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- 저자 : Pengming Fu (검색결과 2 건)
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Yanjie Ji,Pengming Fu,P.T. Blythe,W. Guo,Wei Wang 대한토목학회 2015 KSCE JOURNAL OF CIVIL ENGINEERING Vol.19 No.7
Parking Guidance and Information (PGI) System becomes highly favorable for reducing circulating traffic and making efficient use of existing parking facilities. This paper is to examine the factors influence drivers’ willingness to use PGI. Factor analysis and the Structure Equation Model (SEM) were used to identify the latent attitudinal factors and the sensitivity of the factors was judged by Bayesian network. The heterogeneity of the factors was explored based on driver’s gender, age, driving years, education and travel frequency. The results show that drivers’ willingness to use PGI is significantly correlated to five attitudinal factors: perception of existing PGIs, difficulty in parking, confidence in the accuracy of the information, easy acquisition of information and information attributes. Male drivers, younger drivers, novice drivers and drivers who travel less frequently have lower level of willingness to use PGI.
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The Research on Prediction Models for Urban Family Member Trip Generation
Shuo Yang,Wei Deng,Qionghua Deng,Pengming Fu 대한토목학회 2016 KSCE JOURNAL OF CIVIL ENGINEERING Vol.20 No.7
This paper validates the hypothesis that head of household’s attributes and travel behavior having effects on other family members’ trip frequency in the same household. With using Multinomial Logit Model (MNL) and data source obtained from Resident Trip Survey in Nanjing, 2010, it shows that other family members’ trip frequency are much more relative to head of household’s. Therefore, we propose an idea that investigates the potential possibility of using Multinomial Logit model (MNL) and Support Vector Machine (SVM) to predict other family members’ trip frequency with household’s information. By comparing the prediction accuracy, it is found that the SVM only slightly performs better on overall average accuracy. Then a modified SVM model with significant variables estimated by MNL model is given. The results report that, modified SVM model not only performs better on overall average accuracy than general SVM model and MNL model, but significantly improves partial average accuracy compared with general SVM model. However, the modified SVM still expectedly has a poor performance on predicting for small proportion samples. In the end, some possible improvements are discussed as well as expectation for further studies.
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