The representation, visualization, and modeling of traffic data is at the heart of intelligent transportation systems. Different types of traffic data exist, and novel ways of their accurate representation and modeling, which are useful for further analyses, simulations, and optimizations, are sought. In this work, location-specific traffic flows are represented by finite mixtures of circular normal (von Mises) statistical distributions. The parameters of the distributions are learned from empirical data by two variants of the expectation-maximization (EM) algorithm and by a nature-inspired method, differential evolution (DE). A proposed statistical model and a fitting strategy are evaluated on real-world data sets describing traffic flows in New York City. The experimental results show that the EM algorithm is able to find model parameters that correspond to input data and that are better than their analytic estimates, while DE evolves even more accurate models. The models based on circular distributions can be represented by circular plots as a novel type of visually appealing and easily interpretable fingerprints of the underlying traffic flow patterns.

中文翻译：

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使用简单圆形正态分布的有限混合对车辆流进行统计和自然启发建模

交通数据的表示、可视化和建模是智能交通系统的核心。存在不同类型的交通数据，并且正在寻求对其准确表示和建模的新颖方法，这些方法对于进一步分析、模拟和优化非常有用。在这项工作中，特定位置的交通流量由圆形正态 (von Mises) 统计分布的有限混合表示。分布的参数是通过期望最大化 (EM) 算法的两种变体和受自然启发的方法差分进化 (DE) 从经验数据中学习的。在描述纽约市交通流量的真实世界数据集上评估了提议的统计模型和拟合策略。实验结果表明，EM 算法能够找到与输入数据相对应且优于其解析估计的模型参数，而 DE 演化出更准确的模型。基于圆形分布的模型可以用圆形图表示，作为潜在交通流模式的新型视觉吸引力和易于解释的指纹。