Study On Passenger Flow OD Calculation And Visualization Method Based On Big Bus Data

OD data of bus passenger flow,as the basic data of urban public transport characteristics research,plays an important role in bus passenger flow analysis,prediction,planning and optimization of bus network.In recent years,with the gradual improvement of the Smart City System in China,the core business of the urban public transport system has been continuously expanded,and the research on the big data mining method and visualization technology of public transport supported by big data technology has gradually become a research hotspot in the field of intelligent transportation.As a product of urban public transport system,big bus data is the core component of urban spatial big data,which provides data support for the research on OD feature mining method of bus passenger flow.Big bus data inherits the characteristics of big data,such as “large amount”,“high speed”,“diversity”,“low value density” and “authenticity”.In big bus data,there are problems such as big data noise and data redundancy.At the same time,as urban public transport adopts the on-board one-ticket billing system,OD data of bus passenger flow cannot be directly extracted from big bus data generated by various data collection mechanisms.This paper studies the storage and preprocessing methods of big bus data,proposes the OD data mining algorithm of bus passenger flow based on big bus data,obtains the spatial and temporal distribution data of bus passenger flow,and constructs the visualization system of urban spatial big data based on bus passenger flow data.The specific research content is as follows:(1)This paper used shijiazhuang AVL data,bus IC card data and GPS data of bus stations,etc.as the main research objects,studied the distributed big bus data storage method,through the analysis of big bus data structures,extracted the key fields in the bus data and the mapping relations between space and time in multi-source bus data,realized the process of bus data cleaning,integration,transformation and reduction pretreatment,etc.(2)By using time matching method and setting up time elasticity factor,the in-tegration of IC card data and AVL data were realized,and passenger boarding point was calculated.Based on the characteristics of passengers bus travel behavior,the collection of passengers downstream and high frequency stations was extracted from the passengers boarding site data.Based on Poisson distribution theory,this paper studied the different performances of passenger travel station that is parameter lambda,under the condition of different travel line and travel time.The results showed that parameter lambda was affected by the combination of travel line and travel time.Therefore,a method combining the attraction intensity of multi-period bus stations with the method of bus trip chain was proposed,which realized the calculation of the getting-off station,and a model checking method at the aggregate level was proposed.The example of Shijiazhuang bus data was used to verify the method.(3)This paper studied the online crawling method of urban space vector data,designed the online crawling method of urban road network and building outline based on the 3DGIS platform,and realized the 3D visualization of urban scene through the rapid construction technology of 3D model.Based on the OD data of bus passenger flow,the temporal and spatial distribution characteristics of bus passenger flow were extracted.Taking time and location as key factors,the visualization method of bus station and line passenger flow and the visualization method of dynamic passenger flow distribution were studied.Based on the big data of Shijiazhuang public transportation,this paper verified the validity and applicability of the research results,meanwhile,obtained the spatial-temporal distribution characteristics data of shijiazhuang public transportation passenger flow.Based on the 3DGIS platform,it realized the visualization of 3D urban scene and bus passenger flow characteristics of Shijiazhuang.