论文标题
使用机器学习技术的交通拥堵预测
Traffic Congestion Prediction Using Machine Learning Techniques
论文作者
论文摘要
交通拥堵的预测在做出未来的决策中起着至关重要的作用。尽管已经进行了许多有关拥塞的研究，但其中大多数不能涵盖所有重要因素（例如天气条件）。我们提出了一个交通拥堵的预测模型，该模型可以根据日，时间和几个天气数据（例如温度，湿度）来预测拥堵。为了评估我们的模型，已针对新德里的流量数据进行了测试。通过这种模型，可以预测一周的道路交通拥堵，平均RMSE为1.12。因此，该模型可用于事先采取预防措施。
The prediction of traffic congestion can serve a crucial role in making future decisions. Although many studies have been conducted regarding congestion, most of these could not cover all the important factors (e.g., weather conditions). We proposed a prediction model for traffic congestion that can predict congestion based on day, time and several weather data (e.g., temperature, humidity). To evaluate our model, it has been tested against the traffic data of New Delhi. With this model, congestion of a road can be predicted one week ahead with an average RMSE of 1.12. Therefore, this model can be used to take preventive measure beforehand.
