Collecting load tickets is an example of an antiquated practice that puts inspectors in harm’s way either adjacent to traffic, in close proximity to moving or backing equipment, or at times requires climbing onto trucks to reach tickets. Technology exists to collect this information electronically allowing for safer, efficient inspection methods. Departments of Transportation are charged with inspecting an increasing work load with a diminishing number of inspection staff. Recently, doing more with less has led to the prioritization of inspection activities and resulted in less collection of data and visual inspection on projects. Technology advancements are available to improve data collection and provide for more efficient inspection. Using GPS and GIS technology tied into electronic scale report-out systems, a fleet tracking system traces haul routes, reports travel time and tonnage, and even assists contractors with equipment matching and balancing. Data from this system coupled with other technologies remote monitoring of temperature, intelligent compaction, and network enabled cameras provide an opportunity to enhance inspection and increase construction inspection productivity all the while enriching detail of project records. The contribution of this paper is to provide a framework in which to combine these technologies into a multi-faceted, enhanced inspection approach.

The goal of this research was to examine the potential predictive ability of socioeconomic and demographic data for drivers on Kentucky crash occurrence. Identifying unique background characteristics of at-fault drivers that contribute to crash rates and crash severity may lead to improved and more specific interventions to reduce the negative impacts of motor vehicle crashes. The driver-residence zip code was used as a spatial unit to connect five years of Kentucky crash data with socioeconomic factors from the U.S. Census, such as income, employment, education, age, and others, along with terrain and vehicle age. At-fault driver crash counts, normalized over the driving population, were used as the dependent variable in a multivariate linear regression to model socioeconomic variables and their relationship with motor vehicle crashes. The final model consisted of nine socioeconomic and demographic variables and resulted in a R-square of 0.279, which indicates linear correlation but a lack of strong predicting power. The model resulted in both positive and negative correlations of socioeconomic variables with crash rates. Positive associations were found with the terrain index (a composite measure of road curviness), travel time, high school graduation and vehicle age. Negative associations were found with younger drivers, unemployment, college education, and terrain difference, which considers the terrain index at the driver residence and crash location. Further research seems to be warranted to fully understand the role that socioeconomic and demographic characteristics play in driving behavior and crash risk.

Over recent years, organizations such as the Federal Highway Administration and Departments of Transportation across the United States have showed interest in automating highway construction processes. The addition of e-Construction and other advanced technologies can significantly improve the efficiency and safety of highway paving operations, specifically paving inspections. Activities such as collecting load tickets, tracking pavement lay-down temperatures, and monitoring roller movement are antiquated practices that DOT inspectors perform during paving operations. E-Ticketing, Paver Mounted Thermal Profiling, and Intelligent Compaction were proposed to automate paving inspections and were recently tested in two resurfacing pilot projects in the state of Kentucky. Findings from the projects indicate that the three technologies display great potential in improving safety and efficiency of paving inspections. The contribution of this thesis is to document the research effort, evaluate the effectiveness of the technologies compared to the traditional practices, and discuss the lessons learned for industry practitioners.

As-built drawings are the traditional method used by the construction industry to record changes made during construction. As-builts provide valuable information for new design projects as well as rehabilitation and remediation projects. The completeness and accuracy of these plans are essential for transportation industries and their success. While the importance of as-builts is widely recognized, the process of creating them has proven to be difficult. It is a time consuming process and entities often lack the resources necessary to complete accurate and detailed as-builts. After an investigation of current as-built operations within State Transportation Agencies, recommendations have been made to redefine construction "as-built" plans to meet current state transportation needs. First, the importance of a central storage location accessible to all stakeholders cannot be overemphasized. Along with a central storage location, standard guidelines should be developed regarding what information is required to be included within as-built plans. This study's approach to developing such guidelines included meeting with as-built end users and formulating a list of requested information. To ensure as-builts are being completed on time and accurately, it is recommended that as-built plans be developed throughout the project using simple to use editing software on iPads. A PDF editor is ideal for as-built development as PDF is the requested format by most end users. Finally, to ensure this process is being followed by construction, a liaison between as-built developers and users is recommended.

Transportation infrastructure improvement projects are typically huge and have significant economic and environmental effects. Forecasts of demand of the facility in the form of traffic level help size the project as well as choose between several alternatives. Inaccuracy in these forecasts can thus have a great impact on the efficiency of the operational design and the benefits accrued from the project against the cost. Despite this understanding, evaluation of traffic forecast inaccuracy has been too few, especially for un-tolled roads in the United States. This study, part of a National Cooperative Highway Research Program (NCHRP) funded project, bridges this gap in knowledge by analyzing the historical inaccuracy of the traffic forecasts based on a database created as part of the project. The results show a general over-prediction of traffic with actual traffic deviating from forecast by about 17.29% on an average. The study also compares the relative accuracy of forecasts on several categorical variables. Besides enumerating the error in forecasts, this exploration presents the potential factors influencing accuracy. The results from this analysis can help create an uncertainty window around the forecast based on the explanatory variables, which can be an alternate risk analysis technique to sensitivity testing.

The Crash Modification Factor (CMF) clearinghouse can be used to estimate benefits for specific highway safety countermeasures. It assists safety professionals in the allocation of investments. The clearinghouse contains over 7000 entries of which only 446 are categorized as intelligent transportation systems or advanced technology, but none directly address connected or autonomous vehicles (CAVs). Further, the effectiveness of highway safety countermeasures is assumed to remain constant over time, an assumption that is particularly problematic as new technologies are introduced. For example, for the existing fleet of human-driven vehicles, installation of rumble strip can potentially reduce “run-off-road” crashes by 40%. If specific CAV technologies, e.g., lane-tracking, can work without rumble strips, and say, half of all cars are so equipped, only half of the fleet will benefit, reducing the benefits of rumble strips by a commensurate amount. Benefits of the two improvements, e.g., rumble strips and automated vehicles, should not be double-counted. As there will still be human-driven and/or non-connected vehicles in the fleet, conventional countermeasures are still necessary, although returns on conventional safety investments may be significantly overestimated. This is important as safety investments should be optimized and geared to future, not past fleets. Moreover, as CMFs are based on historical events, the types of crashes experienced by human-driven, un-connected cars are likely to be much different in the future. This research presents methods to estimate the safety benefits that autonomous vehicles have to offer and the changes needed in CMFs as a result of their adoption. This will primarily be achieved by modifying and enhancing a tool co-developed by the Fellow that estimates the safety benefits of different levels of autonomy. This tool, ddSAFCAT, estimates CAV safety benefits using real-world data for crashes, market penetration, and effectiveness.

The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the development's negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example. The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITE's Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITE's TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes. This research developed and tested a method to adjust ITE's Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITE's Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories. 195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITE's Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments. For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITE's Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions.

This research focuses on the data quality control methods for evaluating the performance of Weigh-In-Motion (WIM) systems on Oregon highways. This research identifies and develops a new methodology and algorithm to explore the accuracy of each station's weight and spacing data at a corridor level, and further implements the Statistical Process Control (SPC) method, finite mixture model, axle spacing error rating method, and data flag method in published research to examine the soundness of WIM systems. This research employs the historical WIM data to analyze sensor health and compares the evaluation results of the methods. The results suggest the new triangulation method identified most possible WIM malfunctions that other methods sensed, and this method unprecedentedly monitors the process behavior with controls of time and meteorological variables. The SPC method appeared superior in differentiating between sensor noises and sensor errors or drifts, but it drew wrong conclusions when accurate WIM data reference was absent. The axle spacing error rating method cannot check the essential weight data in special cases, but reliable loop sensor evaluation results were arrived at by employing this multiple linear regression model. The results of the data flag method and the finite mixed model results were not accurate, thus they could be used as additional tools to complement the data quality evaluation results. Overall, these data quality analysis results are the valuable sources for examining the early detection of system malfunctions, sensor drift, etc., and allow the WIM operators to correct the situation on time before large amounts of measurement are lost.

Wider bike facilities intuitively accommodate a greater number of cyclists in the same amount of time, but specific queue discharge characteristics associated with varying widths and/or types of bike facilities have not been thoroughly documented. The focus of this research analyzed queues of cyclists at four signalized intersections in Portland, OR with varying widths on the approach and downstream intersection legs. A total of 2,820 cyclists within 630 groups of queued cyclists were observed at five different intersection layouts in Portland, Oregon. The layouts consisted of: a standard bike lane six feet wide connecting bicyclists to a standard bike lane six feet wide, a standard bike lane five feet wide connecting bicyclists to two standard bike lanes each five feet wide, a buffered bike lane 12 feet wide connecting bicyclists to a standard bike lane 6.5 feet wide, a bike box 21 feet wide connecting bicyclists to a buffered bike lane 10 feet wide, and a bike box 15 feet wide connecting bicyclists to two standard bike lanes each five feet wide. For each configuration, the following aspects were analyzed: average headway per cyclist within each queue, the time required for queues to enter the intersection, the time required for queues to clear the intersection, the number of cyclists within queues, the width of the bicycle facilities, the approach grade, and the utilization of a bike box at the intersection approach if it was present. The first major focus of the analysis reviewed the average headway values associated with each observed queue of cyclists. The queue size with the lowest mean of the average headway was for groups of seven cyclists with an average headway of approximately 0.8 seconds per cyclist. For queues larger than seven in size, the mean of the average headway remained relatively stable until queues of 12 in size and started to slightly increase toward approximately 1.0 seconds for queues larger than 12 cyclists. In addition, it appears that utilization of a bike box has a potential relationship with a reduced average headway as compared to queues that do not utilize a bike box. The associated reduction in the mean of the average headway was approximately 0.2 to 0.3 seconds per cyclist for queues of three or more in size. The second major focus of the analysis reviewed the queue discharge rate associated with each observed queue of cyclists. The results appear to potentially indicate that wider bike facilities approaching an intersection, wider receiving bike facilities, or utilization of a bike box generally discharge queues of bicyclists into the intersection over a shorter amount of time as compared to facilities that are narrower or underutilized. The installation of a bike box at one of the study intersections increased the approach width from five to 15 feet and resulted in consistently lower average discharge times for all queue sizes, a reduction of greater than one second for queues of two cyclists to as much as about four seconds for queues of nine cyclists. The third major focus of the analysis reviewed the intersection clearance time associated with each observed queue of cyclists. The results appear to potentially indicate that wider bike facilities approaching an intersection, wider receiving bike facilities, or utilization of a bike box generally clear queues of bicyclists through the intersection over a shorter amount of time as compared to facilities that are narrower or underutilized.

Traffic simulation has become an invaluable part of the traffic engineering toolbox. However, the majority of driver models are designed to recreate traffic performance based on interactions among vehicles. In keeping with this pursuit, most are fundamentally built to avoid collisions. This limits the applicability of using these models for addressing safety concerns, especially those regarding pedestrian safety performance. However, by explicitly including some of the sources of human error, these limitations can, in theory, be overcome. While much work has been done toward including these human factors in simulation platforms, one key aspect of human behavior has been largely ignored: driver distraction. This work presents a novel approach to inclusion of driver distraction in a microsimulation or agent-based model. Distributions of distraction events and inter-distraction periods are derived from eye-glance data collected during naturalistic driving studies. The developed model of distraction is implemented -- along with perception errors, visual obstructions, and driver reaction times -- in a simulated mid-block pedestrian crossing. The results of this simulation demonstrate that excluding any of these human factors from the implemented driver model significantly alters conflict rates observed in the simulation. This finding suggests that inclusion of human factors is important in any microsimulation platforms used to analyze pedestrian safety performance.