Customization method and apparatus of advertising lines based on smart bus card data

Abstract

The invention provides a customization method and apparatus of advertising lines based on smart bus card data. The method includes: collecting the smart bus card data SCD, and obtaining bus station data and bus line data; performing mining processing on the smart bus card data, the bus station data and the bus line data, and extracting characteristic attributes of the bus lines; matching the characteristic attributes of the bus lines according to advertising requirements and intentions, and selecting one or more bus lines with the matched characteristic attribute to generate a bus body advertising line scheme; and displaying the bus lines in the advertising line scheme and characteristic attribute information thereof in a visualized manner. According to the method, the bus line advertisingscheme can be made in a targeted manner, and the advertising effect is guaranteed.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and big data, in particular to a method and device for customizing advertising delivery routes based on smart bus card data. Background technique [0002] With the development of cities and public transport systems, public transport has become one of the most important means of transportation for urban residents, and it is also a very penetrating outdoor advertising medium. Although the bus carries a large number of user groups every day, the target advertising revenue has not been very effective. The main reason is that it still relies on the traditional simple and rough pricing method, which is generally based on the urban area covered by a bus line or the cost of a bus. The number of passengers on board is used to evaluate the effectiveness of advertising on bus routes and set prices. In fact, this cannot scientifically assess the actual ad delivery performance-to-revenue rati...

AI Technical Summary

Problems solved by technology

[0003] Due to the variety and complexity of urban bus lines, the passenger flow at bus stations varies greatly at different times. In addition, each bus line will pass through different functional areas and different types of passengers, resulting in different advertising effects in different fields. Therefore, relying solely on bus lines Length and passenger flow are difficult to scientifically and accurately evaluate the effect of advertising routes, and it is urgent to solve this problem

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