Falling weight deflectometer (FWD) testing is commonly used to estimate subgrade resilient modulus ($M_R$) by using back calculation and/or the AASHTO $M_R$ equation. Considering the availability of limited information in the pavement management information system (PMIS) database, the AASHTO $M_R$ approach is suitable for subgrade assessment at a network level because of its simplicity. An adjustment factor is commonly applied to the AASHTO $M_R$ equation because of the difference between laboratory-measured $M_R$ and estimated $M_R$. However, large variations in the adjustment factor have been observed with different geographical locations and climate conditions. Thus, using a fixed value of the adjustment factor has led to inaccurate assessment of subgrade soil $M_R$. This paper presents a methodology to improve the accuracy of the existing AASHTO $M_R$ equation by constructing an $M_R$ adjustment factor based on the correlation of back-calculated $E_R$ and AASHTO $M_R$. This study also investigated the effects of subgrade soil stiffness, bedrock depth, highway classification, and environmental condition on the correlation. Additionally, the uncertainty of the AASHTO subgrade $M_R$ equation because of the variation in the adjustment factor was quantified. As an example analysis, the methodology was applied to five districts in the state of Texas. The newly constructed $M_R$ could reduce variation in the adjustment factor, increasing the $R^2$ value from 0.524 to 0.894 in the correlation. The developed methodology can be applied to a smaller set of FWD data such as each road and county, which can reduce an uncertainty in the adjustment factor.

Variable speed limits (VSL) aim to improve freeway mobility and safety by influencing collective behaviors of drivers. Thus, VSL benefits should be positively correlated with the VSL compliance level (CL). Surprisingly, a number of heuristic VSL control strategies have shown that VSL with increased CLs can, in fact, increase travel time. However, it has yet to be analyzed whether or not that outcome is because of the control strategy design or the CL. Some recent studies have shown that, regardless of CL, a proactive optimal VSL control provides mobility benefits; however, no evidence has been found to indicate which CL is most achievable in practice, nor has a description been found for the distribution of speed of a given VSL. The objective of this paper is to quantify the relative contribution of CLs with a proactive optimal VSL control toward improving mobility and safety. In this study, several CL-to-VSL strategies have been modeled after real-world driver behavior. To quantify the impact of CLs only, speed distributions are altered with the static speed limit. Then, the benefits are quantified by implementing a proactive optimal VSL control strategy with CLs. The simulation evaluation shows that both VSL mobility and safety benefits are positively correlated with increasing CLs. Specifically, the travel time, throughput, and collision probability are improved in the CL ranges of 5–15%, 6–8%, and 50–60%, respectively. The study findings will help guide transportation agencies in deploying VSL control by considering CL, so as to achieve maximum mobility and safety benefits.

This paper examines the value of utilizing dynamic, revenue-maximizing, congestion pricing on a privately operated tolled route, when the only alternative is a highly congested, public, free-access route. Three methods are specified by which revenue can be maximized—through the selection of a fixed percent of users to set the toll price for, through the selection of a fixed level of service on the toll road, and through the use of a nonlinear optimization model. These methods are tested on a case study involving a highly congested corridor. In simulation, the corridor’s flow rates are observed following the posting of a toll, the toll is updated, and the new flow observed until a subsequent round of revenue maximizing is applied to the new conditions. It was concluded that in a highly congested corridor a dynamic, revenue-maximizing toll can finance the construction of new capacity without degrading social welfare.

Traffic is one of the most important variables used in pavement design methods. There are currently two methods for characterizing wheel load repetitions: equivalent single axle load (ESAL) and load spectra, which is a more precise characterization of traffic but relies on the same input data used to calculate ESALs. Although the load spectra are described using a bimodal mixture distribution, samples taken in Colombia indicate that the load spectra exhibit an intermediate region that can be modelled with an additional distribution. This research proposes the use of a mixture of three distributions: two log-normal distributions for the description of the peaks and a normal distribution for the intermediate region, finding a closed statistical solution for the formal evaluation of its properties, and deduces the expression of the $n$th moment that facilitates obtaining important features of the proposed distribution. In a case of application in two geographic regions of Colombia, with more than 53,000 tandem and tridem observed axles, it was found that the proposed model effectively improves the characterization of the load spectra.

This study focuses on the development of equipment for measuring riding smoothness along bicycle routes. In addition, a bicycle motion analysis model was also developed and verified for conducting effective and efficient assessment of riding smoothness. Compared to motor vehicles, bicycles have a relatively simple shock absorber design. Most of the motorway smoothness indices developed for passenger cars do not satisfy the needs of evaluating bicycle routes. A test bicycle equipped with multiple sensors was developed in this study to measure the vertical acceleration while riding a bicycle. A lightweight inertial profiler was also designed and manufactured for measuring the bicycle route profile, which was used as the major input of the analysis model. Through several stages of field tests, it was concluded that the measured and calculated accelerations are fairly consistent, and the average error percentage is less than 10%. By integrating the usage of a lightweight inertial profiler and the bicycle motion analysis model, riding smoothness of bicycle route networks can be assessed effectively and efficiently.