A study of short-term passenger flow forecasting for public transportation based on the time series method

Abstract. In order to solve the conflicts between the supply and demand of urban transport, improve the living standard of urban residents, raise the level of urbanization, and promote the sustainable socio-economic development of the whole city, it is necessary to increase the pace of urban transport construction, planning and management in China. And at the same time, its necessary to take various measures to regulate the structure of urban transport and guide urban transport to develop in the direction of taking public transport as the mainstay. The experience of the development of urban transportation in China and abroad also proves that prioritizing the development of public transportation is the fundamental way to solve the problem of urban transportation. Public transport passenger flow data is the basis for optimizing public transport operations and scheduling. Accurate passenger flow forecasts on public transport routes can effectively guide public transport operation decisions, formulate operation scheduling plans and effectively improve the operational efficiency of the public transport system. The following are some of the key factors that can be taken into consideration when making passenger flow forecasts. The main contribution of this article is to propose the use of data mining technology, use bus IC card data and bus GPS data to integrate and organize them, conduct correlation analysis on the data, and on this basis, obtain the main distribution characteristics of bus passenger flow, and provide short-term Passenger flow forecast provides relevant ideas.