Analysis and Visualization of Road Accidents Using Heatmaps Based on Web Data-Reference-Cited by-同舟云学术

Analysis and Visualization of Road Accidents Using Heatmaps Based on Web Data

Published:2023-12-01 Issue:2 Volume:18 Page:176-190
ISSN:1857-8462
Container-title:SEEU Review
language:en
Short-container-title:

Author:

Sinanaj Luan¹,Bexheti Lejla Abazi²

Affiliation:

1. 1 Faculty of Contemporary Sciences and Technologies , South East European University , North Macedonia

2. 2 Faculty of Contemporary Sciences and Technologies , South East European University , North Macedonia

Abstract

Abstract Road accidents have increased rapidly in recent years for a variety of reasons. Analyzing and visualizing road accidents through heatmaps can help improve policies for their prevention by informing about areas with a high-risk of road accidents. The purpose of this research is to build a model for the analysis and visualization of road accidents through heatmaps. Information about road accidents is extracted from the news of the main online media portals through scripts in the Python language and Web Scraping techniques. From the extraction of about 30,000 articles from news portals for one year, only 829 were selected in the end that provided information about road accidents. As a result, and contribution of this research, a corpus was built with the geographic coordinates of road accidents and on this data our model was applied for the analysis and visualization of high-risk areas of road accidents using heatmaps. The visualization of heatmaps was done through a Python script, where it was applied to the geographic coordinates of road accidents.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Link

https://www.sciendo.com/pdf/10.2478/seeur-2023-0064

Reference20 articles.

1. Biswas, A. A., Mia, M. J., & Majumder, A. (2019). Forecasting the Number of Road Accidents and Casualties using Random Forest Regression in the Context of Bangladesh. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5). Kanpur, India: IEEE. doi:10.1109/ICCCNT45670.2019.8944500

2. Chen, M.-M., & Chen, M.-C. (2020). Modeling Road Accident Severity with Comparisons of Logistic Regression, Decision Tree and Random Forest. Information, pp. 11(5), 270. doi: https://doi.org/10.3390/info11050270

3. Feng, M., Zheng, J., Ren, J., & Liu, Y. (2020). Towards Big Data Analytics and Mining for UK Traffic Accident Analysis, Visualization & Prediction. 12th International Conference on Machine Learning and Computing (ICMLC ‘20) (pp. 225–229). New York, NY, USA: ACM. doi: https://doi.org/10.1145/3383972.3384034

4. Folium. (2023, August). Folium — Folium 0.12.1 documentation. Retrieved May 2022, from https://python-visualization.github.io/folium/

5. INSTAT. (2022). Transporti, Aksidentet dhe Karakteristikat e Mjeteve Rrugore. (Instituti i Statistikave - Tiranë) Retrieved May 2022, from http://www.instat.gov.al/al/temat/industriatregtia-dhe-sh%C3%ABrbimet/transporti-aksidentet-dhe-karakteristikat-e-mjeteve-rrugore

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