Road traffic flow forecasting is among the most important use case associated with smart cities. Traffic forecasting allows drivers to select the fastest route towards their target destinations. A prerequisite for traffic flow management is accurate traffic forecasting. In this study, we introduce a framework for traffic forecasting that uses data on air pollution. The reason to select that data is air pollution rates are often associated with traffic congestion, and there is intensive research that exists to forecast air pollution by road traffic. To the best of our knowledge, any effort to enhance road traffic prediction through air quality and ensemble regression model techniques is not yet been proposed. In this research, our contribution is twofold. Firstly, we have performed a comparative analysis of 7 different regression models to find out which model gives better accuracy. Secondly, we propose a framework using regression models in which the first regression model's result is boosted using boosting ensemble method and is passed to the next regression model which shows that the proposed framework gives more satisfying results than the above 7 regression models. The experimental findings show the effectiveness of the proposed framework which decreases the error rate by 2.47 %.

道路交通流量预测是与智慧城市相关的最重要的用例之一。交通预测允许驾驶员选择前往目标目的地的最快路线。交通流管理的先决条件是准确的交通预测。在本研究中，我们引入了一个使用空气污染数据的交通预测框架。选择该数据的原因是空气污染率通常与交通拥堵有关，并且存在大量研究来预测道路交通造成的空气污染。据我们所知，尚未提出任何通过空气质量和集成回归模型技术来增强道路交通预测的努力。在这项研究中，我们的贡献是双重的。首先，我们对 7 种不同的回归模型进行了比较分析，以找出哪种模型的准确性更高。其次，我们提出了一个使用回归模型的框架，其中第一个回归模型的结果使用 boosting ensemble 方法被提升，并传递给下一个回归模型，这表明所提出的框架比上述 7 个回归模型给出了更令人满意的结果。实验结果表明，所提出框架的有效性降低了 2.47% 的错误率。s结果使用boosting ensemble方法提升并传递给下一个回归模型，这表明所提出的框架比上述7个回归模型给出了更令人满意的结果。实验结果表明，所提出框架的有效性降低了 2.47% 的错误率。s结果使用boosting ensemble方法提升并传递给下一个回归模型，这表明所提出的框架比上述7个回归模型给出了更令人满意的结果。实验结果表明，所提出框架的有效性降低了 2.47% 的错误率。