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Analysis of Bus Commuting Time of High School Students in Taichung City, Taiwan

Author

Listed:
  • Yao-Nan Lien

    (Department of Computer Science and Information Engineering, Asia University, Taiwan)

  • Jing-Doo Wang

    (Department of Computer Science and Information Engineering, Asia University, Taiwan)

  • Shin-Hung Pan

    (Department of M-Commerce and Multimedia Applications, Asia University, Taiwan)

  • Cheng-Yuan Ho

    (Department of Computer Science and Information Engineering, Asia University, Taiwan)

  • Shu-Chuan Liao

    (Department of Social Work, Asia University, Taiwan)

Abstract

Most high school students in the metropolitan areas of Taiwan commute to school by public transportation. Due to the great pressure of academic workload, the time spent on commuting is an unbearable burden. In this study, by using both field study and the analysis based on the Taichung City Traffic Big Data database, we analyzed the bus journeying behavior of high school students of Taichung City on their school commuting. Our study results will be provided to the city government and high schools as a reference to support their transportation policy making. The results of the study show that, with the exception of a very small number of students, most students ridded less than one hour. It took less time to go to school in the morning and more time to get home in the afternoon. We also provided descriptive statistics on the commuting time of the main commuters of each school. The results of big data analysis was verified by the field observation without any obvious inconsistency, which lays an empirical basis for further use of big data analysis in the future.

Suggested Citation

  • Yao-Nan Lien & Jing-Doo Wang & Shin-Hung Pan & Cheng-Yuan Ho & Shu-Chuan Liao, 2020. "Analysis of Bus Commuting Time of High School Students in Taichung City, Taiwan," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 167-189, June.
  • Handle: RePEc:aag:wpaper:v:24:y:2020:i:2:p:167-189
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    More about this item

    Keywords

    Smart Transportation; Big Data Analytics;

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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