Dynamic traffic assignment (DTA) is increasingly being considered to model advanced strategies. Capacity is a crucial parameter in the calibration of traffic flow models utilized as part of DTA modeling. The Highway Capacity Manual has been used as the authoritative source for defining and estimating capacity in the United States. With increased traffic detector data availability in recent years, it is now feasible to locally measure capacity at bottleneck spots with different methods. This study investigates the benefits and necessity of implementing DTA in the analysis of advanced strategies, such as managed lanes, and the importance of the calibration of the associated traffic model parameters. In this regard, the importance of coding capacity based on detector measurements in DTA tools is illustrated, particularly when there is evidence that the modeled corridor capacity is lower than estimates based on the Highway Capacity Manual. The shortcomings of utilizing the traffic flow model of static assignment tools for assessing managed lanes, even when the measured capacity values are coded, are also demonstrated; this drawback illustrates the need to utilize simulation-based DTA modeling for such assessments.

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 vi 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.

The recently completed SHRP 2 L08 project developed methods, guidance, and associated computational engines for incorporating travel time reliability into the Highway Capacity Manual (HCM) analyses. This paper presents an investigation of the use of these products to assess advanced traffic management strategies such as incident management and ramp metering. The paper examines the impacts of input parameters to the traffic flow model and of the scenario generation module incorporated as part of the computational engines of the HCM-based reliability estimation procedure of freeway facilities. The results from the calibration of the traffic flow model of the computational engine indicate that adjusting the capacity values to the measured values based on traffic detector data improves the system's ability to replicate real-world queues and travel times. The results show that the methods used to derive incident attributes in the computational engine affect reliability performance measures, particularly the 95th percentile travel time index and the misery index, which are indicators of the worst 5th percentile conditions on the corridor. The assessment based on modeling results indicates that incident management can significantly reduce the 95th percentile travel time index and the misery index. The assessment of ramp metering shows only a slight improvement in reliability, but this result may be because of the inability of the model to assess adaptive ramp metering, which is the main type of ramp metering in use.