Public transportation operational health assessment based on multi-source data

Abstract

In order to solve the problem of inefficient long-term operation of urban public transport vehicles and the difficulty of finding the cause of the disease, a new analysis idea was designed using machine learning methods. This study aimed to provide a rapid, accurate, and convenient solution model and algorithm to address the drawbacks of traditional analysis tools that are incapable of handling multiple sources of public transport data. Based on a full process analysis of the bus operation status, the influencing factors and calculation methods were defined. Afterwards, the calculation results were used to construct a training set with a Random Forest regression model to obtain the weight ranking of different influencing factors. The results of the simulation validation proved that the model can use the basic data of bus operation to quickly find out the primary factors affecting the operation condition and pinpoint to the bottleneck interval. The method has high accuracy and feasibility. It can be universally applied to the analysis of regular bus scenarios to provide strong decision support for the operation level.