Improving road safety through proper pavement maintenance is one of the goals of pavement management. Many studies have found that pavement conditions significantly influence traffic safety. Although several studies have explored the relationship between pavement conditions and crash occurrence, the effect of poor pavement conditions on crash severity levels has not been investigated, especially by using a discrete model that can handle ordered data. This paper focuses on the development of the relationship between poor pavement conditions and crash severity levels using a series of Bayesian ordered logistic models for low/medium/high speed roads and single/multiple collision cases. The Bayesian ordered logistic regression models indicated that the poor pavement condition decreases the severity of single-vehicle collisions on low-speed roads whereas it increases their severity on high-speed roads. On the other hand, the poor pavement condition increases the severity of multiple-vehicle crashes on all roads. Findings of this study can assist transportation agencies at the federal, state, and local levels to select appropriate pavement maintenance and rehabilitation strategies to reduce traffic crash severity levels.

Traffic performance of a highway is expressed in terms of level of service (LOS). It is a major input to important decisions on public fund investments. Because of its unique operational characteristics, evaluation of traffic performance on two-lane highways is a critical task. This research examines the applicability of various performance measures for two-lane highways under mixed traffic conditions and assigns threshold values for different levels of service based on the best performance measure observed. Graphical and statistical analyses are carried out among the six most popular performance measures and traffic volume, but none of the considered measures are found to be reliable. A new parameter, number of followers (NF), which is defined as the number of vehicles traveling with a gap less than 2.6 s, is found to have good correlation with traffic volume but it does not reflect the true congestion conditions of a roadway. Therefore, the NF is further modified to the number of followers as a proportion of capacity (NFPC), and this parameter demonstrates a very strong correlation with traffic volume. Threshold values are proposed for the different LOS based on NFPC for mixed traffic conditions.

Physical disability is as a multifaceted phenomenon, whose manifestations may be difficult to realize and properly address when designing or improving urban built environments. The mere application of technical requirements cannot account for designing accessible public spaces and infrastructures for pedestrian mobility. In this perspective, unsignalized crosswalks are emblematic. Design recommendations for them are prevalently focused on purely technical aspects, such as pedestrian walking speed and visibility problems. Apprehension and nervousness experienced by pedestrians when attempting to traverse the roadway is usually neglected, but this uneasinesses may weaken the autonomy and comfort of pedestrians with disabilities. Interaction with approaching drivers is a decisive factor. It is known that unimpaired pedestrians attempt hazardous crossings if they realize that drivers do not observe their right-of-way. The question arises of investigating street-crossing behavior of people with different impairments and their interaction with drivers. Field surveys was carried out wherein people with different disabilities were requested to traverse the roadway using unsignalized crosswalks. Survival analyses were then applied on gathered waiting times at the curbside to ascertain the way in which disability status and other characteristics influence the interaction between drivers and pedestrians. However, given the complexity of the examined phenomenon, the analytical approach must act in synergy with qualitative observations and people’s concerns for acquiring the most possible complete framework. Street crossing was particularly arduous for people in wheelchairs, while individuals with hearing impairments revealed unexpected versatility against a hostile urban environment.

Many surveyors who work in/near live traffic encounter unique safety challenges as they are frequently exposed to fast moving vehicles. Unlike traditional work zones, safety countermeasures at surveying sites are usually limited to advanced-warning signs, traffic cones and reflective safety vests. As a result, motorists may not be well alerted for the presence of surveyors. Because of the unique safety challenges encountered by surveyors and the motorists, this study aims to quantify the effectiveness of using temporary rumble strips, as a potential safety countermeasure in enhancing safety at surveying work sites. A type of portable plastic rumble strip (PPRS) was tested at eight land surveying sites in New Jersey. The effectiveness of PPRS was evaluated based on selected surrogate measures of effectiveness (SMOE) that were extracted from the video data recorded during the 2-h field test at each site. Statistically significant reductions in driving speed and speeding proportion, and increases in braking rate were found when the rumble strips were deployed during the field tests. These observed changes suggest that the deployment of PPRS can be a useful countermeasure to increase motorists’ awareness at land surveying sites. The findings can serve as a reference for practitioners when deploying temporary and portable traffic control device in these unique short-term work zones.

A problem commonly encountered in airport pavement management is how to deal with pavement condition and performance databases with various extents of missing data. While robust imputation techniques are now available to handle the problem of missing data, there are little to no guidelines available to assist airport engineers in evaluating the level of impact that data imputation has on the decisions made with respect to airport pavement maintenance. This research attempts to address the aforementioned issue by analyzing how different degrees of missing data would affect the maintenance decision concerning runway pavement sections in need of friction improvement. Using the runway friction data sample for illustration, 200 sets of data with varying degrees of missingness were generated. The generated data sets cover the following 10 levels of missingness: 5.55, 16.66, 25, 36.11, 44.44, 55.55, 66.66, 75, 86.11, and 91.66%. For each level of missingness, a total of 20 data sets with different patterns of missing data were randomly generated, and 20 plans of runway friction maintenance were derived with the help of imputed data. The derived maintenance plans, using data sets containing imputed data, were compared with the actual plan based on the original complete data set, and statistical hypothesis testing was performed to determine the reliability of maintenance plans derived using data sets containing missing data. The results of the analysis indicated a declining trend of reliability with increasing extent of missing data. Subsequent analysis of the results led to the conclusion that if the proportion of missing data remains under 40%, the derived maintenance plan is statistically indifferent from the actual plan at the 95% confidence level.