Arliansyah, Joni and Hartono, Yusuf (2015) Trip Attraction Model Using Radial Basis Function Neural Networks (Peer Review). Elsevier Ltd.
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Abstract
Trip Attraction model with seven independent variables, i.e., population size, number of schools, number of students, number of teachers, areas of school buildings, number of offices, and number of houses applying Radial Basis Function Neural Networks (RBFNN) is presented in this paper. The data used in this study were derived from the origin destination survey in Palembang and the model was developed using 85 sets of land use - trip attraction data. A comparison was made between RBF model and regression model. The results show that RBF model performs better than regression model in predicting trip attraction and important variables are number of students, number of teachers, total areas of school buildings and number of offices.
| Item Type: | Other |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA169.H37 Reliability (Engineering), Engineering design, Civil engineering |
| Divisions: | 03-Faculty of Engineering > 22101-Civil Engineering (S2) 03-Faculty of Engineering > 22201-Civil Engineering (S1) |
| Depositing User: | Dr Joni Arliansyah |
| Date Deposited: | 15 Nov 2019 08:18 |
| Last Modified: | 09 Mar 2020 07:13 |
| URI: | http://repository.unsri.ac.id/id/eprint/16539 |
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