Open Access
ARTICLE
Gender Forecast Based on the Information about People Who Violated Traffic Principle
Rui Li1, Guang Sun1,*, Jingyi He1, Ying Jiang1, Rui Sun1, Haixia Li1, Peng Guo1,2, Jianjun Zhang3
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
Cite This Article
R. Li, G. Sun, J. He, Y. Jiang, R. Sun
et al., "Gender forecast based on the information about people who violated traffic principle,"
Journal on Internet of Things, vol. 2, no.2, pp. 65–73, 2020.
Citations