Safest Route Detection via Danger Index Calculation and K-Means Clustering
- Authors
- Puthige, Isha; Bansal, Kartikay; Bindra, Chahat; Kapur, Mahekk; Singh, Dilbag; Mishra, Vipul Kumar; Aggarwal, Apeksha; Lee, Jinhee; Kang, Byeong-Gwon; Nam, Yunyoung; Mostafa, Reham R.
- Issue Date
- 2021
- Publisher
- Tech Science Press
- Keywords
- Agglomerative; clustering; crime rate; danger index; DBSCAN
- Citation
- Computers, Materials and Continua, v.69, no.2, pp 2761 - 2777
- Pages
- 17
- Journal Title
- Computers, Materials and Continua
- Volume
- 69
- Number
- 2
- Start Page
- 2761
- End Page
- 2777
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19079
- DOI
- 10.32604/cmc.2021.018128
- ISSN
- 1546-2218
1546-2226
- 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.
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- Appears in
Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
- College of Engineering > Department of Information and Communication Engineering > 1. Journal Articles
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