Reducing travel survey cost by utilising cross-sectional data from multiple time points
| Project/Area Number |
16K03931
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Commerce
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| Research Institution | Kobe University |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 消費者行動 / 交通行動 / 需要予測 / 繰り返し断面データ / 非集計モデル / サンプル数 / 調査頻度 / モデルの更新 |
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| Outline of Final Research Achievements |
When estimating disaggregate demand models for forecasting, only the most recent data is utilised even when cross-sectional data from multiple time points are available. This is not a good use of the data. This study applied a method, proposed by the author, which improves demand forecasts by utilising data from multiple time points. The study demonstrated that using data from multiple time points, where the number of observations from each time point is smaller, produced statistically significantly better forecasts than using data from the most recent time point. Practical contribution is to improve forecasting performance without any additional cost for the survey.
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| Academic Significance and Societal Importance of the Research Achievements |
交通調査には多大な費用を要する．本研究では，複数時点のデータを用いることで予測精度の向上が可能であり，それは調査済みの過去のデータを用いることで追加費用なく，また，各回の調査規模（調査費用）を削減することによっても実現可能であることを示した．また，大規模低頻度調査よりも小規模高頻度調査のほうが望ましい可能性を示した．
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Report
(4 results)
Research Products
(12 results)
