Providing adequate nighttime visibility to roadway users is an important consideration for state and local transportation agencies. Driving at night is less dangerous when pavement markings are easily discernable. Retroreflectivity is a measure of nighttime visibility. Transportation agencies could use estimates of the expected service life of pavement markings to plan restriping operations at a time when markings are near a minimum threshold level of retroreflectivity. The present study proposes the use of an artificial neural network to predict pavement marking retroreflectivity as a function of initial retroreflectivity, the age of the markings, traffic flow, pavement marking type, and route location information using data from North Carolina. The results show that many of the input variables have a nonlinear association with pavement marking retroreflectivity. Surface plots of the degradation pattern are provided to illustrate the relationship between input and output variables. Estimates of service life are provided to show how the output can be used to manage pavement marking systems.

The paper is aimed at investigating the dielectric response of the dense-graded hot-mix asphalt mixture (HMA) using an open-ended coaxial probe technique over a wide frequency range. The effects of asphalt content, air voids and density on dielectric response were examined. The results indicated that the dielectric constant was frequency- independent in the range of 3–7 GHz. Beyond the 3–7 GHz range, the dielectric constant decreased with increasing frequency due to polarization phenomenon. A linear relationship between the dielectric constant and density of the compacted asphalt mixture was identified. A higher density of HMA induced a reduction in air voids, thus increasing the dielectric constant. The asphalt content showed limited effect on the dielectric constant. The dielectric mixing model was proposed to obtain the dielectric constant of the heterogeneous asphalt mixtures according to the volume fraction and the dielectric constant of the constituents in asphalt mixtures. In addition, the density prediction model was developed to estimate density of the dense-graded asphalt mixture using dielectric constant, air voids and asphalt content. Comparisons of predicted density and measured values showed a good agreement with $R^2=0.94$ . Both the dielectric mixing and the density prediction models offer a great potential for applying the electromagnetic technique to improve the nondestructive testing of asphalt pavements.

Traffic sensors have been deployed for decades to freeways to meet the requirements of various traffic/transportation applications, most noticeably traffic control and traveler information applications. A unique feature of traffic sensor deployment is that it is a continuous and evolving process. That is, with new applications that emerge, additional sensors are usually required to be deployed to meet new requirements. This process is also sequential in nature and the new deployment has to consider existing sensors. In this paper, we propose a modeling framework to capture this sequential decision-making process for traffic sensor deployment. The framework is based on our recent findings that (1) optimal sensor deployment for a single application can be determined by a staged process or, more formally, a dynamic programming (DP) method and (2) new sensor locations for new applications can be optimally solved by the DP method via considering existing sensors. We illustrate the framework using two applications: ramp metering control and travel time estimation. It is found that the proposed scheme can appropriately capture the decision-making process of traffic sensor deployment and can generate optimal sensor placement at any stage by considering sensors that have already been deployed. The model is tested using global positioning system enabled cell phone data and traffic simulation on a real-world freeway route in the San Francisco Bay Area.

Crossing behaviors at intersections are conventionally described through the gap acceptance theory; however, many gap-forcing behaviors exist at Chinese urban intersections. In this study, gap-forcing behaviors were analyzed and modeled from the perspective of conflict avoidance using a new modeling methodology called virtual field graph. After providing the definition of the preemptive status and the method to calculate the preemptive level, a new driving behavior model that differentiates from the conventional gap acceptance model was established. Analysis of collected field conflict data indicates that 82.2% of drivers’ decisions are normal decisions, while 17.8% are abnormal decisions. This suggests that there should be a physiological critical preemptive level while the drivers are making decisions. The driving population’s critical preemptive level was calibrated with the collected field conflict data through the logistic regression method and maximum likelihood approach.

Civil asset managers continue to seek appropriate thresholds of asset performance for applying standard interventions. Recognizing that between the two extremes of profligacy and parsimony there exists a certain optimal intervention application threshold that is consistent with greatest possible overall benefits at least possible overall costs, this paper develops a framework for establishing such thresholds. The framework dimensions are costs (monetary) and benefits (durability and service life), agency and user cost considerations, and initial and life-cycle contexts. The paper utilizes well-established concepts of cost-effectiveness analysis to address the long-standing conundrum of optimal asset performance threshold determination. The application of the methodology is then demonstrated for a common pavement rehabilitation treatment, and the derived optimal thresholds are compared with those of past practice. The results showed that for this treatment, the past practice is not only inconsistent with optimal practice but is also associated with significant variability thus precluding its suitability for the practice. The developed methodology can be used to establish performance thresholds for various standard interventions not only in the management of transportation assets but also of systems in civil and other engineering disciplines.

Magnetic levitation (maglev) systems have become a focus of the worldwide transportation industries. Despite demands for this type of transportation, no practical and well-accepted algorithm has been publicly presented yet for the analysis and design of maglev guideway structures. The maglev vehicle/guideway interaction problem has bothered researchers for years. A systematic approach for this methodology is required. In this paper, the technical specification of the guideways and the forces imposed on them are investigated, and a maglev vehicle/guideway system interaction model is presented. Design methods and criteria of the guideway, including the deflection regulations and stresses limits, are also discussed. Moreover, a case study is conducted to investigate the dynamics of the maglev model. Modeling the electromagnetic suspension system, the five-car vehicle system, and the vehicle/guideway interactions is accomplished. Eventually, the behavior of the elevated guideway is technically compared with that of railroad bridges to illustrate the structural advantages of the guideway. The case study illustrates the practicality of the analysis, so the results obtained indicate that the method presented is useful for the design of the maglev guideway.