Impacts of public transportation fare reduction policy on urban public transport sharing rate based on big data analysis

Urban transport is an important support system to the city. With the city's development, traffic congestion has become a major traffic problem nowadays and it is badly in need of solutions. Big data analysis has been widely used in the domain of transportation in recent years and it does great help to find solutions to different kinds of problems from historical data. In order to solve the urban traffic problems fundamentally, developing the public traffic is one of the major effective ways and bus priority policies and strategies are such important measures that they have contributions to the increase of public transport sharing rate. This paper mainly studied the influences of two bus fare adjustment policies in Beijing on urban public transport sharing rate based on big data analysis through the computer software SPSS, and then put forward corresponding recommendations to the reform of bus fares in Beijing.