This paper proposes a Kalman Filter (KF) technique to predict the short-term flow at urban arterials based on the information of connected and Bluetooth equipped vehicles. Online traffic flow prediction using real-time data derived from different sensors is still an open research subject. To this end, a Kalman Filter model is developed to predict traffic flow based on two sources of real-time data, i.e. Connected Vehicles (CVs) and Bluetooth data. We also apply a Faded Memory Kalman Filter (FMKF) by considering more weights for new measurements to overcome the issue of inaccuracy in the prediction model and to predict the traffic flow with more resolution. At first, based on training data from Vissim traffic simulator, parameters of the KF's equations are calibrated using a machine learning based and big data processing. Performance of the conventional and faded memory KF models are then validated and compared using some test data pertaining to different rates of connected vehicles and Bluetooth-equipped vehicles (BVs). We use a pilot study of the city of Melbourne, Australia for numerical tests. The results indicate significant superiority of the FMKF over the KF in various traffic situations, as such the prediction error in some cases has reduced up to 60%. This paper contributes to the literature in three folds: (i) It uses a computationally efficient flow prediction algorithm based on synthesizing data from CVs and BVs (ii) It proposes to use an adaptive form of KF (i.e. FMKF) to compensate for the prediction error originating from modelling error. Hence, the model can perform well for a range of traffic conditions (iii) The proposed model works well even with low penetration rates (PRs) of the CVs or BVs (say 20%).

本文提出了一种卡尔曼滤波器（KF）技术，用于根据已连接和配备蓝牙的车辆的信息来预测城市动脉的短期流量。使用来自不同传感器的实时数据进行在线交通流量预测仍然是一个开放的研究课题。为此，开发了卡尔曼滤波器模型，以基于两个实时数据源（即联网车辆（CV）和蓝牙数据）来预测交通流量。我们还通过考虑对新度量值使用更多权重来应用衰落内存卡尔曼滤波器（FMKF），以克服预测模型中不准确的问题，并以更高的分辨率预测流量。首先，根据来自Vissim交通模拟器的训练数据，使用基于机器学习和大数据处理的方法对KF方程的参数进行校准。然后，使用与已连接车辆和配备蓝牙的车辆（BV）的不同速率有关的一些测试数据来验证和比较常规和褪色内存KF模型的性能。我们使用对澳大利亚墨尔本市的初步研究进行数值测试。结果表明，在各种交通情况下，FMKF优于KF，因此在某些情况下，预测误差降低了60％。本文从三个方面为文献做出了贡献：（i）使用基于CV和BV数据合成的高效计算流量预测算法（ii）提出使用自适应形式的KF（即FMKF）来补偿预测源自建模错误的错误。因此，