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| Improved model of road impedance function based on LSTM neural network |
Fei WANG( ),Wei-xiang XU*() |
| School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China |
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Abstract The classical BPR impedance function model was improved in order to more accurately calculate the impedance value of road traffic. The long short-term memory (LSTM) neural network was established to predict the positive and negative of the undetermined coefficient value in the improved function. The traffic data collected from the Shangtang Elevated to Zhonghe Elevated sections of Hangzhou City were used to verify the model. The results were compared with the traditional BPR impedance function method, the classic EMME/2 cone delay function, the BP neural network prediction method and the LSTM neural network prediction method. Results show that the improved model has higher accuracy and reliability under the premise that the data accuracy meets the requirements, indicating that the road impedance calculated by using the improved model can more realistically reflect the traffic operation condition of the road.
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Received: 31 May 2020
Published: 30 July 2021
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| Fund: 国家自然科学基金资助项目（61672002） |
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Corresponding Authors:
Wei-xiang XU
E-mail: 18120893@bjtu.edu.cn;wxxu@bjtu.edu.cn
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基于LSTM神经网络改进的路阻函数模型
为了更加精确地计算道路的交通阻抗，对经典的BPR阻抗函数模型进行改进，建立长短期记忆(LSTM)神经网络预测改进函数中待定系数的正负，结合杭州市上塘高架至中河高架路段采集的交通数据进行验证. 与传统BPR阻抗函数方法、经典的EMME/2锥形延误函数计算方法、BP神经网络预测方法、LSTM神经网络预测方法得出的结果进行对比分析，结果显示在数据精度满足要求的前提下，改进的模型具有更高的准确性和可靠性. 说明使用改进模型计算得到的道路阻抗能够更为真实地反映道路的交通运行状况.
关键词：
城市交通,
改进BPR函数,
路阻函数,
长短期记忆神经网络,
行程时间计算
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