Open AccessResearch on Dynamic Decision-making Hybrid Model of Pedestrian Flow Based on Deep Learning
Author(s) -
Zhao Shu,
Shiyao Men,
Baohua Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1437/1/012028
Subject(s) - computer science , pedestrian , time series , population , artificial intelligence , traffic flow (computer networking) , deep learning , machine learning , computer security , engineering , transport engineering , demography , sociology
With the progress and development of China’s transportation system, pedestrian travel behavior and safety have received increasing attention. At the same time, controlling the population density and population movement in densely populated areas plays an important role in ensuring national security for the public security departments. Therefore, how to properly adjust the switching time of traffic signals has become an urgent problem. In view of the above requirements, this paper proposes a signal switching model based on deep learning for dynamic regulation of pedestrian traffic. The hybrid model is divided into three parts, named the real-time data acquisition part, the historical data analysis and prediction part and the decision model part. Firstly, the LSTM model is used for the analysis and prediction of historical traffic data with time series characteristics. Then, the real-time data acquisition adopts the lightweight and high-performance target detection model MobileNet-SSD proposed by Google. Finally, the signal switching decision model is proposed to analyze and determine the data provided by the above model, and two adjustment factors are defined to adjust the proportion of historical data and real-time data to the impact of decision results. CCS Concepts •Computing methods ➝Artifical intelligence ➝Computer vision ➝computer vision tasks.