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

机译:基于区块链的城市轨道客流预测的联邦学习方法

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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.
机译:随着城市的加速发展,交通能力不能赶上交通崛起。城市轨道交通系统面临着严峻的挑战。准确的客流预测有助于优化操作计划,提高运行效率。基于传统的基于机器学习的智能控制方法受数据不足的限制。由于缺乏有效的激励和信任,来自不同城市铁路或运营商的数据无法直接共享。在本文中,我们提出了一种分布式联邦学习方法,用于基于区块链的轨道交通乘客的精确预测。该方法在没有可信中央服务器的情况下执行分布式机器学习。区块链智能合同用于实现整个联邦学习的管理。考虑到传统时间序列模型的局限性,我们选择分布式的长期内存(LSTM)网络作为乘客预测的监督学习模型。此外,我们建立了一个激励机制,以奖励为本模型做出贡献的参与者。仿真结果表明了我们所提出的智能控制方法的高效率和准确性。

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