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首页> 外文会议>IEEE International Conference on Cloud Computing and Big Data Analysis >Integrated predicting model for daily passenger volume of rail transit station based on neural network and Markov chain
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Integrated predicting model for daily passenger volume of rail transit station based on neural network and Markov chain

机译：基于神经网络和马尔可夫链的轨道交通车站日客流量综合预测模型

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Passenger volume prediction plays an important role in rail transit in order to support traffic planning, construction, operation, and departure interval calculation. Firstly, the paper analyzes the influencing factors of passenger volume, such as the land use. Then, by combining the characteristics of a neural network and a Markov chain, we present a predict model. The land use, information entropy, maturity of the community and the number of bus stations of the rail transit line 6, 7, 13 in Beijing were selected as the input data, and the daily passenger volume of the rail transit station as the output. The model was calibrated and verified with the data of Line 1 and 2. The result shows that neural network model can apply in long-term passenger volume prediction well and predict precision of the model are relatively high.
机译：客运量预测在铁路运输中起着重要作用，以支持交通规划，建设，运营和出发间隔的计算。首先，分析了客运量的影响因素，如土地利用情况。然后，通过结合神经网络和马尔可夫链的特征，我们提出了一个预测模型。选择北京轨道交通6、7、13号线的土地利用，信息熵，社区成熟度和公交车站数量作为输入数据，并选择轨道交通车站的每日载客量作为输出数据。对模型进行了标定和线1和线2的验证。结果表明，神经网络模型可以很好地应用于长期客运量预测，预测精度较高。

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