Fuel Efficiency Analysis of the Public Transportation System Based on the Gaussian Mixture Model Clustering
This work addresses fuel efficiency in public transportation for environmental sustainability, but it is incremental as it applies existing clustering methods with a new integration technique to a specific dataset.
This paper tackled the problem of analyzing bus fuel efficiency to reduce greenhouse gas emissions by using Gaussian mixture models to cluster a dataset of 4006 bus trips, resulting in four categories of fuel efficiency and showing that driving behaviors and route conditions significantly influence efficiency.
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.