A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data

Authors

Franklin Abodo, Florida International UniversityFollow

Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Computer Science

First Advisor's Name

Leonardo Bobadilla

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Mark Finlayson

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Patricia McDermott-Wells

Third Advisor's Committee Title

Committee Member

Keywords

probabilistic programming, hierarchical modeling, Bayesian inference, data analysis, probabilistic graphical models, car-following, model calibration, parameter estimation, model validation, traffic simulation

Date of Defense

6-24-2022

Abstract

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.

Identifier

FIDC010774

ORCID

0000-0001-8553-3663

Creative Commons License

This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.

Recommended Citation

Abodo, Franklin, "A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data" (2022). FIU Electronic Theses and Dissertations. 5002.
https://digitalcommons.fiu.edu/etd/5002