Increasingly, sustainability is a key indicator for planning transportation projects. As a result, evaluating the applicability of various transportation sustainability rating systems (TSRS) for adoption is important for the transportation industry in general, and for specific transportation organizations in particular. Several contending TSRSs are emerging, and provide various methods of guidance with regard to sustainability for infrastructure projects throughout project delivery. Different TSRSs, for example, employ different methods to determine or quantify sustainability and emphasize different sustainability factors. As a result, comparing the various systems and their suitability for implementation by a specific transportation organization can be a challenge. In this research, the authors apply the Analytical Hierarchy Process (AHP) as the method to assist in such an evaluation according to the Colorado Department of Transportation’s (CDOT’s) preferences. Application of AHP in previous research resulted in identification of the Infrastructure Voluntary Evaluation Sustainability Tool (INVEST) as the most suitable TSRS for CDOT. Here we compare previous results to CDOT’s recently developed custom TSRS. Findings indicate that CDOT’s own custom system ranks fourth compared to existing and publicly available TSRSs according to its own stated preferences. This unexpected result highlights unique issues and challenges related to developing an effective TSRS for a Department of Transportation (DOT), as well as potential issues and biases when applying AHP to assess sustainability tools.

In order to optimally schedule railway track maintenance operations, it is essential to accurately estimate future track conditions. This study proposes a railway track geometry degradation model that considers uncertainties in the forecast by defining a track geometry reliability parameter. The degradation model is integrated in a multiobjective optimization approach to assess railway track maintenance strategies considering a cost–reliability trade off. Finally, a numerical application of the model to a real case study is presented. The results show the usefulness of the proposed approach to guarantee a required track geometry performance with effective maintenance investments.

The objectives of this study are to simulate the Thornthwaite moisture index (TMI) for zones within the Atlantic provinces of Canada (APC) during three 30-year periods in the 21st century and to estimate the interactive effect of TMI and simulated freight traffic loads on the deterioration of pavement structure during the same period. Regional Highways 1, 2, 7, 15, 16, 102, and 104 connecting the APC are considered as the case study. Integration of spatial input-output and transportation models simulates freight movements on the selected regional highways during the period of 2012–2100. TMI is estimated using downscaled average monthly precipitation and temperature at 34 stations within the APC. Simulated traffic loads and TMI are applied to mechanistic modeling of roughness progression on the pavement structure. The findings of this study show that an increase in TMI can cause 11–68% increase of roughness progress rate on pavement structure.

This paper presents a decision-making framework based on a travel time reliability methodology developed under the U.S. Strategic Highway Research Program. Existing methods consider a set of predefined prevailing conditions for the analysis of freeway facilities as the base case. However, a reliability analysis accounts for multiple recurring and nonrecurring congestion sources to estimate the travel time distribution over a long time horizon. This approach considers variations in traffic demand levels, inclement weather conditions, and incidents that occur stochastically on a freeway facility. Several performance measures are defined based on the travel time distribution, which comprehensively cover the full range of operational conditions on the system. Based on the proposed decision-making framework, mobility strategies can be identified, evaluated, and improved.

Random regret minimization (RRM) interpretations of discrete choices are growing in popularity as a complementary modeling paradigm to random utility maximization (RUM). While behaviorally very appealing in the sense of accommodating the regret of not choosing the best alternative, studies to date suggest that the differences in willingness to pay estimates, choice elasticities, and choice probabilities compared to RUM are small. However, the evidence is largely based on a simple multinomial logit (MNL) form of the RRM model. This paper revisits this behavioral contrast and moves beyond the multinomial logit model to incorporate random parameters, revealing the presence of preference heterogeneity. The important contribution of this paper is to see if the extension of RRM-MNL to RRM-mixed logit in passenger mode choice widens the behavioral differences between RUM and RRM. The current paper has identified a statistically richer improvement in fit of mixed logit compared to multinomial logit under RRM (and RUM) but found small differences overall between the empirical outputs of RUM and RRM, with no basis of an improved model fit between these two nonnested model forms. The inclusion of both model forms should continue to inform the likely range of behavioral outputs during investigation of a broader range of process heuristics designed to capture real world behavioral response.

Accurate traffic state information is essential for both travelers and transportation agencies. In the past, traffic condition data were usually collected by a government agency using its own sensors. Recently, a number of private sector companies have started selling travel-time and speed data collected using probe vehicles, which provides a viable opportunity to outsource traffic data collection. Because these data sources and their related algorithms are proprietary, the reliability and accuracy of this private sector data is often an important issue for transportation agencies. Previous studies have examined the accuracy of private sector data on freeways, but arterials have not been examined extensively. Arterials represent a fundamentally more challenging environment for probe vehicle data given the larger variance in travel times created by traffic signals and other intermediate access points. In the research, the quality of private sector data on arterials is evaluated by utilizing Bluetooth travel-time data as the ground truth. The evaluation is conducted from two perspectives: the ability to track real-time conditions, and the ability to identify long-term traffic state changes. The study sites are three signalized arterials in the state of Virginia. The results indicate that the private sector data evaluated were not suitable for real-time applications, but could be used to measure long-term traffic state changes for performance measurement programs.

This paper develops a new two-dimensional numerical model of vehicle/discontinuous slab track dynamic interaction. The vehicle is simulated as a multibody system with 10 degrees of freedom. The slab track is represented by a three-layer model that consists of a continuous rail layer, a discontinuous concrete slab layer, and a continuous concrete base layer. All the slab track layers are modeled by Euler-Bernoulli beam elements with finite lengths. In the New Model (NM), the wheel/rail contact is modeled by nonlinear Hertz springs for the first time. This results in an improvement in the modeling of the slab track/rolling-stock dynamic interaction. The governing differential equations of the model are derived in the time domain and solved in a coupled form. Through several numerical examples, the accuracy and the computational efficiency of the NM in various vehicle/slab track conditions is illustrated.

Front and rear axle braking performance in a vehicle is a well-documented subject in reference to longitudinal forces, but this statement is not valid for vertical forces. To analyze this behavior, a commercial vehicle (equipped with an anti-lock braking system) was instrumented with strain gauges and one accelerometer for testing in a closed course proving ground located in Brazil. By using the vehicle’s springs as load cells, the vertical load applied to the pavement was evaluated during different braking maneuvers. Using a bidirectional ANOVA and a post hoc Tukey’s honest significant differences (HSD) test, statistical differences of the transferred load were evaluated between the vehicle’s initial speed and brake pedal position. Although it is recommended to repeat the same procedure on different vehicles and varying pavements surfaces, the results indicate that, unlike the brake pedal position, the vehicle’s initial speed does not influence the vertical load applied to the pavement during braking maneuvers.