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.
EXISTING SYSTEM :
Urban traffic is an important support system for the city, which directly influences the development, efficiency and quality of life of the city. Traffic structure is the traffic proportion of different modes of transportation in the comprehensive transportation system, which reflects the characteristics of traffic demand and the main functions and status of different modes of transportation .
The traffic structure reflects the ratio of different modes of transportation under the conditions of different regions, different time, different research contents, which is an important index to reflect the traffic development pattern.
It fully embodies the strategic focus of urban traffic and it has important guidance to the planning, construction, operation and management of urban transportation . For the developed cities, traffic congestion brings the problems of the difficulties of traffic management, the waste of time and space, fuel consumption and air pollution .
PROPOSED SYSTEM :
In order to solve the problems of urban traffic, public transport should be developed in priority, while the priority policies of public transport play a driving role in the development of public transport in big cities. Therefore, the analysis of the effect of the bus priority policy on the sharing rate of different modes of urban transportation is very important to help the managers to realize the maximization of the efficiency of public transport and it will have great help to solve the city traffic problems.
Big data has penetrated and applied in all fields as a new trend in the development of information technology. Big data becomes an important driving factor and hot bonanza, and brings the heavy waves of change in various industries . This paper tried to explore the impacts of public transportation fare reduction policy on urban public transport sharing rate based on big data and computer data analysis.
This paper introduced the changes of public transport mode choice in Beijing in recent years, and we select the year of 2007 when two typical public transit fare adjustment policies were put into effect as a time node. We took the number of the bus vehicles, the total length of the city roads, the number of the urban rail transit trains as the independent variables and the quantity of the passengers of the public transport as the dependent variable. Then the multivariate regression model was established, and the analysis that how the quantity of the trips and sharing rate of the public transit change whether the influences of the public transport fare adjustment policies are put into effect or not. Generally, reduction of public transport fares can really make its sharing rate rise in a short period of time, but as time goes by, its simulative impact will diminish gradually. In the long run, the reduction of public transport fares does not necessarily enhance its sharing rate.