Analysis And Design Of Public Transport Intelligent Forecasting Station System Based On Data Mining

The intelligent bus forecasting station system can combine advanced communication technology,global positioning technology and geographic information technology,and combine the analysis of the characteristics of public transport vehicles to predict the approximate arrival time of public transport vehicles,and achieve effective dispatch control of public transport vehicles in real time.In order to improve the operating efficiency of the bus system and reduce the cost of maintenance.However,the bus intelligent forecasting station system in most areas of China is still relatively backward.The accuracy of the arrival information of vehicles is low and the real-time performance is poor,which seriously affects the operating efficiency of the public transportation system,affects people’s daily life,and gives the line Great inconvenience.How to improve the accuracy of the forecast and guide passengers to travel is an urgent problem to be solved.In view of this,this paper conducts an in-depth study on the implementation and management process of the bus system in Suzhou High-tech Zone,understands the entire process of bus operation,and the business needs of the vehicle forecasting station,and designs a complete intelligent bus forecasting station system..The main work of this paper is as follows:(1)Establish a classification model based on K-center point algorithm and decision tree,find out the environmental factors that have great influence on vehicle operation according to the past driving data of the vehicle,and use SPSS Modeler for verification;then carry out the past data according to environmental factors.Classification processing to obtain average data under various environmental conditions.(2)Establish a prediction model based on BP neural network,and train the neural network according to the past driving data of the vehicle classified according to environmental factors.After the training is completed,historical data and real-time data are input into the neural network model to obtain the estimated arrival time of the vehicle.(3)Using B/S architecture and Spring MVC design pattern,combined with HTML5 and Redis related technologies,using JAVA language to develop the system,and applying the above two data mining schemes to the bus forecasting station system to achieve accurate public transportation Forecast station function.The bus intelligent forecasting station system designed and developed in this paper makes a relatively accurate prediction of the time of arrival of the vehicle,and updates and maintains the data related to the bus in real time,which effectively facilitates the passengers’ travel and has strong operation.Sex and reference.