基于BP神經(jīng)網(wǎng)絡(luò)的網(wǎng)約車服務(wù)質(zhì)量研究

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中圖分類號：F572 文獻(xiàn)標(biāo)志碼：A DOI： 10.13714/j.cnki.1002-3100.2025.12.014

Abstract：Toexplorethekeyfactorsafecting thequalityofonlineride-haiingservices，aBPneuralnetworkisusedto construct anonlineride-hailing servicequality model.The paperuses MIVtoanalyzetheimportanceof influencing factorsand appliesK-meansclusteringmethodtoanalyzetheheterogeneityofonlineride-hailingpassengers.Theresultsindicatethat factorssuchasdriversatisfaction，travelcosts，traveltime，operatingtimerange，andtraficsafetyplayakeyoleintheoeall servicesatisfactionofonlineride-halingservices，andthecorrespondingcountermeasuresareproposedbasedontheimportance of above-mentionedfactors.Usingpassngergender，age，averagemonthlyhouseholdincome，andfrequencyofonlineridehailing usageasclusteringvariables，fourdiferentcharacteristicsofolieridehailingpasengergroupsareidentified.Thepaperprovides a theoretical basis and practical guidance for improving the quality of online ride-hailing services.

Key Words： online ride-hailing; service quality; BP neural network; MIV; K-means

0引言

隨著Uber、Lyft、滴滴出行、Grab各種網(wǎng)約車平臺(tái)的興起，網(wǎng)約車逐漸成為人們?nèi)粘３鲂械闹匾M成部分。（剩余6289字）