Eltemasi Mahshid, Behtooiey Hassan
Knowledge& Information Science, Faculty of Management, University of Tehran, Tehran, Iran.
Meteorology, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran.
Heliyon. 2024 Mar 22;10(7):e28536. doi: 10.1016/j.heliyon.2024.e28536. eCollection 2024 Apr 15.
This study investigates the relationship between ambient temperature, weather conditions, and types of road accidents in Qazvin province, Iran. The research addresses a significant societal challenge of road accidents, particularly in developing countries like Iran. The objectives are to analyze the correlation between temperature and accident types and to develop a predictive model using data mining techniques. The study employs a quantitative approach, analyzing over 15,000 accident records from 2010 to 2020. The findings reveal a connection between the temperature variable and the type of road accidents as well as weather conditions. Additionally, data mining analysis identifies a predictable pattern among temperature variables, types of road accidents, and weather conditions. Implications of the study underscore the importance of considering temperature and weather conditions as secondary factors influencing accidents. The predictive model can aid decision-makers in formulating effective strategies to reduce accidents. Understanding the relationship between temperature, weather, and accident types enables the design of targeted interventions to enhance road safety. This research contributes valuable insights to accident reduction efforts and emphasizes the significance of addressing environmental variables in road safety planning and policy-making. Moreover, the results of the data mining pattern analysis indicate that car overturning accidents in various weather conditions are the primary type of accidents, followed by chain accidents. However, the types of accidents vary based on different weather conditions and temperatures. The study highlights the intricate connection between weather conditions, temperature, and types of road accidents. By utilizing data mining techniques, the research provides a predictive model for accident patterns, offering valuable insights to enhance road safety strategies.
本研究调查了伊朗加兹温省环境温度、天气状况与道路事故类型之间的关系。该研究解决了道路事故这一重大社会挑战，尤其是在伊朗这样的发展中国家。其目标是分析温度与事故类型之间的相关性，并使用数据挖掘技术开发一个预测模型。该研究采用定量方法，分析了2010年至2020年期间超过15000条事故记录。研究结果揭示了温度变量与道路事故类型以及天气状况之间的联系。此外，数据挖掘分析确定了温度变量、道路事故类型和天气状况之间的可预测模式。该研究的意义强调了将温度和天气状况视为影响事故的次要因素的重要性。预测模型可以帮助决策者制定有效的事故减少策略。了解温度、天气和事故类型之间的关系有助于设计有针对性的干预措施以提高道路安全。这项研究为事故减少工作提供了有价值的见解，并强调了在道路安全规划和政策制定中考虑环境变量的重要性。此外，数据挖掘模式分析的结果表明，在各种天气条件下，汽车翻车事故是主要的事故类型，其次是连环事故。然而，事故类型因不同的天气条件和温度而有所不同。该研究突出了天气状况、温度和道路事故类型之间的复杂联系。通过利用数据挖掘技术，该研究提供了一个事故模式预测模型，为加强道路安全策略提供了有价值的见解。
