Bus Passenger Flow Law Of Time Study Based On Ic Card

With the rapidly developing of national economy, the accelerating paces on urbanization, motorization and the enlarging urban scale, motor vehicle quantities and road traffic flow increasing sharply, the problems caused by traffic are becoming more and more serious, especially in metropolis. Research experience shows that developing public transportation, establishing Advanced Public Transportation System (APTS), improving the level of urban informatization and realizing intelligent bus scheduling are available means to solve the traffic problems.Passenger flow information is basic data for public transportation planning, bus scheduling and driving tissue. Bus companies can take corresponding measure to meet the demand of passenger, improve transport capacity, reduce the cost of economic and social costs, if the accurate bus passenger flow data timely obtained. Nowadays, we mainly utilize the manual count method to obtain the transit passenger flow information in China, which is cost-consuming and even difficult to get expected results, for example, the obtained data can not reflect the long-term variable trend of urban transit trip. Presently, transit IC card has been widely utilized in many cities, which not only provides convenience to passengers, but also comes into being a new survey and statistic approach of passenger flow in order to obtain comparatively real passenger flow data which can validly reflect the bus passenger flow characteristics. Just for the reason, this thesis named with" Time Regularity Characteristics Research of Urban Public Traffic Based on the Data of Bus Intelligent Card", research the characteristics of urban public traffic by using intelligent card data.Take Kunming bus system as an example, this thesis analysis the producing background of bus IC card data, analysis method and application firstly. On this basis, research the bus passenger time regularity characteristics from the three aspects which include bus system passenger flow, bus line passenger flow and the different groups travel time distribution. Then statistical properties of bus passenger flow are applied to determine minimum sample size the sampling statistics required, analysis bus system demand stabilities and divide the peak interval. Finally, build ARIMA Model, ARIMA-GARCH Model and the improved ARIMA-GARCH Model. Contrast analysis of the three methods revealed that the improved ARIMA-GARCH Model not only eliminated the heteroscedasticities, the forecasting accuracy demonstrated also higher.