International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Call for Paper Volume 7 Issue 3 March 2026 Submit your research before last 3 days of to publish your research paper in the issue of March.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
March 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Daylength Analysis-based Accident Hotspot Prediction with GIS Data for Road Safety Improvement using ANFGuIS

Author(s) Lalitha Reddy Badam
Country United States
Abstract An intelligent Geographic Information System (GIS)-based Accident Hotspot Prediction (AHP) in smart cities is necessary for road safety. However, the prevailing works overlooked the twilight duration via latitude of a location, causing improper AHP. Thus, daylength-based density analysis using Inverse Hyperbolic Tangent-Kernel Density Estimation (IHT-KDE) and Adaptive Neuro Fuzzy Gudermannian Interference System (ANFGuIS)-based AHP is proposed. Primarily, the GIS-based road accident data are collected and pre-processed. Then, the weather-based clustering is performed using Dirichlet Phi-Square Density-Based Spatial Clustering of Applications with Noise (DPS-DBSCAN). Next, the feature extraction, twilight and latitude-based daylength analysis, and density analysis are performed. Meanwhile, the road network accident propagation is modeled using a Knowledge Graph (KG), and the accident propagation features are extracted. Further, the ANFGuIS-based accident labeling and prediction is done. Finally, severity-based road safety intervention using Haversed Sine Markov Decision Process (HS-MDP) is carried out. Thus, the proposed system predicts the accident hotspot with an accuracy of 98.87%, outperforming existing works.
Keywords Accident Prediction, Road Safety, Smart Cities, Geographic Information System (GIS), Machine Learning (ML), Data Analytics, and Road Safety Intervention
Field Engineering
Published In Volume 7, Issue 3, March 2026
Published On 2026-03-05
DOI https://doi.org/10.70528/IJLRP.v7.i3.2003
Short DOI https://doi.org/hbrj2f
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 12 Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com