Calibration of dynamic traffic assignment models with point-to-point traffic surveillance

Accurate calibration of demand and supply simulators within a Dynamic Traffic Assignment (DTA) system is critical for the provision of consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as Automatic Vehicle Identification (AVI) technology provide a rich source of disaggregate traffic data. This thesis presents a methodology for calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic sensing technologies. The calibration problem has been formulated in two different frameworks, viz. in a state-space framework and in a stochastic optimization framework. Three different algorithms are used for solving the calibration problem, a gradient approximation based path search method (SPSA), a random search meta-heuristic (GA) and a Monte-Carlo simulation based technique (Particle Filter). The methodology is first tested using a small synthetic study network to illustrate its effectiveness. Later the methodology is applied to a real traffic network in the Lower Westchester County region in New York to demonstrate its scalability.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Vaze, Vikrant (Vikrant Suhas)

Advisor dc:contributor.advisor

• Moshe E. Ben-Akiva.

• Civil and Environmental Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/39282

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/39282