Data Analysis Of Public Transit Passenger Flow And Static Scheduling Of Vehicle Based On Information Of Public Transportation IC Cards

Urban Public Transportation keeps developing constantly, along with the economic soared. In the meantime, urban buses system is a critical part of Urban Public Transportation, which has a profound function of our daily live. Since the distribution of bus passengers has an impact on both the organization of cars’arrangements and long perspective planning, the bus companies and related government departments will put more emphasis on the questions that refer to passengers flow or daily scheduling. Only when we have a deep knowledge of the laws of passengers flow, would we find out an appropriate transit operation and schedule in a scientific way. Today, Public transit system is popularizing certain advanced techniques to collect the data of passengers flow; those techniques could offer some help in collecting the data of passengers flow which expand drastically. Therefore, a new method of data analysis is urgent, handling massive data and ameliorating the flaws brought by traditional methods. Based on the outcome of data analysis, we can arrange static scheduling properlyIn my paper, first, I did some research in how to use clustering analysis method to deal with the data of passengers flow. By analyzing the times when passengers used their bus cars, my article precede with predictive parsing of stations and time when passengers who uses card get in and get out, and analyze the data based on reality. Besides, it laid the foundation of a following model of static scheduling referring to the frequency of sending off a bus. Second, my article built the model of static scheduling which determines the frequency of sending off a bus on the basis of the analysis of data of passengers flow. Fixing the frequency of sending off a bus by the laws of passengers flow is an crucial question to the static scheduling. My article concerns the laws of passengers flow in time, and analyzes the scheduling problem from an angle of interest of passengers and companies. And1also manage to build the multiple target optimized modeling of the frequency of sending off a bus based on different periods, discussing the application of modeling with the real data.