Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadways. These simulators are driven by models of microscopic driver behavior from which macroscopic measures like flow and congestion can be derived. Many models are designed for a subset of possible traffic scenarios and roadway configurations, while others have no explicit constraints on their application. Work zones (WZs) are one scenario for which no model to date has reproduced realistic driving behavior. This makes it difficult to optimize for safety and other metrics when designing a WZ. The Federal Highway Administration commissioned the USDOT Volpe Center to develop a car-following (CF) model for use in microscopic simulators that can capture and reproduce driver behavior accurately within and outside of WZs. Volpe also performed a naturalistic driving study to collect telematics data from vehicles driven on roads with WZs for use in model calibration. 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 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 CF hierarchically. Finally, I apply what was learned in the first three phases using an established CF model, Wiedemann 99, to the probabilistic modeling of the Volpe model. Validation is performed using information criteria as an estimate of predictive accuracy.

翻译：交通仿真软件被交通研究人员和工程师用于设计和评估道路改造方案。这些模拟器由微观驾驶员行为模型驱动，可从中推导出流量、拥堵等宏观指标。许多模型仅针对特定交通场景和道路配置子集设计，而另一些模型则未对其应用场景设置明确约束。施工区（WZ）是尚无任何模型能准确再现真实驾驶行为的场景之一，这使得在施工区设计过程中难以优化安全性及其他指标。美国联邦公路管理局委托美国交通部沃尔普中心开发适用于微观模拟器的跟驰（CF）模型，该模型需能精确捕捉并再现施工区内外驾驶行为。沃尔普中心还开展了自然驾驶研究，收集施工区路段车辆遥测数据用于模型校准。在模型开发过程中，沃尔普研究人员发现校准过程存在困难，进而质疑模型本身、数据质量或数据驱动校准方法是否存在缺陷。本文采用贝叶斯方法进行数据分析和参数估计，以探究并尽可能解决上述问题。首先，通过贝叶斯推断评估数据集规模的充分性；其次，对比遗传算法校准（沃尔普团队采用）与贝叶斯校准的流程与结果；再次，探索分层建模在跟驰模型中的优势；最后，将前三阶段研究成果应用于经典跟驰模型Wiedemann 99，建立沃尔普模型的概率化建模框架。验证环节采用信息准则作为预测精度的评估指标。