E-Learning of Router Applications to Drivers in Order to Reduce Collisions and Road Accidents with Wild Animals

Document Type : Commentary

Authors

1 Department of Educational Management, Gorgan Branch, Islamic Azad University, Gorgan, Iran

2 Department of English, Bojnourd University, Bojnourd, Iran

3 Department of Agricultural Sciences (Watershed Management), University of Agricultural Sciences and Natural Resources, Gorgan, Iran

4 Department of Crisis Management, Tehran Branch of Science and Research, Islamic Azad University, Tehran, Iran

Abstract

Road collisions are the most important cause of wildlife mortality, and almost all countries are involved in some way. Researchers believe that changing the behavior of drivers through e-learning can help reduce road collisions by informing and raising awareness about the negative consequences of animal deaths on the road; installing warning signs, markings and speed bumps, increasing road lighting, and reducing traffic speeds physically or mentally, by creating speed bumps or increasing obstacles and road curves in high-risk areas can also help. Placing wildlife trails or approaching wildlife crossings in navigation programs like Balad and Neshan that are more popular with drivers can help guide traffic and reduce wildlife crashes. People should be taught how to use the software above to find areas where animals are likely to be on the roads. This will help people avoid accidents with animals on the roads and save them both money and time.

Keywords

References

Niazmand MR, Tavousi M. Interaction between Electronic Learning and Information Ecology. Interdisciplinary Journal of Virtual Learning in Medical Sciences. 2019; 10(3): 61-62.‏ doi: 10.30476/IJVLMS.2019.45844.
 Nasiri A, Ganji N. Determining the optimal route of the road network using GIS (Case study: RoodSar-Qazvin route). Human Geography Research. 2021; 53(1):65-84. doi:10/22059/jhgr.2019/278135/1007888.
Mousavitbar, S H, Shojaeifard M H, & Mollajafari M. An overview of routing methods based on computational intelligence. Iranian Journal of Mechanical Engineers, 30(1), 49-63. doi: 10.30506/mmep.2021.138050.1861.
Mousavi SM, Kazemi E, Poursina M. Road casualties of carnivores with emphasis on road casualties of cheetahs and leopards in Iran, Quarterly Journal of Man and Environment. 2018(45); 145-151.
Hedlund JH., Curtis PD, Curtis G, Williams A F. Methods to reduce traffic crashes involving deer: what works and what does not. Traffic injury prevention. 2004; 5(2): 122-131.‏ doi:10.1080/15389580490435079.
McCollister MF, Van Manen FT. Effectiveness of wildlife underpasses and fencing to reduce wildlife‐vehicle collisions. The Journal of Wildlife Management. 2010; 74(8): 1722-1731.‏ doi:10.2193/2009-535.
Sharma S U, Shah D J. A practical animal detection and collision avoidance system using computer vision technique. 2016; 347-358.‏ doi:10.1109/ACCESS.2016.2642981.
 

Files

Share

How to cite

Statistics