.png){kind=icon}
Return
Deep learning method for traffic accident prediction security
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-022-07096-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Since frequent traffic accidents bring great losses to people's safety and social property, this paper takes the results of traffic accident risk prediction as the basis so that it can help city managers to reasonably deploy police force, relieve traffic pressure, avoid traffic accidents and provide safe guidance to pedestrians. Based on this paper, a deep learning framework including spatiotemporal attention mechanism is proposed to solve the problem of traffic accident prediction in urban areas, and the experimental simulation shows that the accuracy of traffic accident risk prediction proposed in this paper reaches 94%.
Keywords:
Deep learning
Traffic flow
Risk prediction
Algorithm
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W
Organization
No organization information available
Cited Papers
No cited papers available