abstract: Priced Managed Lanes (MLs) have been increasingly advocated as one of the effective ways to mitigating congestion in recent years. This study explores a new and innovative pricing strategy for MLs called Travel Time Refund (TTR). The proposed TTR provides an additional option to paying drivers that insures their travel time by issuing a refund to the toll cost if they do not reach their destination within specified travel times due to accidents or other unforeseen circumstances. Perceived benefits of TTR include raised public acceptance towards priced MLs, utilization increase of HOV/HOT lanes, overall congestion mitigation, and additional funding for relevant transportation agencies. To gauge travelers’ interests of TTR and to analyse its possible impacts, a stated preference (SP) survey was performed. An exploratory and statistical analysis of the survey responses revealed negative interest towards HOT and TTR option in accordance with common wisdom and previous studies. However, it is found that travelers are less negative about TTR than HOT alone; supporting the idea, that TTR could make HOT facilities more appealing. The impact of travel time reliability and latent variables representing psychological constructs on travelers’ choices in response to this new pricing strategy was also analysed. The results indicate that along with travel time and reliability, the decision maker’s attitudes and the level of comprehension of the concept of HOT and TTR play a significant role in their choice making. While the refund option may be theoretically and analytically feasible, the practical implementation issues cannot be ignored. This study also provides a discussion of the potential implementation considerations that include information provision to connected and non-connected vehicles, distinction between toll-only and refund customers, measurement of actual travel time, refund calculation and processing and safety and human factors issues. As the market availability of Connected and Automated Vehicles (CAVs) is prognosticated by 2020, the potential impact of such technologies on effective demand management, especially on MLs is worth investigating. Simulation analysis was performed to evaluate the system performance of a hypothetical road network at varying market penetration of CAVs. The results indicate that Connected Vehicles (CVs) could potentially encourage and enhance the use of MLs. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018

This research examined if travelers are paying for travel on managed lanes (MLs) as they indicated that they would in a 2008 survey. The other objectives of this research included estimating travelers’ value of travel time savings (VTTS) and their value of travel time reliability (VOR), and examining the multiple survey designs used in a 2008 survey to identify which survey design better predicted ML traveler behavior. To achieve the objectives, an Internet-based follow-up stated preference (SP) survey of Houston’s Katy Freeway travelers was conducted in 2010. Three survey design methodologies—Db-efficient, random level generation, and adaptive random—were tested in this survey. A total of 3,325 responses were gathered from the survey, and of those, 869 responses were from those who likely also responded to the previous 2008 survey. Mixed logit models were developed for those 869 previous survey respondents to estimate and compare the VTTS to the 2008 survey estimates. It was found that the 2008 survey estimates of the VTTS were very close to the 2010 survey estimates. In addition, separate mixed logit models were developed from the responses obtained from the three different design strategies in the 2010 survey. The implied mean VTTS varied across the design-specific models. Only the Db-efficient design was able to estimate a VOR. Based on this and several other metrics, the Db-efficient design outperformed the other designs. A mixed logit model including all the responses from all three designs was also developed; the implied mean VTTS was estimated as 65 percent ($22/hr) of the mean hourly wage rate, and the implied mean VOR was estimated as 108 percent ($37/hr) of the mean hourly wage rate. Data on actual usage of the MLs were also collected. Based on actual usage, the average VTTS was calculated as $51/hr. However, the $51/hr travelers are paying likely also includes the value travelers place on travel time reliability of the MLs. The total (VTTS+VOR) amount estimated from the all-inclusive model from the survey was $59/hr, which is close to the value estimated from the actual usage. The Db-efficient design estimated this total as $50/hr. This research also shows that travelers have a difficulty in estimating the time they save while using a ML. They greatly overestimate the amount of time saved. It may well be that even though travelers are saving a small amount of time they value that time savings (and avoiding congestion) much higher – possibly similar to their amount of perceived travel time savings. The initial findings from this study, reported here, are consistent with the hypothesis that travelers are paying for their travel on MLs, much as they said that they would in our previous survey. This supports the use of data on intended behavior in policy analysis.

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.

This thesis presents a statistical analysis of weaving in a managed lane system which is evolving from a High-Occupancy Vehicle (HOV) system to a High-Occupancy Toll system (HOT). Weaving was, assessed along the I-85 corridor in Atlanta, during three different phases in the conversion from HOV to HOT: 1) the existing HOV managed lane system prior to conversion to HOT lanes, 2) after restriping of some weaving zones but prior to conversion of the HOV lanes to HOT lanes and, 3) after the HOT managed lane system opened. Each phase was analyzed to see how weaving behavior into and out of the managed lane system was affected by changes in the system. To accomplish the analysis, video was collected using Georgia Department of Transportation cameras along the corridor. The videos were transferred to an Android Tablet, in which an App developed by the research team was used to record data from the videos. Using the processed weaving data, a comparison of weaving activity during each phase was performed. Data were also analyzed across time of day, speed differentials, and whether the weaves in question were performed legally (within established weaving zones) or illegally (across double-solid striped lane markings). After a comparison of weaving behavior along different variables, a regression tree analysis was completed. The analysis showed that weaving intensity increased as the system was converted from HOV to HOT. However, illegal weaving decreased significantly once the HOT system was in place, perhaps due to stricter enforcement or perhaps due to driver response to illegally entering and leaving tolled lanes. The regression tree analyses indicated that weaving intensity was highly dependent upon whether it was legal or illegal to weave and upon the phase of conversion during which the weave occurred.

Advanced modeling tools and methods are essential components for the analyses of congested conditions and advanced Intelligent Transportation Systems (ITS) strategies such as Managed Lanes (ML). A number of tools with different analysis resolution levels have been used to assess these strategies. These tools can be classified as sketch planning, macroscopic simulation, mesoscopic simulation, microscopic simulation, static traffic assignment, and dynamic traffic assignment tools. Due to the complexity of the managed lane modeling process, this dissertation investigated a Multi-Resolution Modeling (MRM) approach that combines a number of these tools for more efficient and accurate assessment of ML deployments. This study clearly demonstrated the differences in the accuracy of the results produced by the traffic flow models incorporated into different tools when compared with real-world measurements. This difference in the accuracy highlighted the importance of the selection of the appropriate analysis levels and tools that can better estimate ML and General Purpose Lanes (GPL) performance. The results also showed the importance of calibrating traffic flow model parameters, demand matrices, and assignment parameters based on real-world measurements to ensure accurate forecasts of real-world traffic conditions. In addition, the results indicated that the real-world utilization of ML by travelers can be best predicated with the use of dynamic traffic assignment modeling that incorporates travel time, toll, and travel time reliability of alternative paths in the assignment objective function. The replication of the specific dynamic pricing algorithm used in the real-world in the modeling process was also found to provide the better forecast of ML utilization. With regards to Connected Vehicle (CV) operations on ML, this study demonstrated the benefits of using results from tools with different modeling resolution to support each other’s analyses. In general, the results showed that providing toll incentives for Cooperative Adaptive Cruise Control (CACC)-equipped vehicles to use ML is not beneficial at lower market penetrations of CACC due to the small increase in capacity with these market penetrations. However, such incentives were found to be beneficial at higher market penetrations, particularly with higher demand levels.

Autonomous vehicles (AVs) are a disruptive technology that is expected to vastly change the current transportation system. AV potential benefits in terms of safety, mobility, efficiency and other impacts types have been documented in the literature. AVs are expected to increase travel demand due to the enhanced ease of making trips and provision of mobility to people currently with travel-limiting disabilities. The potential increase in travel demand, with its attendant congestion, may probably be offset by the transportation network capacity increase due to the reduced operational headways between AVs. However, such capacity benefits can be fully realized only when AVs fully saturate the market, because operating at low headways may be unsafe for Human Driven Vehicles (HDVs). Thus, to promote AV ownership while capturing the capacity benefits of an AV-only traffic stream, the conversion of traditional lanes to AV-exclusive use is prescribed often. In the AV-exclusive lanes, the vehicles can operate at reduced headways and at higher speeds, sharply increasing throughput. However, the metric used frequently by researchers for AV-exclusive lane evaluation is the total system travel time. AV-exclusive lanes may appear to be beneficial in terms of total system travel time but may come at a cost of environmental protection and social equity, the other two elements of sustainable development. Appropriating HDV lanes for AV-exclusive use will cause congestion on HDV lanes thereby increasing their emissions. Further, the AVs benefits may be accompanied by increased cost of HDV travel, which raises questions about equity. This thesis therefore presents a sustainable AV-exclusive lane deployment strategy by formulating and solving a multicriteria bi-level optimization problem with equity-related constraints. Mathematically, the problem is described as a discrete network design problem. Recognizing the difficulty of solving this NIP hard problem, the thesis combines the active set method with heuristic conditionalities to improve computational efficiency. The thesis’s framework can be used by agencies for evaluation and decision support regarding AV-exclusive lane deployment in a manner that fosters long-term sustainability.

Managed lane refers to the application of various operational and design strategies on highway facilities to improve system efficiency and mobility by proactively allocating traffic capacity to different lanes. One of the key elements to understand the behavior changes and underlying causalities in user responses to managed lanes is to examine the value of time (VOT) and value of reliability (VOR). The breadth of this dissertation encompasses two major dimensions of VOT and VOR estimation – distributions or variations across different users and under different circumstances; and influences of unobserved attitudinal characteristics on roadway pricing valuation. To understand travelers’ choice behavior regarding the usage of managed lanes, combined revealed preference (RP) and stated preference (SP) data were used in this study. Mixed logit modeling was applied as the state of the art methodology to capture heterogeneity in users’ choice behavior. The model revealed an average value of $10.68 per hour for VOT and $13.91 per hour for VOR, which are reasonable considering the average household income in the region, and are well within the ranges found in the literature. In terms of user heterogeneity, the mixed logit model was further enhanced by adding interaction effects of variables, which helped recognize and quantify potential sources of heterogeneity in user sensitivities to time, reliability, and cost. The findings indicated that travelers were likely to exhibit higher willingness to pay when they were female, younger (years), older (>54 years), had higher income (> 50 K), driving alone, and traveled on weekdays. Attitudinal aspects are rarely incorporated into roadway pricing analysis. The study herein presents an effort to explore the role of attitudinal factors in drivers’ propensity toward using managed lanes. Model results boded for a significant contribution of attitudinal parameters in the model, both in terms of coefficients and model performance. This study provides a robust approach to quantify user heterogeneity in VOT and VOR and capture the impacts of attitudinal attributes in pricing valuation. The results of this study contribute to a better understanding on what attributes lead to higher or lower VOT and VOR and to what extent.

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.

This paper examines the issues of equity, as applicable to the HOV-to-HOT conversion project planned for the I-85 North corridor in the Metropolitan Atlanta Region. A review of literature is undertaken to describe the typology of transportation equity issues within the wider context of environmental justice, and to highlight socio-economic factors and local and national transportation funding factors that influence people's travel choices and their mobility and accessibility options. Demographic data on the I-85 corridor peak period commuters in Metropolitan Atlanta is analyzed, in addition to results of focus groups polling current Metropolitan Atlanta interstate commuters on the topic of managed lanes during 2008. The thesis makes a conclusion that a final decision about the equity impact of the I-85 HOV-to-HOT conversion is likely not possible without undertaking a Metropolitan area-wide analysis. Some of the equity findings that emerge indicate that there are no significant income differences between the the HOV lane users and general purpose lane I-85 commuters; that there are differences between median incomes of block groups represented by current I-85 commuters (both HOV lane users and general purpose lane users) and median incomes of block groups typical for the base geography; and that investing in Xpress bus service improvements would primarily serve those households with more vehicles than drivers, unless improvements to reverse commute options and feeder bus networks are made. The focus group findings suggest that current interstate highway users in Metropolitan Atlanta, originating in the suburbs, are generally accepting of the HOT concept and recognize the value of travel time savings.