Application Of Regression Model In Big Data Analysis Of Urban Public Bicycle Rental Business

A city-level public bicycle business system since 2008 on-line since the initial service point of 61 points, 2800 bicycles, the average daily rental of 2607 passengers, the maximum rental of more than 6,000 people; as of the end of June 2014 reached a total of 3111 service points , 78,000 bicycles, the current average daily rent of 28.8 million passengers, the highest rental volume of 41.14 million passengers. As the business development, operational and management of the shortage of resources gradually exposed, the public bicycle business lack of guidance management methods, the lack of early business development in advance of the judge. In the event of rental car peak, the frequent occurrence of resource coordination unreasonable,temporary deployment of difficult problems.In the era of big data, how to carry out real-time summary of the operational data associated with the whole public bicycle,multidimensional analysis, timely prediction and reminder; how to analyze the rental car data statistics, etc. , is the core issue to be solved recently, Information development must be the stage. Therefore,the public bicycle industry is also an urgent need to introduce in the mature information industry has been widely used statistical analysis methods and technology integrated public bicycle business system data,to ensure data integrity and accuracy based on the public bicycle data theme analysis model, Operational decision-making to provide real-time,scientific, reasonable and predictive analysis based on.This paper will use the linear regression analysis method, combined with the user's operational data, to build a public bicycle rental business volume regression model to help users accurately grasp the rent of the variable by the other one or more variables affected, and then provide for the forecast Scientific basis.