A Blockchain Based Federal Learning Method for Urban Rail Passenger Flow Prediction

Abstract

With the accelerated development of cities, the traffic capacity cannot catch up with traffic rising. The urban rail transit system is facing severe challenges. Accurate prediction of passenger flow can help optimize the operation plan and improve operation efficiency. Traditional machine learning-based intelligent control methods are restricted by insufficient data. Owing to lacking effective incentives and trust, data from different urban rail lines or operators cannot be shared directly. In this paper, we propose a distributed federal learning method for accurate prediction of rail transit passenger flow based on blockchain. The proposed method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federal learning. Considering the limitations of the traditional time series model, we choose the distributed long and short term memory (LSTM) networks as the supervised learning model for passenger flow prediction. In addition, we establish an incentive mechanism to reward those participants who contribute to the model. The simulation results demonstrate high efficiency and accuracy of our proposed intelligent control method.