Capacity Estimation in Support of Mesoscopic Simulation as Part of Dynamic Traffic Assignment Models

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.

[1]  Hein Botma,et al.  Assessment of Roadway Capacity Estimation Methods , 1996 .

[2]  Lily Elefteriadou,et al.  Probabilistic nature of breakdown at freeway merge junctions , 1995 .

[3]  Nagui M. Rouphail,et al.  Identification and Calibration of Site-Specific Stochastic Freeway Breakdown and Queue Discharge , 2010 .

[4]  黒田 孝次,et al.  Highway Capacity Manual改訂の動向--テイラ-教授の講演より , 1984 .

[5]  Alexander Skabardonis,et al.  Systematic Identification of Freeway Bottlenecks , 2004 .

[6]  Robert L. Bertini,et al.  Use of Performance Measurement System Data to Diagnose Freeway Bottleneck Locations Empirically in Orange County, California , 2005 .

[7]  Jaimyoung Kwon,et al.  Automatic Calibration of the Fundamental Diagram and Empirical Observations on Capacity , 2009 .

[8]  Amjad Dehman Breakdown Maturity Phenomenon at Wisconsin Freeway Bottlenecks , 2012 .

[9]  Hesham Rakha,et al.  Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data , 2010, Transp. Sci..

[10]  Robert L. Bertini,et al.  Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon , 2008 .

[11]  Albert Gan,et al.  Assessing the Benefits of Incident Management Systems , 2010 .

[12]  Kunal Kamlakar Kundé Calibration of mesoscopic traffic simulation models for dynamic traffic assignment , 2002 .

[13]  Bart van Arem,et al.  AN ON-LINE PROCEDURE FOR ESTIMATING CURRENT CAPACITY , 1992 .

[14]  Jiyoun Yeon,et al.  Differences in Freeway Capacity by Day of the Week, Time of Day, and Segment Type , 2009 .

[15]  H S Mahmassani Transportation and Traffic Theory: Flow, Dynamics and Human Interaction , 2005 .

[16]  Abishai Polus,et al.  Meaning of actual capacity of freeways and its estimation , 2010 .

[17]  Markos Papageorgiou,et al.  An adaptive freeway traffic state estimator , 2009, Autom..

[18]  Werner Brilon,et al.  Reliability of Freeway Traffic Flow: A Stochastic Concept of Capacity , 2005 .

[19]  Lily Elefteriadou,et al.  DEFINING, MEASURING AND ESTIMATING FREEWAY CAPACITY , 2003 .

[20]  Chao Chen,et al.  An Empirical Assessment of Traffic Operations , 2005 .

[21]  D. Levinson,et al.  Some Properties of Flows at Freeway Bottlenecks , 2004 .

[22]  Fred L. Hall,et al.  FREEWAY CAPACITY DROP AND THE DEFINITION OF CAPACITY , 1991 .

[23]  Scott S. Washburn,et al.  Investigation of freeway capacity : (a) effect of auxiliary lanes on freeway segment volume throughput and b) freeway segment capacity estimation for Florida freeways , 2010 .