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

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.

Bibliographic Details
Main Author: Vaze, Vikrant (Vikrant Suhas)
Other Authors: Moshe E. Ben-Akiva.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2007
Subjects:
Online Access:http://hdl.handle.net/1721.1/39282
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author Vaze, Vikrant (Vikrant Suhas)
author2 Moshe E. Ben-Akiva.
author_facet Moshe E. Ben-Akiva.
Vaze, Vikrant (Vikrant Suhas)
author_sort Vaze, Vikrant (Vikrant Suhas)
collection MIT
description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.
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spelling mit-1721.1/392822019-04-12T15:33:57Z Calibration of dynamic traffic assignment models with point-to-point traffic surveillance Vaze, Vikrant (Vikrant Suhas) Moshe E. Ben-Akiva. Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. Civil and Environmental Engineering. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007. Includes bibliographical references (p. 173-180). 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. (cont.) The estimation results are tested using a calibrated Microscopic Traffic Simulator (MITSIMLab). The results are compared to the base case of calibration using only the conventional point sensor data. The results indicate that the utilization of AVI data significantly improves the calibration accuracy. by Vikrant Vaze. S.M. 2007-10-22T17:30:19Z 2007-10-22T17:30:19Z 2007 2007 Thesis http://hdl.handle.net/1721.1/39282 171033874 eng 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. http://dspace.mit.edu/handle/1721.1/7582 180 p. application/pdf Massachusetts Institute of Technology
spellingShingle Civil and Environmental Engineering.
Vaze, Vikrant (Vikrant Suhas)
Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
title Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
title_full Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
title_fullStr Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
title_full_unstemmed Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
title_short Calibration of dynamic traffic assignment models with point-to-point traffic surveillance
title_sort calibration of dynamic traffic assignment models with point to point traffic surveillance
topic Civil and Environmental Engineering.
url http://hdl.handle.net/1721.1/39282
work_keys_str_mv AT vazevikrantvikrantsuhas calibrationofdynamictrafficassignmentmodelswithpointtopointtrafficsurveillance