Anhui Expressway Information Management

Construction Goals

By applying big data analytics to traffic flow, passenger flow, weather, and consumer behavior data, the system generates an integrated view of vehicle traffic and sales performance across all service areas in Anhui Province. This empowers operators with actionable statistical insights for daily management as well as investment, operations, and asset distribution planning.

By leveraging big data to collect and analyze sales information. Develop sales analysis models to explore the relationships between service area performance and factors such as traffic flow and weather. This enhances the accuracy of inventory allocation plans, preventing overstock and stockouts, thereby boosting operational effectiveness.

By integrating traffic, weather, and service area resources including parking, lodging, dining, and convenience stores. Support the development of online-offline platforms offering services such as online dining, supermarkets,and nearby information queries, providing data-driven support to elevate public service levels in service areas.

Solution

Data Mining Algorithms Selection

The core objective is to analyze the root causes of operational performance across service areas. Since fluctuations in service area operations are often influenced by multiple factors, using an optimal combination of multiple independent variables to predict or estimate the dependent variable is more effective and realistic than relying on a single variable. In economics, when a variable is affected by multiple factors, a multivariate linear regression model is commonly used for analysis.

结果输出

服务区名称、便利店日均消费预测（元）、餐饮店日均消费预测（元）、小吃店日均消费预测（元）、水果店日均消费预测（元）

User Benefits

Customer Benefits

Through the application of advanced data mining models, the solution can accurately forecast the revenues of convenience stores, restaurants, snack bars, and fruit stores in each service area.

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