With the deployment Automatic Vehicle Location systems to monitor transit vehicles (and other fleet vehicles), there have been several efforts in recent years that seek to leverage these data to also obtain arterial travel times for the private vehicle population. The fleet vehicle data capture the traffic conditions, but they also capture behavior unique to the fleet, e.g., servicing passengers at a bus stop. There are several strategies used in conventional practice to eliminate the biases that occur in the vicinity of the bus stop. While investigating the benefits of using a perception sensor to identify ambient traffic conditions around the bus and correct for transit operations, the present work revealed that even a perfect correction for local conditions resulted in large deviation between the travel time measured from the bus and the actual travel time experienced by the surrounding private vehicles. This research uncovered a fundamental issue affecting almost all systems that use buses as arterial travel time probes: the fact that transit operations inevitably pull the bus out of the traffic signal progression no matter what corrections are made locally at the bus stop. The impact at subsequent traffic signals far downstream of a bus stop can be much larger than the local effects at the bus stop itself. This point is an important finding for any system that seeks to use transit vehicle probes to estimate the private vehicle travel times. To date the literature has made little consideration of the signal progression biases relative to the private vehicles that occur far beyond the bus stop. Finally, though the focus the present work is buses, the basic findings should apply to other fleets used as travel time probes as well if the given fleet behavior differs from the private automobiles.

Bridges are an integral infrastructure component and, as such, have been the subject of extensive research efforts related to structural performance. However, there has been little study on the traffic safety performance of bridges, which have very different physical and operational characteristics compared with regular roadway facilities. This study develops safety performance functions (SPFs) for overall vehicle crashes and single-vehicle crashes occurring on major highway bridges in Alabama. The bridge characteristic data and crash information are obtained from three different databases. Geographic information systems (GIS) are used to spatially represent bridges as vectors and associate crashes to the bridges based on location attributes from the crash data. SPFs of several functional forms are developed and investigated for identifying the best model using negative binomial regression. The models are validated by comparing their relative predictive capabilities. This paper recommends models that fit the Alabama data well. These models can be used for estimating the expected number of crashes on bridges along major highways in Alabama.

Current materials and construction specifications for pavement preservation treatments are predominantly prescriptive and rarely correlate the initial quality of the treatment to its future performance. As a result, it is difficult for highway agencies to account for the performance lost or gained due to differences between the as-designed treatment and as-constructed treatment. Also, these specifications may not incentivize contractors to focus on factors that affect the treatment’s long-term performance. To account for these limitations, this paper provides a simulation-based performance-related specification (PRS) methodology for pavement preservation treatments, with application to thin hot-mix asphalt (HMA) overlays. Through performance prediction models and lifecycle cost analysis, this methodology determines pay factors based on the probability distributions of the total lifecycle costs of the as-designed and as-constructed treatments and the unit bid price. The developed PRS methodology was applied to four Long-Term Pavement Performance sections in Texas, Idaho, Florida, and New Jersey that received thin HMA overlay treatments. These case studies demonstrated the applicability of the developed methodology and provided insights into the relationships between the initial quality of thin HMA overlays and expected pay factors.

Empirical performance-prediction models are a central part of every network-level pavement management system. In this regard, a variety of novel techniques including computational intelligence have been applied, mainly without a systematic approach to ensure compliance with principles of pavement engineering. In this study, a framework is provided for development and comprehensive comparison of alternative techniques for pavement performance modeling. As an example, several machine-learning techniques are compared in developing flexible pavement-roughness prediction models using Federal Highway Administration (FHWA’s) long-term pavement performance (LTPP) data. Three important principles of model development—maximum likelihood, consistency, and parsimony—are considered in providing a robust parameterization guideline. Variant architectures of artificial neural networks (ANN), radial basis function (RBF) networks, and support vector machines (SVM) are tested to determine the optimum parameters. Final developed models are compared through quantitative and qualitative evaluations by means of a testing database that has not been used for model development. The example comparison gives the generalized RBF network model an edge over other machine-learning techniques in predicting pavement performance. This framework can be implemented by roadway agencies to develop a robust and representative performance-prediction model for pavement management systems. Moreover, the provided framework can be used to benchmark and compare alternative modeling paradigms for specific prediction problems.

This study develops passenger-car equivalents (PCEs) for heavy vehicles at roundabouts. For this task, the movements of cars and heavy vehicles were observed using video records collected from three roundabouts in Vermont; Ontario, Canada; and Wisconsin. The PCE was developed such that the variation in the entry capacity in a various mix of cars and heavy vehicles was minimized. The PCE was also applied to the prediction of the entry capacity using a roundabout capacity model. For the model inputs, the study adjusted the critical headway and the follow-up headway to account for the difference in driver’s gap acceptance behavior between cars and heavy vehicles. The result shows that the PCEs for heavy trucks and light trucks are 1.5–2.5 and 1.0–1.5, respectively, at the three roundabouts. In particular, the PCE for light trucks was lower than a default value of 2.0. The result also shows that the established model generally predicted the capacity more accurately when the critical headway and the follow-up headway were adjusted by different weights instead of the same weight (i.e., the default value of the PCE).

Local calibration for mechanistic-empirical (ME) pavement performance models are not only important but also useful toward the assurance of achieving adequate design predictability. An advanced ME design model for continuously reinforced concrete (CRC) pavement featuring analysis of punchout as affected by erosion was used to demonstrate the process to carry out calibration. The parameters for erosion calibration can be provided by either faulting data or data determined by erosion testing using the hamburg wheel-tracking device (HWTD). A method for both fatigue and erosion equivalency was also presented. A promising new approach addressing the structural equivalency of the pavement section based on the degree of erosion was established in this research. Included in this discussion is the use of performance data from two sites included in the long-term pavement performance (LTPP) database with respect to CRC punchout distress to demonstrate the process of calibration.