Using convolutional neural network to construct classification models of bus drivers\' car-following behavior
| Autor: | Lin, Wen-Chin, 林玟妗 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Safety is one of the issues in public transportation that the government emphasizes. If bus companies are able to comprehend the driving behavior of bus drivers, then companies can effectively adopt appropriate management measures for drivers. The purpose of this study is to develop classification models of bus drivers' car-following behavior using convolutional neural networks (CNN). The headway data recorded by Mobileye systems were used as the input data to build and train the CNN classification models. The proposed classification models would facilitate determining the car-following types of bus drivers based on Mobileye headway data. We firstly processed the Mobile headway data and then considered three major factors that may affect the car-following behavior of drivers, including weather, speed and day-or-night. According to the three factors, we designed seven different scenarios and filtered the data by scenario. The filtered data were used to generate headway matrices for each driver. The matrices were the input data of the CNNs for training and classification. The trained models can be used to determine the car-following type of the input headway matrix of a driver. The experimental results show that the proposed CNN models have accurate prediction in most of the scenarios. The average accuracy is 97%. The results of this study can be used as a reference in classifying and determining the car-following types of drivers. Bus companies can utilize the classification results to take actions on the driving behavior of drivers. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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