Development of AI intelligent traffic monitoring system using self-powered censors
| Project/Area Number |
22K04371
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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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| Review Section |
Basic Section 22050:Civil engineering plan and transportation engineering-related
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| Research Institution | Toyota Transportation Research Institute |
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Principal Investigator |
安藤 良輔 (宿良) 公益財団法人豊田都市交通研究所, その他部局等, 主幹研究員 (70251121)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2024: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | traffic engineering / sensors layout methods / monitoring network / traffic flow sensing / freeway / optimization goal / 交通量調査 / AI / セルフ電源供給センサ |
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| Outline of Research at the Start |
本研究は、交通調査システムを構築して、新しいアプローチで識別と判別の課題を克服していく。このシステムは、1）SPP（空間ピラミッドプーリング）ネットワークを導入し複雑な交通環境による混雑なインプット信号を処理して、低い識別精度の問題を解決する。 2）WGAN-GPを活用し自動生成能力を持たせて、不均衡や極少サンプルの課題に対応して、車種分類の精度を高める。また、このシステムは圧電エネルギーに基づくセルフ電源供給の特徴を有すると同時に、検知センサーの設置が簡単で携帯性が優れていることから実用性が高い特徴を有する。交通量と速度を、同時に高い精度で低コストで実現する新しい交通調査として期待できる。
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| Outline of Annual Research Achievements |
Aiming to provide theoretical support and a decision-making for the engineering practice of freeway monitoring network deployment, the relevant literature was comprehensively reviewed and the common methods from three aspects: precision-oriented research, cost-oriented research, and reliability-oriented research, were summarized. The model framework, algorithm characteristics and applicable scenarios of common deployment methods were systematically combed. Furthermore, the future developing direction was discussed considering the current gap between theoretical research and engineering practice as well as the new demands in the context of intelligent freeway construction and management. Results show that by combining traditional methods, e. g. traffic wave theory and planning model, with some methods, e. g. neural network, genetic algorithm and multi-objective dynamic deployment model, the sensor accuracy of main traffic parameters can be effectively improved at both section levels and network-level. Focused on cost, most research used biological heuristic algorithms to introduce cost constraint parameters or considering reducing cost as the optimization goal, in which the process of cost control can be reflected in the layout scheme formulation. To improve the reliability of monitoring network, various methods were utilized that generally followed two concepts: introducing sensor failure probability or reliability oriented global optimization. After years of development, the research on freeway monitoring networks can support most scenarios in engineering practice.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
研究分担者が新型コロナ感染症の影響を受けて中国へ帰国され、研究協力者となった。一方、日中間の行き来は双方の政府の水際対策等で困難な状況にあった。本研究の実験は打合せや確認ができないままで推移してきている。そのため、理論的な内容のみ優先的に進めている状況である。
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| Strategy for Future Research Activity |
日中両国政府が今年（2023年）に入ってから徐々に様々な緩和策を打ち出されてきたので、4月から対面打合せや実験の準備等を加速的に進めていく予定である。
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Report
(1 results)
Research Products
(3 results)
