Lesson

Use Analysis, Modeling, and Simulation (AMS) to identify gaps, determine constraints, and invest in the best combination of Integrated Corridor Management (ICM) strategies.

Experience with the AMS framework on the ICM Test Corridor, San Francisco.


September 2008
United States


Background (Show)

Lesson Learned

The Analysis, Modeling, and Simulation (AMS) framework enabled cost-effective ICM strategies to be identified and evaluated on the ICM Test Corridor. The methodology was found to be robust and flexible enough to support the planning and evaluation activities on other major transportation corridors. In view of applying AMS to other Pioneer Corridors, the following guidance was provided:
  • Be prepared to significantly modify the analysis framework and customize macroscopic, mesoscopic, and microscopic models to address different priorities on different corridors. Depending on the scope, complexity, and questions to be answered within a specific corridor, there may be more or less emphasis on each type of network analysis. Currently, there is no single model available that provides visibility into the cascading impacts of various congestion management strategies, much less combinations of strategies, across the entire network, transportation modes, and facility types; thus, an integrated approach is recommend that focuses on interfacing between the three classes of simulation models. In the Test Corridor, one of the primary goals was to measure the impacts of ICM on the performance of alternate routes and alternate transportation modes in the vicinity of high frequency incident sites during medium and high travel demand scenarios. All three classes of simulation modeling were proposed. In addition, researchers discussed applications for parametric simulation, time-of-departure choice, and pivot-point mode shift models.
  • Consider the impacts of data quality on model calibration. Although the AMS framework is flexible and allows for the application of different types of models at the Pioneer Sites, limitations in all three locally available software programs (macroscopic, mesoscopic, and microscopic) may present challenges. Use available data. Depending on the availability of data, accuracy of model calibration can be impacted.
The utility of the AMS methodology was validated on the test corridor. All affected modes, facility types, and jurisdictions were accounted for, and the potential impacts of individual and combined ICM strategies were measured under a variety of operational conditions. Overall, HOT lanes and highway traveler information systems were found to be the most effective ICM investments. In addition, researchers noted that the effectiveness of ICM strategies varies under different prevailing conditions and that the benefits derived from a combination of some ICM strategies may be less than the benefits resulting from some individual strategies. For example, freeway ramp metering can produce positive benefits under high travel demand, but negative benefits under medium travel demand.

The AMS framework used in this study was designed to help decision-makers identify gaps, determine constraints, and invest in the best combination of strategies to improve performance. In addition, AMS provides a long-term capability enabling corridor managers to continually improve implementation strategies based on experience.


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Source

Integrated Corridor Management: Analysis, Modeling, and Simulation Results for the Test Corridor

Author: Alexiadis, V.

Published By: U.S. Department of Transportation

Prepared by Cambridge Systematics for the U.S. DOT

Source Date: September 2008

URL: http://ntl.bts.gov/lib/32000/32300/32303/14440_files/14440.pdf

Lesson Contacts

Lesson Analyst:

Mike Mercer
Noblis
202-863-3646
michael.mercer@noblis.org


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States

None defined

Countries

United States

Goal Areas

Productivity

Keywords

DMS, CMS, VMS, Changeable Message Signs, Variable Message Signs, ramp meters, high occupancy vehicles, carpool lanes, high occupancy vehicle lane, managed lanes, HOV, congestion pricing, value pricing, variable road pricing, traffic signals, adaptive signals

Lesson ID: 2009-00504