A Methodology to Estimate Time Varying User Responses to Travel Time and Travel Time Reliability in a Road Pricing Environment

Alvarez, Patricio

doi:10.25148/etd.fi12050237

Cited by 1 publication

(1 citation statement)

References 154 publications

(128 reference statements)

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“…Studies show that the learning process that leads to a high correlation between saved time and ML selection takes about 60 days. In other words, it takes 60 days of adjustment prior to choosing ML over GPL, based on the saved travel time (Alvarez, 2012).…”

Section: Managed Lane Modeling Frameworkmentioning

confidence: 99%

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Shabanian¹

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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.

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Section: Managed Lane Modeling Frameworkmentioning

confidence: 99%

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Shabanian¹

View full text Add to dashboard Cite

show abstract

A Methodology to Estimate Time Varying User Responses to Travel Time and Travel Time Reliability in a Road Pricing Environment

Cited by 1 publication

References 154 publications

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

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