Open Access

ARTICLE

Gender Forecast Based on the Information about People Who Violated Traffic Principle

by Rui Li, Guang Sun, Jingyi He, Ying Jiang, Rui Sun, Haixia Li, Peng Guo, Jianjun Zhang

1 Hunan University of Finance and Economics, Changsha, China
2 University Malaysia Sabah, Kota Kinabalu, Malaysia
3 Hunan Normal University, Changsha, China

* Corresponding Author: Guang Sun. Email:

Journal on Internet of Things 2020, 2(2), 65-73. https://doi.org/10.32604/jiot.2020.09868

Received 08 November 2019; Accepted 07 June 2020; Issue published 14 September 2020

Abstract

User portrait has been a booming concept in big data industry in recent years which is a direct way to restore users’ information. When it talks about user portrait, it will be connected with precise marketing and operating. However, there are more ways which can reflect the good use of user portrait. Commercial use is the most acceptable use but it also can be used in different industries widely. The goal of this paper is forecasting gender by user portrait and making it useful in transportation safety. It can extract the information from people who violated traffic principle to know the features of them then forecast the gender of these people. Finally, it will analyze the prediction based on characteristics correlation and forecasting results from models which can verify if gender can have an obvious influence on the traffic violation. Also we hope give some advice to drivers and traffic department by doing this research.

---

Keywords

---

Citations