| 研究課題/領域番号 |
22K14339
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分22050:土木計画学および交通工学関連
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| 研究機関 | 広島大学 |
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研究代表者 |
VARGHESE VARUN 広島大学, 先進理工系科学研究科(国), 助教 (40834718)
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| 研究期間 (年度) |
2022-04-01 – 2024-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2023年度: 2,730千円 (直接経費: 2,100千円、間接経費: 630千円)
2022年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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| キーワード | Transportation Planning / Machine Learning / ICT / Emissions / Randomized Control Trial |
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| 研究開始時の研究の概要 |
The goals of the research will be achieved through two objectives. The first objective of the research is to develop an ICT platform that implements an ethical, attractive, and personalized algorithm that will prioritize the reduction of CO2 emissions. The ICT platform will provide users with a choice between the novel algorithm and traditional algorithms (such as profit maximization and travel time reduction). The second objective of the research is to conduct a randomized control trial in Higashihiroshima to quantitatively evaluate the adoption and effectiveness of the ICT platform.
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| 研究実績の概要 |
Firstly, based on a two-week field experiment in Hiroshima, an empirical analysis established positive impact of monetary incentives on mode choice utility. Meanwhile, people with pro-environmental attitudes were observed to be less likely to choose cars and more likely to choose public transit and non-motorized transport modes. The findings were presented at the 102nd TRB annual meeting.
Second analysis in the project, tested the impact of data preprocessing and GPS data frequency on accuracy and interpretability using machine learning models. The results show that data preprocessing have a larger impact as compared to GPS data frequency.
Third analysis involves a comprehensive review of literature on travel mode recommender systems, which summarizes the different algorithms, data types, and impacts of such tools.
One manuscript is currently under review and two under preparation.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The project is progressing as per plan.
Background work to understand the impact of monetary incentive provision and personal preferences on mode choice was finished. The relative contribution of both of these important variables on predicted transport mode shares was also evaluated. In addition, the impact of data collection frequency and data preprocessing on model prediction accuracy and interpretability was also analyzed. A review of previous literature on existing tools was also conducted as per plan.
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| 今後の研究の推進方策 |
In the second year of the project, the coding work on the algorithm development will be done. This part would entail development of the ICT platform which recommends travel mode to people based on CO2 emission minimization. Upon completion of the ICT platform, the platform will be tested through a trial survey in Higashihiroshima, Japan. The findings of the project will be summarized in papers and will be presented in peer-reviewed conferences and published in journals.
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