Using Artificial Neural Network in Passenger Trip Distribution Modelling
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- YALDI Gusry
- ISST-Transport Systems, University of South Australia
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- TAYLOR Michael A P
- ISST, University of South Australia
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- YUE Wen Long
- ISST-Transport Systems, University of South Australia
書誌事項
- タイトル別名
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- (A Case Study in Padang, Indonesia)
説明
The recent literature indicates a growing adoption of Artificial Neural Network (NN) in travel demand modelling, and this study is one of them, focusing on passenger trip distribution, especially work trips. Various models of NN were developed with the variables of learning rates (LR), hidden layer node numbers (HLNN), and percentages of dataset for training, validation and testing. Comparisons with the Doubly Constrained Gravity model (DCGM) were used to measure the performance of NN models. The results suggested that the validated NN model with learning rate 0.1 can almost reach the same performance of DGCM model. Further, the statistical test results shows that the NN models are unable to reach the same performance as DGCM although the NN model was trained, validated and tested using the same data.
収録刊行物
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- Journal of the Eastern Asia Society for Transportation Studies
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Journal of the Eastern Asia Society for Transportation Studies 8 (0), 682-693, 2010
Eastern Asia Society for Transportation Studies
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詳細情報 詳細情報について
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- CRID
- 1390282680267974656
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- NII論文ID
- 130000400969
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- ISSN
- 18811124
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可
