基于双长短期记忆网络组合的网络货运平台成交定价预测模型

网络货运平台运输服务订单的成交定价的预测结果是平台运营策略和承运人决策的直接体现，显著影响平台效益和承运人市场健康发展。以顺丰速递网络货运平台为例，通过缺失值处理和类别型数据转换进行数据预处理。针对网络货运平台成交定价预测精度问题，设计了基于双长短期记忆网络（LSTM）组合的网络货运平台成交定价预测模型，并采用K-means聚类分析预测结果。双LSTM组合模型相较于LSTM、支持向量回归（SVR）、两者相融合的LSTM-SVR以及基于灰色GM（1，1）和反向传播（BP）组合（GM（1，1）-BP）等模型，平均绝对误差（MAE）、均方误差（MSE）、平均绝对百分比误差（MAPE）最低且R²最高，分别为9.90、402.54、1.48和0.999 97。而K-means聚类分析对预测的订单成交定价进行评级的结果与实际情况一致。实验结果表明，所提出的双LSTM组合模型具备有效性和准确的网络货运平台成交定价预测效果。

Prediction model of transaction pricing in internet freight transport platform based on combination of dual long short-term memory networks

Prediction results of transaction pricing of transport service orders in internet freight transport platform are the direct reflections of both platform operation strategy and carrier decision， and influences both platform benefits and the healthy development of carrier market significantly. Taking internet freight transport platform of SF Express network as an example， the data were preprocessed through missing value processing and categorical data conversion. Aiming at the prediction precision problem of transaction pricing in internet freight transport platform， a new prediction model of transaction pricing in internet freight transport platform based on combination of dual Long Short-Term Memory networks（LSTM） was designed， and the prediction results were analyzed by K-means clustering. Compared with the models such as LSTM， Support Vector Regression （SVR）， Long Short-Term-Memory combined with Support Vector Regression （LSTM-SVR）， and combination of grey GM（1，1） and Back Propagation （BP） （GM（1，1）-BP）， the combination model of dual LSTM has the lowest Mean Absolute Error （MAE）， Mean Square Error （MSE）， Mean Absolute Percentage Error （MAPE） and highest R square （R²）， which is 9.90， 402.54， 1.48 and 0.999 97 respectively. The evaluation results of predicted order transaction pricing by using K-means clustering analysis are consistent with the actual values. Experimental results indicate that， the proposed combination model of dual LSTM has effectiveness and precise prediction effect of transaction pricing in internet freight transport platform.