The implementation of managed lanes (MLs), also known as dynamically priced express lanes, to improve freeway traffic flow and personal throughput is on the rise. Congestion pricing is increasingly becoming a common strategy for congestion management, often requiring microscopic simulation during both planning and operational stages. VISSIM is a recognized microscopic simulation software used for analyzing the performance of managed lanes (MLs). This thesis addressed two important microscopic simulation issues that affect the evaluation results of MLs. One of the microscopic simulation issues that has not yet been addressed by previous studies is the required minimum managed lane routing decision (MLRD) distance upstream of the ingress point of MLs. Decision distance is an optimal upstream distance prior to the ingress at which drivers decide to use MLs and change lanes to orient on a side of MLs ingress. To answer this question, this study used a VISSIM model simulating I-295 proposed MLs in Jacksonville, Florida, United States (U.S), varying the MLRD point at regular intervals from 500 feet to 7,000 feet for different levels of service (LOS) input. Three measures of effectiveness (MOEs) - speed, the number of vehicles changing lanes, and following distance - were used for the analysis. These MOEs were measured in the 500 feet zone prior to the ingress. The results indicate that as the LOS deteriorates, speed decreases, the number of vehicles changing lanes increases, and the following distance decreases. When the LOS is constant, the increase in the MLRD distance from the ingress point was associated with the increase in the speed at the 500 feet zone prior to the ingress, less number of lane changes, and the increase in following vehicle gap. However, the MOEs approached constant values after reaching a certain MLRD distance. LOS D was used to determine the minimum MLRD distance to the ingress of the MLs. The determined minimum MLRD distances were 4,000 and 3,000 feet for 6 and 3 lane segments prior to the ingress point, respectively. Another issue addressed in this thesis is the managed lane evaluation (MLE) outputs, which include speed, travel time, density, and tolls. In computing the performance measures, the existing VISSIM managed lane evaluation (EVMLE) tool is designed to use the section starting at the point when vehicles are assigned to use MLs, also known as the MLRD point, which is located upstream of the ingress. The longer the MLRD distance from the ingress, the more the EVMLE tool uses the traffic conditions of the MLs traffic before entering the ML in its computations. This study evaluates the impact of the MLRD distance on the EVMLE outputs and presents a proposed algorithm that addresses the EVMLE shortcomings. In order to examine the influence of the MLRD distance on the outputs of the above-mentioned two algorithms, simulation scenarios of varying MLRD distances from 500 ft to 7,000 feet from the ingress were created. For demonstration purposes, only the speed was used to represent other performance measures. The analysis of variance (ANOVA) test was performed to determine whether there was a significant difference in the speed results with the change in the MLRD distance. According to the ANOVA results, the EVMLE tool produced ML speeds that are MLRD dependent, yielding lower speeds with an increased MLRD distance. On the other hand, the ML speed results from the proposed algorithm were fairly constant, regardless of the MLRD distance.

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

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.