Research Of Urban Rail Transit Passenger Flow Analysis System Based On Data Warehouse

Data warehouse is a new branch of storage technology. It has become research focus in the field of data analysis. At present, it has been widely used in the areas of banks, telecommunications and e-commerce. After years of operating management, Urban Rail Transit operating company has accumulated a large number of data. Enterprises' managers need to solve the issue of how to make better use of valuable data, enhance the management level and find new profit growth.First of all, the thesis described research background and significance of the subject, analysised major problems of building urban rail passenger flow data warehouse. Secondly the thesis used Non-Aggregation Logit model to predict the ticketing revenue after contrast, in the next place given Logit model mathematical expression of forecast revenue under the competitive conditions. Subsequently, for calculating statistical samples of Logit model needed to consume a large amount of system I/O resources , the thesis proposed adjust-field-order compression algorithm. Compared time of calculated Logit model before and after compression , experiments proved that the algorithm has not significant increase the system overhead during the compressing data, avoid the drawbacks of Logit model. Finally, as an example of Shenzhen Metro , the thesis built data warehouse analysis system of the urban rail transit passenger flow ,calculated KPI values of passenger flow of Shenzhen Metro, made a variety of online analytical processing (OLAP) for different scenarios, used the Logit model to identify the mathematical relationship between the urban rail transit passenger flow and the fare, predicted average fare of the largest ticketing revenue .The forecast result provided strong support of urban rail transit operators to develop better pricing program.Finally, the thesis summarized the main work and looked forward to the next step in the direction of the research.