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.

Proper vehicle operation and route planning are critical for achieving the best match between bus operation and passenger services. In order to enhance the attractiveness of public transportation, a new type of the public transportation dispatching method based on passenger reservation data is proposed. This mode can meet the requirements of multiple lines in urban centers during peak hours, which can realize direct service between two stations. Then, taking the lowest operating cost of the enterprise and the lowest passenger waiting cost as the optimization goal, a customized dynamic dispatching model of multiline and hybrid vehicles was established. Finally, a calculation example is designed and the genetic algorithm is used to solve the model. The results show that the hybrid vehicle solution is more reasonable than the traditional single-vehicle solution and reveal that the model is feasible to optimize scheduling plan. The conclusions obtained in this research lay a theoretical foundation for APP setup and operation plan formulation.

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There is an inherent coupling relationship between the time when buses arrive at the station and the time when they arrive the intersection, and it is essential to study the relationship as a whole to maximize the benefits of company operations and passenger services. In this study, a coordinated control method of signal priority and speed regulation in the stop-skipping mode at peak hours is proposed. First, the decision result of stop-skipping is obtained based on the historical passenger flow data. On this basis, the signal-priority decision is made for each vehicle in combination with the signal period and the arrival time of the intersection, and coordinated control is carried out in combination with the speed adjustment. The result of the genetic algorithm shows that cooperative control and prevention can minimize the passenger delay time and enterprise operation cost. The conclusions obtained in this research lay a theoretical foundation for company operation and signal-priority triggering mechanism.

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