scholarly journals A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction

Author(s):  
Honglei Ren ◽  
You Song ◽  
Jingwen Wang ◽  
Yucheng Hu ◽  
Jinzhi Lei
2020 ◽  
Vol 2020 (13) ◽  
pp. 380-1-380-6
Author(s):  
Hiroto Suto ◽  
Xingguo Zhang ◽  
Xun Shen ◽  
Pongsathorn Raksincharoensak ◽  
Norimichi Tsumura

Improving drivers’ risk prediction ability can reduce the accident risk significantly. The existing accident awareness training systems show poor performance due to the lack of immersive sense. In this research, an immersive educational system is proposed for risk prediction training based on VR technology. The system provides a highly realistic driving experience to driver through 360 degrees video using VR goggle. In the nearly actual driving scene, users are expected to point out every potential dangerous scenario in different cases. Afterwards, the system evaluates users’ performances and gives the corresponding explanations to help users improve safety awareness. The results show that the system is more effective than previous systems on improving drivers’ risk prediction capability.


2021 ◽  
pp. 462-471
Author(s):  
C. Harinath Reddy ◽  
B. V. Koushik Kumar ◽  
N. Sai Teja Varma ◽  
S. Vidya ◽  
P. Nagaraj ◽  
...  

2018 ◽  
Vol 7 (2.21) ◽  
pp. 283
Author(s):  
A Manikandan ◽  
R Anandan

A short time period in development of rural places and public vehicle transportation system globally increased. The road accident are increased by the traffic problems last five years. It is a big problem of human society. These traffic accident are how can we happen and how to solve traffic management. Here we collect the traffic accident data and GPS record data using these data to build a deep learning model of stochastic gradient descent learning algorithm method used to solve critical problem of a traffic accident risk.    


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