… A few studies in the literature examined the traffic flow prediction with a multi-step ahead
strategy. Adaptive Kalman filtering theory-based prediction models were proposed and …

… (DNN) with multiple hidden layers on historical traffic flow data and contextual factor data
for long-term traffic flow forecasting. Taking advantage of the automatic feature extraction …

… Subsequently, some extended Kalman filtering approaches have been applied to traffic … ,
such as machine translation [61], multi-step ahead time series prediction [62], and other various …

… traffic flow prediction is important to realize real-time traffic … and non-stationarity in short-term
traffic volume data, it is hard to … prediction models should be focused; the multi-step ahead …

… mentioned above, we introduce a multi-step coupled graph … -grained predicting urban crowd
flows with adaptive spatio-… Ye, “Short-term traffic volume forecasting using kalman filter with …

… [11] proposed a KARIMA prediction model to forecast traffic flow, which combined Kohonen
maps … An adaptive Kalman filter based traffic prediction algorithm for urban road network. In …

… Kalman filtering models, which use both historical and real-time data, have been widely used
to estimate bus arrival times [(27; 1; 26)]. Previous research in this field has mostly focused …

Abstract To accurately predict traffic flow on … ) traffic flow prediction model based on
Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, …

… —Traffic flow prediction is the foundation of urban traffic … [21] used the Kalman filter to
complete the traffic prediction task … to predict multi-step urban crowds by combining the prediction …

… models include the historical average model [4] , Autoregressive Integrated Moving Average
(ARIMA) [5] and Kalman filter model [6] , which generally have fewer model parameters and …