Travel demand analysis has traditionally focused on exploring and modeling travel behavior on weekdays. This emphasis on weekday travel behavior analysis was largely motivated by the presence of well-defined peak periods, primarily associated with the journey to and from work. Most travel demand models are based on weekday travel characteristics and purport to estimate traffic volumes for daily or peak weekday conditions. Much of the planning and policy making that occurs in transportation arena in response to weekday travel behavior and forecasts. More recently, there had been a growing interest in exploring, understanding, and quantifying weekend travel characteristics. The ability to do this has been limited due to the non-availability of travel survey data that includes weekend trip information. Most travel surveys collect information about weekday travel behavior and ignore weekend days. However, the 2001 National Household Travel Survey includes a substantial sample that provided detailed trip information for weekend days and therefore this dataset offers a key opportunity to explore in-depth weekend travel characteristics. Weekend travel behavior is expected to be substantially different from the weekday travel behavior for difference in several spatial and temporal constraints. The difference in constraints can also lead to a change in trip chaining patterns on weekdays and weekends. Differences in constraints coupled with socio-economic changes characterized by greater disposable income, time-constrained lives, and greater discretionary activity opportunities point towards the growing role that weekend travel behavior is going to play in transportation planning and policy-making. This thesis provides a comprehensive analysis of weekend travel behavior using the 2001 NHTS. Differences and similarities between weekday and weekend travel behavior are identified and presented for different urban areas sizes varying according to Metropolitan Statistical Area (MSA) size. Models of weekend and weekday travel behavior are developed to capture the structural relationship of socio-demographics, activity durations, and travel duration are developed using structural equations modeling approaches to better understand the relationships among these aspects of travel behavior on weekdays and weekends. This report is supposed to act as an updated data guide to the National Cooperative Highway Research Program's (NCHRP) Report 365 titled "Travel Estimation Techniques for Urban Planning" aims at studying the changes in behavioral characteristics between two categories of the day of week - a weekday and a weekend based on personal, household and trip characteristics.

A before and after comparison of vehicle occupancy distributions for the Atlanta, GA I-85 HOV to High Occupancy Toll (HOT) lane conversion scheduled for summer 2011, will assess the changes in vehicle and passenger throughput associated with lane conversion. The field deployment plans and data collection methodologies developed for the HOT evaluation were the result of a comprehensive literature review, an examination of previous data collection methods, an evaluation of the physical characteristics of the I-85 corridor, and the testing of a variety of equipment/manpower strategies. The case study in this thesis evaluates the established vehicle occupancy methodology for consistency across multiple observers during parallel data collection efforts. The differences noted in exact matches and consistency across the use of the "uncertain" values developed for field implementation is specifically assessed. Results from this study are the first step in assessing the validity of the data collection methods used on the HOT corridor and will yield recommendations for improving the methodology for future occupancy studies. A separate assessment of the accuracy of the methodology is also being conducted by the research team and will be published under a separate cover.

The conversion of high-occupancy vehicle (HOV) lanes to high-occupancy toll (HOT) lanes is currently being implemented in metro Atlanta on a demonstration basis and is under consideration for more widespread adoption throughout the metro region. Further conversion of HOV lanes to HOT lanes is a major policy decision that depends on knowledge of the likely impacts, including the equity of the new HOT lane. Rather than estimating these impacts using modeling or surveys, this study collects revealed preference data in the form of observed vehicle license plate data and vehicle occupancy data from users of the HOV corridor. Building on a methodology created in Spring 2011, researchers created a new methodology for matching license plate data to vehicle occupancy data that required extensive post-processing of the data. The new methodology also presented an opportunity to take an in-depth look at errors in both occupancy and license plate data (in terms of data collection efforts, processing, and the vehicle registration database). Characteristics of individual vehicles were determined from vehicle registration records associated with the license plate data collected during AM and PM peak periods immediately prior to the HOV lanes conversion to HOT lanes. More than 70,000 individual vehicle license plates were collected for analysis, and over 3,500 records are matched to occupancy values. Analysis of these data have shown that government and commercial vehicle were more prevalent in the HOV lane, while hybrid and alternative fuel vehicles were much less common in either lane than expected. Vehicle occupancy data from the first four quarters of data collection were used to create the distribution of occupancy on the HOV and general purpose lane, and then the matched occupancy and license plate data were examined. A sensitivity analysis of the occupancy data established that the current use of uncertain occupancy values is acceptable and that bus and vanpool occupancy should be considered when determining the average occupancy of all vehicles on the HOV lane. Using a bootstrap analysis, vehicle values were compared to vehicle occupancy values and the results found that there is no correlation between vehicle value and vehicle occupancy. A conclusions section suggests possible impacts of the findings on policy decisions as Georgia considers expanding the HOT network. Further research using these data, and additional data that will be collected after the HOT lane opens, will include emissions modeling and a study of changes in vehicle characteristics associated with the HOT lane conversion.