Real-Time Calibration of Large-Scale Traffic Simulators: Achieving Efficiency Through the Use of Analytical Mode

Stochastic traffic simulators are widely used in the transportation community to model real-world urban road networks in applications ranging from real-time congestion routing and control to traffic state prediction. Online calibration of these simulators plays a crucial role in achieving high accuracy in the replication and prediction of streaming traffic data (i.e., link flows, densities). In order to be relevant in a real-time context, the problem must also be solved within a strict computational budget. The primary goal of this thesis is to develop an algorithm that adequately solves the online calibration problem for high-dimensional cases and on large-scale networks. In the first half, a new online calibration algorithm is proposed that incorporates structural information from an analytical metamodel into a general-purpose extended Kalman filter framework.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Zhang, Kevin,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Carolina Osorio.

• Operations Research Center.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129009

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129009