PDF(Pubmed)
Abstract：
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.
摘要：
这项研究调查了环境温度之间的关系，天气条件,以及Qazvin省的道路交通事故类型，伊朗。该研究解决了道路交通事故的重大社会挑战，尤其是像伊朗这样的发展中国家。目的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预测模型。这项研究采用了定量的方法，分析了2010年至2020年的15,000多个事故记录。研究结果揭示了温度变量与道路事故类型以及天气条件之间的联系。此外,数据挖掘分析识别温度变量之间的可预测模式，道路交通事故的类型，和天气条件。该研究的含义强调了将温度和天气条件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策略来减少事故。了解温度之间的关系，天气,和事故类型可以设计有针对性的干预措施，以提高道路安全。这项研究为减少事故的努力提供了宝贵的见解，并强调了在道路安全规划和决策中解决环境变量的重要性。此外，数据挖掘模式分析结果表明，各种天气条件下的翻车事故是事故的主要类型，其次是连锁事故。然而,事故的类型根据不同的天气条件和温度而有所不同。这项研究强调了天气状况之间的错综复杂的联系，温度,以及道路交通事故的类型。通过利用数据挖掘技术，这项研究为事故模式提供了一个预测模型，提供有价值的见解，以加强道路安全策略。
