Driving in congested traffic is a nuisance that not only results in longer travel times, but also triggers frustration and impatience among drivers. A few studies have modeled the effects of congested traffic in the resulting route choice behavior of car drivers. The studies used frequentist models such as discrete choice models to analyze large samples. However, these studies did not compare the inferences obtained from the frequentist and Bayesian approaches, particularly for datasets which are not sufficiently large. It has been shown by researchers that Bayesian models perform well, especially when the sample size is small. Thus, this paper develops and compares a multinomial logit (frequentist) and a Naïve Bayes (Bayesian) model on a mid-sized dataset of size around 100 participants which was obtained from a driving simulator experiment to understand driver’s route choice under stop-and-go traffic. The results show that the prediction power of the Naïve Bayes model is much higher than the multinomial logit model (MNL). The Naïve Bayes model is also found to perform better than machine learning algorithms like the decision tree model. The findings from this study will be useful to researchers and practitioners as they should test both the approaches and select the appropriate model, particularly in the case of seemingly large datasets.

在交通拥挤的情况下驾驶是一种麻烦，不仅会导致行驶时间延长，还会引起驾驶员的沮丧和不耐烦。一些研究对交通拥堵对汽车驾驶员选择路线的行为的影响进行了建模。这些研究使用诸如离散选择模型之类的常客模型来分析大样本。但是，这些研究没有比较从常客和贝叶斯方法获得的推论，特别是对于数据集不够大的情况。研究人员已经证明贝叶斯模型表现良好，尤其是在样本量较小的情况下。从而，本文在一个约100名参与者的中型数据集上开发并比较了多项式logit（常客）模型和朴素贝叶斯（贝叶斯）模型（该模型是通过驾驶模拟器实验获得的，以了解在走走停停的交通情况下驾驶员的路线选择） 。结果表明，朴素贝叶斯模型的预测能力远高于多项式对数模型（MNL）。还发现朴素贝叶斯模型的性能优于决策树模型等机器学习算法。这项研究的发现对研究人员和从业人员很有用，因为他们应该测试这两种方法并选择合适的模型，特别是在数据集看似庞大的情况下。结果表明，朴素贝叶斯模型的预测能力远高于多项式对数模型（MNL）。还发现朴素贝叶斯模型的性能优于决策树模型等机器学习算法。这项研究的发现对研究人员和从业人员很有用，因为他们应该测试这两种方法并选择合适的模型，特别是在数据集看似庞大的情况下。结果表明，朴素贝叶斯模型的预测能力远高于多项式对数模型（MNL）。还发现朴素贝叶斯模型的性能优于决策树模型等机器学习算法。这项研究的发现对研究人员和从业人员很有用，因为他们应该测试这两种方法并选择合适的模型，特别是在数据集看似庞大的情况下。