Optimization of Subway Advertising Based on Neural Networks

Abstract

Subway advertising has become a regular part of our daily lives. Because the target audiences are high-level consumers, subway advertising can promote the return on investment. Such advertising has taken root in various countries and regions. However, a lack of appropriate oversight, a single-track operating mode of subway advertising, and unclear price standards significantly reduced the expected advertising effects and the reasonableness of advertising quotations. The shared biking services have gained a great amount of attention in the past few years. Besides, more citizens get involved in using public transportation, which provides a basis for analyzing subway passenger characteristics. First, we examined the use of shared bikes around subway stations to obtain the information on passengers’ age. Then, using daily passenger flow volume, transfer lines, and the original subway advertising quotes, we trained backpropagation neural networks and used the results to provide new quotations. Finally, we combined passenger age structure and different passenger groups’ preferences in every station to identify the most suitable advertisement type. Our goal was to make full use of transportation big data to optimize advertising quotations and advertisement selection for subway stations. We also proposed the using of electronic advertising board to help increase the subway advertising profits, decrease the financial pressure of operations, and boost the public transportation development.