This paper investigates the long-term trend of temporal distribution of roadway traffic using hourly traffic-count data collected in Seoul, South Korea, from 1995 to 2010. Interpolations using quadratic local polynomial regressions and a Gaussian mixture model were applied in order to identify changes in temporal distributions at a scale of less than one hour. The identified changes in distribution characteristics suggested that more attention should be given to outbound traffic, which appeared to be worsening because of the increased population and jobs outside of Seoul. Although this study did not attempt to thoroughly investigate the causal relationship between the changes of temporal distributions and the associated factors, the framework and methodology suggested are expected to play a foundational role in identifying the long-term trend of traffic flow using archived data.

Truck traffic is usually characterized by using truck factors (TFs). A TF converts a mixed truck traffic stream over the pavement design lifetime into a number of equivalent single-axle loads (ESALs). This conversion requires equivalent single-axle load factors (EALFs), which can either be empirical or theoretical. The mechanistic-empirical pavement design guide (MEPDG) considers axle load spectra and uses it to run the response model, considering implicitly theoretical EALFs. This paper presents a methodology for TF estimation based on theoretical EALFs calculated by using simulated axle loads, thus overcoming the unavailability of data to define axle load spectra. Once the EALFs are calculated for several simulations, TFs are established and regression models are defined to predict TFs. To reach this point, the study considered the two most common Portuguese truck configurations and pavement structures. The methodology development and application revealed its flexibility and upgradability. The results indicated that vehicle–pavement interaction increases TFs by up to 27% when the initial international roughness index (IRI) increases $1\mathrm{m}/\mathrm{km}$, underling the importance of the dynamic behavior of the loads.

There is an increased focus worldwide on understanding and modeling rolling resistance because reducing the rolling resistance by just a few percent will lead to substantial energy savings. This paper reviews the state of the art of rolling resistance research, focusing on measuring techniques, surface and texture modeling, contact models, tire models, and macro-modeling of rolling resistance.

The usage of locally calibrated performance models and other related parameters is necessary in order to utilize the pavement management system (PMS) for maintenance strategy decision-making. In this study, the performance models of typical asphalt resurfacing treatments used in Tennessee were calibrated for the PMS by investigating historical maintenance projects. The multiple regression method was employed to analyze the influence of pretreatment PSI, traffic level, overlay thickness, and milling depth on the post-treatment performance curves. Specific designs of asphalt overlays and performance classes were determined according to the regression analysis. The performance curves for each identified treatment method at different performance classes were established and calibrated for the PMS. Rehabilitation trigger values, typical treatments and decision trees for pavement with different functional classes were determined and designed by investigating the historical maintenance projects. Utilizing the calibrated models, pavement maintenance optimization analyses were performed on a network level. The study has found that pavement with lower pretreatment roughness, thick overlay, and deep milling deteriorated at slower rate. Pavement with high traffic levels tended to have lower post-treatment roughness. The distress condition decreases much faster than smoothness. The most cost-effective network level maintenance strategy can be determined by conducting a network optimization analysis with a sufficient high budget limit. It is recommended that an optimized maintenance budget allocation should include combining cost-effectiveness, rolling unused budget over fiscal years, and re-distributing budget among different maintenance regions.

A small fraction of the vehicle fleet produces a large ratio of the total mobile source emissions or the total fuel consumption due to different patterns of driving behavior based on driver characteristics. The Next Generation Simulation (NGSIM)—Federal Highway Administration (FHWA) trajectory data were used to compare the driving patterns of drivers under congested and uncongested conditions. Drivers were statistically classified into three groups; aggressive, moderate, and defensive drivers based on degree of strength of pushing pedals: gas pedal or brake. Accelerating and braking events, named as process, were counted, and strength of pushing pedal was measured in each process. In this paper, the measured strength of pushing pedal is named as target operating acceleration. Then, the emissions and the fuel consumption of each vehicle were estimated using the Virginia Tech Microscopic energy and emission model (VTMicro) model, which consequently provided the comparison of the emissions and fuel consumption of each driver group. The comparative statistical analyses indicate that aggressive drivers, who frequently used stronger acceleration pedal than other drivers, emitted more hydro carbon (HC), carbon monoxide (CO), and oxides of nitrogen (NOx), particularly under uncongested traffic conditions. The results also indicate that if aggressive drivers change their driving pattern to be defensive, the emissions are expected to decrease by 15.1% of HC, 20.8% of CO, and 16.7% of NOx under uncongested condition.

Horizontal curves play a critical role in roadway safety by providing a smooth transition between tangent sections. Because radii of horizontal curves are one of the most fundamental elements in roadway geometry design, transportation agencies, e.g., state DOTs, need to measure them to support network-level safety analysis. However, the traditional methods that are commonly used by transportation agencies, e.g., plan sheet reading method and chord-offset method, are time consuming, labor intensive, and inaccurate. Although some semiautomatic and automatic methods have been developed using global positioning system (GPS) data and/or geographic information system (GIS) functions in recent years, these methods are not yet ready to be practically used in a network-level analysis because they either require intensive manual intervention or lack of the capability in automatically identifying complex curves. This study is aimed to develop a new method using widely available GPS data that can automatically identify all types of horizontal curves and measure the corresponding curve radii, including the most challenging spiral curve. The simulation test using 385 synthetic horizontal curves shows that the proposed method can correctly identify 90.1% of the tested curves and can accurately classify 87.3% of the detected curves types. The field test shows that the proposed method can correctly identify all of the 25 tested curves and can accurately measure the corresponding radii. The results from an experimental test clearly demonstrate the accuracy and effectiveness of the proposed method. A case study on Interstate 285 demonstrates that proposed method is a promising method for transportation agencies to achieve reliable and efficient network-level analysis.

Road dust from unpaved roads (dirt roads) is a major source of airborne particulates; the loss of those fines accelerates the deterioration of roads. As a result, road dust emissions are a major concern of the users and managers of dirt roads. Exxon Mobil has been concerned with the dust emissions and rapid road deterioration of the access roads in its natural gas production facility in the Piceance Creek Basin north of Rifle, Colorado. As part of the access road management plan for Exxon Mobil Piceance Creek, a research project was initiated to investigate the effectiveness of several chemical dust suppressants in reducing dust emissions, thus prolonging the intervals between periodic maintenance. Rio Blanco County and the Bureau of Land Management participated in this project. Using the Colorado State University Dustometer and its associated dust measurement protocol, it was found that all chemical suppressants decrease dust emissions and that magnesium chloride (${\mathrm{MgCl}}_2$) was the most effective dust suppressant under the prevailing weather conditions from March through September, 2010. The fugitive dust emissions from the ${\mathrm{MgCl}}_2$ treated sections of the road were more than four times less than those from the untreated control section, generating less than 0.5 g of dust per 0.8 km (0.5 mi) of test section. It was also found that the native soil road surface type performed better than the new gravel road surface type and that emissions were related to speed.

The 2012 transportation bill, Moving Ahead for Progress in the 21st Century, formalizes the application of risk management in transportation asset management decision making. The bill requires departments of transportation to have a formal risk management plan for the national highway system. When carried out successfully, risk planning can be a very powerful framework to effectively and efficiently manage transportation infrastructure. However, if there is lack of experience with and insight into risk planning, it can go wrong, leading to a counterproductive plan that contributes to ineffectiveness and inefficiencies in the organization and transportation system and to wasted resources. This paper reviews a broad spectrum of literature on risk methods and applications to identify and discuss some common pitfalls—failure to communicate, failure to monitor, proportionality, failure to involve knowledgeable individuals. It synthesizes the results for purposeful use in transportation asset management decision making. The literature review indicates that pitfalls are pervasive and usually familiar to the new generation of industries adopting the principles of risk in their management practices. Accordingly, the paper recommends that decision makers and analysts become more familiar with these pitfalls before embarking on risk implementation programs.