This fellowship deals with the development of flexible models for the representation of traffic dynamics in ways that allow the practical use of rich and diverse data sources and provide insight into the traffic flow problem. The fundamental traffic flow theory relationships are a classic way of modeling traffic dynamics. In this project, an alternative paradigm for traffic dynamics models, appropriate for traffic simulation models, will be developed, based on machine learning approaches such as clustering, classification and local regression techniques. While these models may not directly provide as much insight into traffic flow theory, they allow for easy incorporation of additional explanatory variables, and hence, may be more appropriate for use in traffic estimation and prediction models, especially simulation based.
| ID | ap2 |
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