SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration
This research addresses the critical issue of women’s safety in urban environments, emphasizing the need for innovative solutions to establish secure pathways. SafeRoutes presents a holistic approach, integrating advanced clustering methodologies and GPS technology, detailing its relevanc...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10740283/ |
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| author | Kushal Agrawal Aviral Srivastava Kanishk Sharma Sandeep Kumar Satapathy Sung-Bae Cho Shruti Mishra Abishi Chowdhury Amrit Pal |
| author_facet | Kushal Agrawal Aviral Srivastava Kanishk Sharma Sandeep Kumar Satapathy Sung-Bae Cho Shruti Mishra Abishi Chowdhury Amrit Pal |
| author_sort | Kushal Agrawal |
| collection | DOAJ |
| description | This research addresses the critical issue of women’s safety in urban environments, emphasizing the need for innovative solutions to establish secure pathways. SafeRoutes presents a holistic approach, integrating advanced clustering methodologies and GPS technology, detailing its relevance, ideation, methodology, and anticipated results. During ideation, the team prioritized integrating cutting-edge technologies—artificial intelligence, data analytics, and cloud computing. Emphasizing the constraints of existing safety solutions, the focus was on crafting a sophisticated framework for detailed assessments and real-time risk detection during transit. SafeRoutes aims to redefine women’s safety, providing actionable insights for urban planning and law enforcement. The methodology comprises three integral components. Firstly, a robust data ingestion pipeline connects to public and government data sources, ensuring near real-time models enriched with the latest data. The second component uses unsupervised machine learning models, comparing and employing various clustering algorithms. Parameters like crime rates, police presence, and infrastructure are utilized to cluster regions based on women’s safety. Lastly, integration with map APIs and cab service vendors addresses the travel aspect, facilitating real-time alerts for deviations into unsafe areas. Results encompass a nuanced correlation matrix classifying regions based on safety clusters, offering valuable insights for urban planning and law enforcement. Integration with cab services ensures SafeRoutes not only identifies safe paths but actively contributes to enhancing women’s safety during transit. The anticipated outcome positions SafeRoutes as a pioneering solution, contributing substantially to the discourse on urban safety and establishing a benchmark for future research. |
| format | Article |
| id | doaj-art-5dbc80ec5ba94e0c9d124e77d8a821c1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5dbc80ec5ba94e0c9d124e77d8a821c12024-11-19T00:01:17ZengIEEEIEEE Access2169-35362024-01-011216636816638010.1109/ACCESS.2024.348878410740283SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS IntegrationKushal Agrawal0https://orcid.org/0009-0005-5097-6399Aviral Srivastava1https://orcid.org/0000-0003-1164-7099Kanishk Sharma2https://orcid.org/0009-0005-5808-2306Sandeep Kumar Satapathy3https://orcid.org/0000-0002-4495-8099Sung-Bae Cho4https://orcid.org/0000-0002-7027-2429Shruti Mishra5https://orcid.org/0000-0002-9847-1411Abishi Chowdhury6https://orcid.org/0000-0002-7991-7385Amrit Pal7https://orcid.org/0000-0002-0555-9087School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaDepartment of Computer Science, Yonsei University, Seoul, South KoreaDepartment of Computer Science, Yonsei University, Seoul, South KoreaCentre for Advanced Data Science, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaThis research addresses the critical issue of women’s safety in urban environments, emphasizing the need for innovative solutions to establish secure pathways. SafeRoutes presents a holistic approach, integrating advanced clustering methodologies and GPS technology, detailing its relevance, ideation, methodology, and anticipated results. During ideation, the team prioritized integrating cutting-edge technologies—artificial intelligence, data analytics, and cloud computing. Emphasizing the constraints of existing safety solutions, the focus was on crafting a sophisticated framework for detailed assessments and real-time risk detection during transit. SafeRoutes aims to redefine women’s safety, providing actionable insights for urban planning and law enforcement. The methodology comprises three integral components. Firstly, a robust data ingestion pipeline connects to public and government data sources, ensuring near real-time models enriched with the latest data. The second component uses unsupervised machine learning models, comparing and employing various clustering algorithms. Parameters like crime rates, police presence, and infrastructure are utilized to cluster regions based on women’s safety. Lastly, integration with map APIs and cab service vendors addresses the travel aspect, facilitating real-time alerts for deviations into unsafe areas. Results encompass a nuanced correlation matrix classifying regions based on safety clusters, offering valuable insights for urban planning and law enforcement. Integration with cab services ensures SafeRoutes not only identifies safe paths but actively contributes to enhancing women’s safety during transit. The anticipated outcome positions SafeRoutes as a pioneering solution, contributing substantially to the discourse on urban safety and establishing a benchmark for future research.https://ieeexplore.ieee.org/document/10740283/Data ingestionmachine learning modelGPS integrationunsupervised learningclusteringGaussian mixture models |
| spellingShingle | Kushal Agrawal Aviral Srivastava Kanishk Sharma Sandeep Kumar Satapathy Sung-Bae Cho Shruti Mishra Abishi Chowdhury Amrit Pal SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration IEEE Access Data ingestion machine learning model GPS integration unsupervised learning clustering Gaussian mixture models |
| title | SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration |
| title_full | SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration |
| title_fullStr | SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration |
| title_full_unstemmed | SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration |
| title_short | SafeRoutes: Charting a Secure Path-A Holistic Approach to Women’s Safety Through Advanced Clustering and GPS Integration |
| title_sort | saferoutes charting a secure path a holistic approach to women x2019 s safety through advanced clustering and gps integration |
| topic | Data ingestion machine learning model GPS integration unsupervised learning clustering Gaussian mixture models |
| url | https://ieeexplore.ieee.org/document/10740283/ |
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