Short-term traffic prediction is a key component of Intelligent Transportation Systems. It uses historical data to construct models for reliably predicting traffic state at specific locations in road networks in the near future. Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies. As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models. Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by . Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.

中文翻译：

---

时空数据聚合对基于机器学习算法的短期交通量预测的启示

短期交通预测是智能交通系统的关键组成部分。它使用历史数据来构建模型，以在不久的将来可靠地预测道路网络中特定位置的交通状况。尽管这是一个成熟的领域，但是短期流量预测仍然存在一些与最佳数据分辨率的选择，非经常性拥塞的预测以及相关时空依赖性建模有关的未解决问题。为了解决这些问题，本文研究了人工神经网络，随机森林和支持向量回归算法在不同数据分辨率下可靠地对交通流进行建模并应对意外交通事件的能力。我们还探索了不同的特征选择方法，以识别和更好地理解时空属性，这些时空属性对这些模型的可靠性影响最大。实验结果表明，对于多元时空机器学习模型，数据聚合并不一定能达到良好的性能。使用高分辨率30秒输入数据学习的模型比相应的基线ARIMA模型要好。此外，基于递归特征消除的特征选择导致模型的性能优于基于线性相关的特征选择的模型。