Ideal Ambulance Alignment for Road Collisions Using ML

Ideal Ambulance Alignment for Road Collisions Using ML

1Dr.C.Srinivasa kumar
Professor&Dean, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.

Email:drcskumar46@gmail.com

2T.Greeshma
UG Student, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.

Email:greeshmathuppathuri@gmail.com

3 M.laya Sahithi
UG Student, Department of Computer Science and Engineering5 Vignan’s Institute of Management and Technology for Women, Hyd.

Email: saithichowdary@gmail.com

4D.Brundha
UG Student, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women Email:brundhadandaboyina@gmail.com

5Ch.Sowmya
UG Student, Department of Computer Science and Engineering Vignan’s Institute of Management and Technology for Women, Hyd.

Email: sowmya@gmail.com

Abstract-- This study addresses road accident losses by proposing a deep- bedded clustering- grounded approach for ambulancepre-positioning. The approach aims to reduce response times and give prompt medical attention. Factors and patterns impacting road crashes in a geographical region are pivotal considerations during model structure. The study emphasizes conserving these patterns using Cat2Vec, a deep- literacy- grounded model. A comparison with traditional clustering algorithms like K- means, GMM, and Agglomerative clustering is conducted, pressing the proposed frame's superior performance. The ambulance- positing system achieves an emotional 95 delicacy with k-fold cross validation and a new distance score of 7.581. Overall, the study underscores the effectiveness of the proposed approach in enhancing ambulance positioning for bettered healthcare services and road safety. The number of casualties and losses brought on by road accidents is one of the most significant enterprises in the ultramodern world.

Keywords- Accident detection, Collision Prediction, Emergency response, Geospatial analysis, Maching learning algorithms, Optimal routing, Road safety, Traffic prediction.