Master of Science (MS)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
probabilistic programming, hierarchical modeling, Bayesian inference, data analysis, probabilistic graphical models, car-following, model calibration, parameter estimation, model validation, traffic simulation
Date of Defense
Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadway networks. Underlying these simulators are mathematical models of microscopic driver behavior from which macroscopic measures of flow and congestion can be recovered. Many models are intended to apply to only a subset of possible traffic scenarios and roadway configurations, while others do not have any explicit constraint on their applicability. Work zones on highways are one scenario for which no model invented to date has been shown to accurately reproduce realistic driving behavior. This makes it difficult to optimize for safety and other metrics when designing a work zone.
The Federal Highway Administration (FHWA) has commissioned the Volpe National Transportation Systems Center (Volpe) to develop a new car-following model, the Work Zone Driver Model (WZDM), for use in microscopic simulators that captures and reproduces driver behavior equally well within and outside of work zones. Volpe also performed a naturalistic driving study (NDS) to collect telematics data from vehicles driven on highways and urban roads that included work zones for use in model calibration. The data variables are relevant to the car-following model’s prediction task.
During model development, Volpe researchers observed difficulties in calibrating their model, leaving them to question whether there existed flaws in their model, in the data, or in the procedure used to calibrate the model using the data. In this thesis, I use Bayesian methods for data analysis and parameter estimation to explore and, where possible, address these questions.
First, I use Bayesian inference to measure the sufficiency of the size of the NDS data set. Second, I compare the procedure and results of the genetic algorithm-based calibration performed by the Volpe researchers with those of Bayesian calibration. Third, I explore the benefits of modeling car-following hierarchically. Finally, I apply what was learned in the first three phases using an established car-following model to the probabilistic modeling of WZDM. Validation is performed using information criteria as an estimate of predictive accuracy. A third model used for comparison with WZDM in the simulator, Wiedemann ’99, is also modeled probabilistically.
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Abodo, Franklin, "A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data" (2022). FIU Electronic Theses and Dissertations. 5002.
Applied Statistics Commons, Categorical Data Analysis Commons, Data Science Commons, Longitudinal Data Analysis and Time Series Commons, Multivariate Analysis Commons, Other Statistics and Probability Commons, Statistical Methodology Commons, Statistical Models Commons
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