Value pricing is now an accepted strategy for congestion and demand management in metropolitan areas. Along with alternate congestion management strategies, many transportation agencies have started looking at value pricing as a method to help financial shortfalls of new congestion management projects. Value pricing allows revenue collected from toll facilities to reduce operational concerns with underutilized High Occupancy Vehicle (HOV) facilities and relieves environmental concerns by reducing travel demand. Recently, transportation agencies have become increasingly interested in a high-occupancy toll (HOT) lane value pricing system with time-dependent tolls or dynamic tolls that change by the congestion level. However, there is a lack of proper travel demand forecasting tools that can evaluate and determine the impacts of pricing on travelers' decision in relation to congestion. The current methods use aggregated and zonal based approaches that lack the capability of tracing individual travelers through the supply network in order to capture his/her travel decisions as it pertains to the estimated cost for toll usage. The conventional models do not consider individual traveler socio-economic characteristics, particularly the heterogeneous value of time (VOT). TRANSIMS (Transportation Analysis Simulation System) differs from current travel demand forecasting methods in its underlying concepts and structure. These differences include a consistent and continuous representation of time, a detailed representation of persons and households, time-dependent routing, and a person-based Microsimulator. The TRANSIMS Microsimulator is the only simulation tool that maintains the identity of the traveler throughout the simulation and is capable of accessing the database of each individual (e.g., income, age, trip purpose). It traces the movement of people as well as vehicles on a second-by-second basis. Although TRANSIMS environment has significantly improved over the past few years, there are still issues that need to be improved upon including: the pricing of a HOT lane with dynamic tolls and the rescheduling of activities (i.e., departure time choice model) in response to network conditions. The primary objectives of this study are to improve functions of TRANSIMS by modifying source codes in order to utilize non-linear, individual VOT function in route choice of a HOT lane value pricing system, to implement 15-min dynamic tolls that vary by level of service (i.e., volume/capacity ratio) in the HOT lane(s) and to develop departure time choice model. Testing the proposed methodologies using real-world data as case studies and evaluating the impacts of dynamic tolls and/or departure time choice model are other objectives of this study. The test site of the HOT lane system is a segment of I-5 northbound from Hwy 217 to I-405 near the central business district (CBD) in Portland metropolitan region, Oregon. The experimental analyses of the application of dynamic tolls and individual VOT demonstrate the feasibility of the proposed simulation methodology. The outputs from the microscopic analysis clearly indicate the effectiveness of the analysis in scrutinizing travelers' route choice behavior based on different socio-economic and travel characteristics when different toll rates are applied. The effects of individual VOT on route choice are consistent with intuition; that is, travelers with higher VOTs are more likely to choose the HOT lane(s). In addition, the impacts of various tolls on route choice are analyzed on the basis of socio-economic and trip characteristics of each traveler. In addition to the development of the dynamic value pricing along with individual VOT, the departure time choice model is also developed. The proposed method is a post-processing of route choice and represents a sequential decision making process of travelers who want to depart early or late based on congestion, individual attributes and activity characteristics. This paper presents the results of a departure time choice model and its impacts on a HOT lane system using Portland, Oregon as a case study. The results show that 13.9% of households did change their departure time because of congestion and/or tolls. / Ph. D.

A 15.5-mile portion of the I-85 high-occupancy vehicle (HOV) lane in the metropolitan area of Atlanta, GA was converted to a high-occupancy toll (HOT) lane as part of a federal demonstration project designed to provide a reliable travel option through this congested corridor. Results from the I-85 demonstration project provided insight into the results that may follow the Georgia Department of Transportation's planned implementation of a $16 billion HOT lane network along metropolitan Atlanta's other major roadways [2]. To evaluate the impacts of the conversion, it was necessary to measure changes in corridor travel speed, reliability, vehicle throughput, passenger throughput, lane weaving, and user demographics. To measure such performance, a monitoring project, led by the Georgia Institute of Technology collected various forms of data through on-site field deployments, GDOT video, and cooperation from the State Road and Toll Authority (SRTA). Changes in the HOT lane's speed, reliability or other performance measure can affect the demographic and vehicle characteristics of those who utilize the corridor. The purpose of this particular study was to analyze the changes to the vehicle characteristics by comparing vehicle occupancy, vehicle classifications, and vehicle registration data to their counterparts from before the HOV-to-HOT conversion. As part of the monitoring project, the Georgia Tech research team organized a two-year deployment effort to collect data along the corridor during morning and afternoon peak hours. One year of data collection occurred before the conversion date to establish a control and a basis from which to compare any changes. The second year of data collection occurred after the conversion to track those changes and observe the progress of the lane's performance. While on-site, researchers collected data elements including visually-observed vehicle occupancy, license plate numbers, and vehicle classification [25]. The research team obtained vehicle records by submitting the license plate tag entries to a registration database [26]. In previous work, vehicle occupancy data were collected independently of license plate records used to establish the commuter shed. For the analyses reported in this thesis, license plate data and occupancy data were collected concurrently, providing a link between occupancy records of specific vehicles and relevant demographic characteristics based upon census data. The vehicle records also provided characteristics of the users' vehicles (light-duty vehicle vs. sport utility vehicle, model year, etc.) that the researchers aggregated to identify general trends in fleet characteristics. The analysis reported in this thesis focuses on identifying changes in vehicle characteristics that resulted from the HOV-to-HOT conversion. The data collected from post-conversion are compared to pre-conversion data, revealing changes in vehicle characteristics and occupancy distributions that most likely resulted from the implementation of the HOT lane. Plausible reasons affecting the vehicle characteristics alterations will be identified and further demographic research will enhance the data currently available to better pinpoint the cause and effect relationship between implementation and the current status of the I-85 corridor. Preliminary data collection outliers were identified by using vehicle occupancy data. However, future analysis will reveal the degree of their impact on the project as a whole. Matched occupancy and license plate data revealed vehicle characteristics for HOT lane users as well as indications that the tested data collectors are predominantly synchronized when concurrently collecting data, resulting in an argument to uphold the validity of the data collection methods. Chapter two provides reasons for why HOT lanes were sought out to replace I-85's HOV lanes. Chapter two will also provide many details regarding how the HOT lanes function and it will describe the role the Georgia Institute of Technology played in the assessment the HOV-to-HOT conversion. Chapter three includes the methodologies used to complete this document while chapter four provides results and analysis for the one year period before the conversion and the one year period after the conversion.

Managed lanes are a form of congestion pricing that use occupancy and toll payment requirements to utilize capacity more efficiently. How socio-spatial characteristics impact users’ travel behavior toward managed lanes is the main research question of this study. This research is a case study of the conversion of a High Occupancy Vehicle (HOV) lane to a High Occupancy Toll (HOT) lane, implemented in Atlanta I-85 on 2011. To minimize the cost and maximize the size of the collected data, an innovative and cost-effective modeling framework for socioeconomic analysis of managed lanes has been developed. Instead of surveys, this research is based on the observation of one and a half million license plates, matched to household locations, collected over a two-year study period. Purchased marketing data, which include detailed household socioeconomic characteristics, supplemented the household corridor usage information derived from license plate observations. Generalized linear models have been used to link users’ travel behavior to socioeconomic attributes. Furthermore, GIS raster analysis methods have been utilized to visualize and quantify the impact of the HOV-to-HOT conversion on the corridor commutershed. At the local level, this study conducted a comprehensive socio-spatial analysis of the Atlanta I-85 HOV to HOT conversion. At the general scale, this study enhances managed lanes’ travel demand models with respect to users’ characteristics and introduces a comprehensive modeling framework for the socioeconomic analysis of managed lanes. The methods developed through this research will inform future Traffic and Revenue Studies and help to better predict the socio-spatial characteristics of the target market.