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Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation
A Kalman filter model considering the correlation property of traffic flow parameters is proposed to realize network short-term traffic flow prediction under jam traffic. The proposed state-space model of short-term traffic flow prediction is presented by solving the conservation equation using Lax-Wendroff scheme. In addition, the spatial-temporal characteristics of the traffic flow on urban expressway networks and the influence factors of on and off ramp are taken into account for flow rate prediction. The estimation algorithm of the proposed state-space model is designed based on the Kalman filter method. A region expressway network in Beijing is taken as an example to evaluate the performance of the proposed method. The results show that the maximum prediction mean absolute percentage error (MAPE) of the proposed Kalman filter model is less than 10% since the input of the Kalman filter model considers the impacts the spatial-temporal characteristics, and the mean of prediction MAPE is 7.96%. For the same predicted conditions, the mean prediction MAPEs of ARIMA and Elman model are 19.88% and 10.51%, respectively
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand
vehicle sharing services. However, predicting passenger demand over multiple
time horizons is generally challenging due to the nonlinear and dynamic
spatial-temporal dependencies. In this work, we propose to model multi-step
citywide passenger demand prediction based on a graph and use a hierarchical
graph convolutional structure to capture both spatial and temporal correlations
simultaneously. Our model consists of three parts: 1) a long-term encoder to
encode historical passenger demands; 2) a short-term encoder to derive the
next-step prediction for generating multi-step prediction; 3) an
attention-based output module to model the dynamic temporal and channel-wise
information. Experiments on three real-world datasets show that our model
consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page
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