用高斯混合模型分析公交油耗，识别四类效率状态。
Fuel Efficiency Analysis of the Public Transportation System Based on the Gaussian Mixture Model Clustering
- 用高斯混合模型对公交油耗数据聚类，自动划分效率等级。
- 基于三个指标融合选最优聚类数，4006条丹麦公交行程验证有效。
- 发现驾驶习惯与路线特征显著影响油耗，适合交通管理者参考。
公共交通是温室气体排放的主要来源之一，提升公交车燃油效率至关重要。聚类算法有助于分析油耗数据，但无关特征会干扰分析，且最优聚类数量选择仍具挑战。本文采用高斯混合模型对单一燃油效率数据集进行聚类，并提出一种结合轮廓系数、Calinski-Harabasz指数和Davies-Bouldin指数的集成方法，以确定最优聚类数。以丹麦北日德兰地区4006条公交行程为案例研究，将行程先分为三组，再进一步细分，最终形成四类：极端高效、正常、低效和极低效。通过可视化初步分析驾驶行为与路线条件对燃油效率的影响，结果表明个体驾驶习惯与路线特征均显著影响燃油效率。
原文摘要 · Abstract (English)
Public transportation is a major source of greenhouse gas emissions, highlighting the need to improve bus fuel efficiency. Clustering algorithms assist in analyzing fuel efficiency by grouping data into clusters, but irrelevant features may complicate the analysis and choosing the optimal number of clusters remains a challenging task. Therefore, this paper employs the Gaussian mixture models to cluster the solo fuel-efficiency dataset. Moreover, an integration method that combines the Silhouette index, Calinski-Harabasz index, and Davies-Bouldin index is developed to select the optimal cluster numbers. A dataset with 4006 bus trips in North Jutland, Denmark is utilized as the case study. Trips are first split into three groups, then one group is divided further, resulting in four categories: extreme, normal, low, and extremely low fuel efficiency. A preliminary study using visualization analysis is conducted to investigate how driving behaviors and route conditions affect fuel efficiency. The results indicate that both individual driving habits and route characteristics have a significant influence on fuel efficiency.
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