| Project/Area Number |
17K18236
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Information network
Web informatics, Service informatics
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| Research Institution | Kobe University |
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Principal Investigator |
Nishide Ryo 神戸大学, システム情報学研究科, 特命助教 (10546906)
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | モバイルコンピューティング / モバイルセンシング / 人流解析 / 携帯電話 / モバイル通信 / 無線ネットワーク / 高度交通システム / センシング / 人流 / 無線通信 / モバイルネットワーク |
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| Outline of Final Research Achievements |
Congestion in the bus degrades travel experience for passengers who cannot get their seats. To alleviate the congestion, it is necessary to balance the population of passengers on buses as well as those waiting at the bus stop.
In this project, WiFi sensor was developed to detect and record WiFi activities, and the collected data was examined to precisely grasp the pedestrian population at the bus stop. At the bus stop, unnecessary signals from passing cars and bicycles, surrounding buildings, etc. can also be detected even though they are not related to the potential passengers. To eliminate such signals, filtering parameters were examined carefully to accurately extract only the bus passengers' data.
Such information may be useful for remote passengers who would like to schedule the departure time to avoid congestion. Drivers can contact each other to exchange the information about congestion, and the company can modify the timetable based on the daily or weekly congested time.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究成果をバス会社に提供することで，日常的に繰り返される混雑時間帯や乗客のフローを特定し，早急に対応策を検討しダイヤ改正に反映できる．バス停で待つ客はバスの混雑状況を知ることで，乗車するか後続車を待つか判断するための参考にできる．このように，バスの利用者・事業者双方に役立つ情報システムの実現を目指しており，成果を地域や社会にも還元できる研究である．
技術面でのメリットとしては，他の大きな機器やアプリケーションを必要とせず，乗客のWiFiサービスを阻害しない点が挙げられる．また，乗客自身に機器やアプリケーションの操作を要求せず，シームレスで半自動的にデータ収集が行える点も本研究の特色である．
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