urban environment under different conditions of road geometry. Special cases are also evaluated to study the capability of the general models. As a primary emphasis of the study, delay time at a turning is modeled to estimate the individual arc capacity and linked-arcs capacity. A model to estimate the average velocity in a linked road is developed to determine the average throughput rate of a route for a convoy movement. In all modeling approaches, detailed descriptions of the derivations are illustrated with numerical examples and figures.

Although auxiliary through lanes (ATLs) are often used to increase the capacity at signalized intersections, their utilization by motorists tends to be significantly lower than continuous through lanes (CTLs). This study was designed to (1) determine whether recently proposed models for predicting lane-drop utilization are applicable to Buffalo, New York; (2) develop new models for predicting ATL utilization and for predicting drivers’ merge behavior at ATLs; and (3) assess the ability of microscopic traffic simulation models to reproduce observed merging behavior. To achieve this, geometric, traffic count, and merge data were collected from select sites in Buffalo. The study shows that lane utilization in Buffalo is a function of total through-traffic volume, right-turning volume, and upstream and downstream land-use types. The average merge distance, in contrast, tends to be a function of the ATL total length, the first lane-drop warning sign, the speed limit, and the total through-traffic volume. The study also identified the challenges of calibrating merge behavior in microscopic traffic simulation models against field observations.

Investigations to identify relationships between crashes and road features usually deal with effects of only one or two of the main components of traffic safety, i.e., human, vehicle, and infrastructure performance. There are several contributing factors of the components that together lead to a crash. This study devises an approach to include information from all three components in a system using field operational test (FOT) data. FOT data are recorded from real-life driving that is different from traffic simulations and specific on-site data collection. The study focuses on identifying horizontal curves using FOT and provides access to vehicle and human response data at the exact time when the vehicle drove in a specific location. A method has been developed to derive path radius and to identify start–end points of horizontal curves using FOT data. With this information, vehicle response signals and human behavior data can then be arranged on a common axis referenced to the curve. The approach also identifies lane changing maneuvers on curves that can be used to evaluate potential crash triggers. The application of this method allows for reviewing changes in the regulatory speed limit, curve geometry, or crash history and thus evaluates the design of curves and choosing appropriate countermeasures.

Because of the growing awareness of the importance of the traffic condition uncertainty-related studies, traffic condition uncertainty modeling is gaining increasing attention from the transportation research community. In this field, traffic condition uncertainty, gauged mainly by the conditional variance of traffic characteristics, has been investigated primarily with two major approaches, generalized autoregressive conditional heteroscedasticity approach and stochastic volatility approach; however, both lack a thorough and sound test on the applicability of these approaches. To complete this modeling gap and hence lay the theoretical basis for traffic uncertainty-related studies, an integrated heteroscedasticity test, including an optimal transformation search and four statistical tests, is proposed in this study. By using real world data collected from 36 stations across four regions in both the United Kingdom and the United States and aggregated at 15-min interval as a typical representative, the proposed integrated heteroscedasticity test is demonstrated, validating the heteroscedastic nature of the traffic conditional series. In addition, the effects of transformations are illustrated together with an online short-term traffic condition forecasting algorithm as an additional validation of this heteroscedastic nature. On firmly establishing the heteroscedastic nature of the traffic conditions, future studies are recommended to further the modeling of traffic condition uncertainties over a spectrum of time intervals and apply the uncertainty models in various applications such as travel time reliability or the proactive traffic control systems.

This paper presents an analytical solution for the prediction of the time-dependent one-dimensional temperature profile in an N-layered rigid pavement system using Laplace transformation. Temperature at any depth in a rigid pavement system can be estimated by using the proposed solution with input data such as pavement layer thicknesses, material thermal properties, and the measured air temperature and solar radiation intensities. The associated initial boundary value problem is governed by the classic heat conduction equation with the air temperature and solar radiation considered in the surface boundary condition. Interpolatory trigonometric polynomials, based on the method of discrete least-squares approximation, are used to fit the measured air temperature and solar radiation intensities during a user-defined time period. The relationship between the constants of integration for the $i$th layer and the first layer are deduced, which simplifies the solution process. Model validation with the measured data confirms that the proposed solution generates a reasonable temperature profile in the concrete slab of a four-layer rigid pavement system during two different time periods of the year.