期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2015
卷号：5
期号：2
页码：333-339
DOI：10.11591/ijece.v5i2.pp333-339
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. The bus arrival time is the primary information for providing passengers with an accurate information system that can reduce passenger waiting times. In this paper, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.
其他摘要：Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. The bus arrival time is the primary information for providing passengers with an accurate information system that can reduce passenger waiting times. In this paper, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.
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