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Safest Route Detection via Danger Index Calculation and K-Means Clustering.
- Source :
- Computers, Materials & Continua; 2021, Vol. 69 Issue 2, p2761-2777, 17p
- Publication Year :
- 2021
-
Abstract
- The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations. Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one. The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results. Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results. we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users. The successful implementation would hopefully aid us to curb the ever-increasing crime rates; as it aims to provide the user with a beforehand knowledge of the route they are about to take. A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer. Thus, addressing a social problem which needs to be eradicated from our modern era. [ABSTRACT FROM AUTHOR]
- Subjects :
- K-means clustering
CRIME statistics
HAZARDS
SOCIAL problems
Subjects
Details
- Language :
- English
- ISSN :
- 15462218
- Volume :
- 69
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Computers, Materials & Continua
- Publication Type :
- Academic Journal
- Accession number :
- 151640449
- Full Text :
- https://doi.org/10.32604/cmc.2021.018128