Proactive traffic management and control relies on sound traffic flow models that are central to traffic prediction and analysis. One of the challenges faced by such models is the calibration of these models to prevailing local conditions. As such, an efficient methodology is called for to fine tune model parameters so that they reflect local traffic characteristics. Many calibration procedures have been developed in the past with varying transferability, complexity, and accuracy. For example, some procedures are customized for certain models and are not easy to be adapted to other models; some procedures may involve optimization of multiple levels, so calibrating a model becomes a time-consuming job; some procedures optimize only one side of the model at the cost of the other inherently related side. The objective of this research is to develop a methodology for calibrating equilibrium traffic flow models that is accurate in nature, independent on models, efficient in computation, robust to calibration data. The result of this research can help traffic analysts and transportation agencies to better understand traffic flow characteristics, predict traffic evolution, mitigate traffic congestion, and deploy resources to anticipate incidents.
Recent and continuing growth has resulted in increased demand for travel on urban freeways throughout Texas. Many freeways are operating under congest...
The objective of this project is to develop and calibrate a computational process model of spatial learning, for the micro-simulation of travel dynami...
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United States Department of Transportation
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