Model Controlled Prediction: A Reciprocal Alternative of Model Predictive Control
Shen Li, Yang Liu, Xiaobo Qu
Abstract
Dear editor, This letter presents a reciprocal alternative to model predictive control (MPC), called model controlled prediction. More specifically, in order to integrate dynamic control signals into the transportation prediction models, a new fundamental theory of machine learning based prediction models is proposed. The model can not only learn potential patterns from historical data, but also make optimal predictions based on dynamic external control signals. The model can be used in two typical scenarios: 1) For low real-time control signals (e.g., subway timetable), we use a transfer learning method, so that the prediction models obtained from training data under the old control strategy can be predicted accurately under the new control strategy. 2) For dynamic control signals with high real-time (e.g., online ride-hailing dispatching instructions), we establish a simulation environment, design a control algorithm based on reinforcement learning (RL), and then let the model learn the mapping relationship among dynamic control signals, data, and output in the simulation environment. The experimental results show that the reasonable modeling of control signals can significantly improve the performance of the traffic prediction model.