Due to the limitations of mobile phone positioning technology in the past, it is difficult for an advertising system to obtain users’ locations. Although there are many advertising delivery ideas in the market, time and location-based advertising delivery system have not been able to meet the delivery needs of advertisers. This study investigates various time and location-based advertising needs in the market and finds a universal data processing model that can quickly organize user positioning data from telecom base stations into screening information. It helps us to find specific audiences and to deliver advertisements to target audiences quickly. This study evaluated various advertising scenarios’ effectiveness and explained why there is a performance gap usually between five to ten times worse compared to the past. This study discusses the problems encountered when implementing the advertising system and how to improve them. This study also examines advertising interaction methods based on 5G features, business cooperation and profit splitting with telecom operators, and more diversified data application orientations and possibilities such as market surveys, urban traffic, security issues, and epidemic prevention. The future challenge is to develop footprint tracking technologies based on 5G and provide smart cities with lives-protecting or cost-saving solutions.

This study mainly focuses on the estimation calculation of urban parking space. Urban parking has always been a problem that plagues governments worldwide. Due to limited parking space, if the parking space is not controlled correctly, with the city’s development, the city will eventually face the result that there is nowhere to park. In order to effectively manage the urban parking problem, using the dynamic parking fee pricing mechanism combined with the concept of shared parking is an excellent way to alleviate the parking problem, but how to quickly estimate the total number of available parking spaces in the area is a big problem. This study provides a fast parking space estimation method and verifies the feasibility of this estimation method through actual data from various types of fields. This study also comprehensively discusses the changing characteristics of parking space data in multiple areas and possible data anomalies and studies and explains the causes of data anomalies. The study also concludes with a description of potential applications of the predictive model in conjunction with subsequent dynamic parking pricing mechanisms and self-driving systems.