Driving Risk Detection Model of Deceleration Zone in Expressway Based on Generalized Regression Neural Network
Drivers’ mistakes may cause some traffic accidents, and such accidents can be avoided if prompt advice could be given to drivers. So, how to detect driving risk is the key factor. Firstly, the selected parameters of vehicle movement are reaction time, acceleration, initial speed, final speed, and ve...
Saved in:
| Main Authors: | Weiwei Qi, Zhexuan Wang, Ruru Tang, Linhong Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/8014385 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Driving Risk Analysis and Evaluation in Rightward Zone of Expressway Reconstruction and Extension Engineering
by: Chi Zhang, et al.
Published: (2020-01-01) -
Length Requirements for Urban Expressway Work Zones’ Warning and Transition Areas Based on Driving Safety and Comfort
by: Aixiu Hu, et al.
Published: (2025-06-01) -
Examining driving stability and traffic capacity: A simulation study on appropriate speed limits in expressway work zones.
by: Qingwen Guo, et al.
Published: (2025-01-01) -
Driving Risk Affected Areas and Distribution Function of Sharp Horizontal Curves of Expressway
by: Xiao-fei Wang, et al.
Published: (2015-01-01) -
Acceleration and Deceleration Parameter Calibration of Tunnel Entrance Based on the Naturalistic Driving Test of Passenger Car
by: Shijian He, et al.
Published: (2021-01-01)