Ph.D. Dissertation Proposal
Improving traffic flow forecasts for road networks
with data assimilation
Shiming Yang
3:00pm Wednesday, 8 June 2011, ITE 325b
Macroscopic models for traffic flow in networks of roads are widely used in analyzing traffic phenomena and for the management and planning of transportation road systems. These models have various simplifying assumptions in order to be tractable. Moreover, we often have only partial and inaccurate knowledge of the model parameters. Consequently, there are modeling errors to be dealt with.
An approach to mitigate our partial knowledge and modeling uncertainties, is to collect measurements of the real traffic system and use computational methods to assimilate them with the model in order to derive more accurate forecasts of the state of the system.
In this proposal, we propose to design, develop, and analyze methods for assimilating measurements from road networks to improve the accuracy of short-term forecasting of traffic flow in road networks. The proposed methods will overcome challenges due to the non-linearity of traffic flow behavior, high dimensionality of the modeled state space, and anisotropic non-Gaussian modeling and measurement error processes.
Committee:
- Dr. Kostas Kalpakis (chair)
- Dr. Milton Halem
- Dr. Yaacov Yesha
- Dr. James Smith