Performance Comparison of Machine-learning Models for Analyzing Weather and Traffic Accident Correlations-Reference-Cited by-同舟云学术

Performance Comparison of Machine-learning Models for Analyzing Weather and Traffic Accident Correlations

Published:2023-09-30 Issue:3 Volume:21 Page:225-232
ISSN:2234-8883
Container-title:Journal of information and communication convergence engineering
language:en
Short-container-title:JICCE

Author:

Xuan Li Zi¹^ORCID,Yang Hyunho¹^ORCID

Affiliation:

1. School of Software, Kunsan National University, 54150, Republic of Korea

Funder

LX Spatial Information Research Institute

Publisher

Korea Institute of Information and Communication Engineering

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Media Technology,Information Systems

Link

https://pdf.medrang.co.kr/JICCE/2023/021/JICCE021-03-225.pdf

Reference10 articles.

1. US News & World Report. (2023, January 10). US Study: One Year of Road Crashes Cost Society $340 Billion. [Online] Available: https://www.usnews.com/news/business/articles/2023-01-10/us-studyone-year-of-road-crashes-cost-society-340-billion.

2. Predicting road traffic density using a machine learning-driven approach

3. A Comparative Study of Machine Learning Algorithms to Predict Road Accident Severity

4. A Short-Term Traffic Flow Prediction Method Based on SVR

5. Demo: Intelligent Transport System (ITS) Assisted Road Weather & Traffic Services

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