Development of AI intelligent traffic monitoring system using self-powered censors

使用自供电传感器开发AI智能交通监控系统

• 批准号: 22K04371
• 负责人: 安藤 良輔
• 金额: $ 2.66万
• 依托单位: Toyota Transportation Research Institute
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K04371/
• 关键词: 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

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.

为了给高速公路监测网布设的工程实践提供理论支持和决策依据，本文对相关文献进行了全面综述，并从精度导向研究、成本导向研究和可靠性导向研究3个方面总结了常用的监测网布设方法。系统梳理了常用部署方法的模型框架、算法特点和适用场景。结合目前理论研究与工程实践的差距以及智能高速公路建设与管理的新需求，探讨了智能高速公路的未来发展方向。结果表明，结合传统方法，e. G.交通波理论和规划模型，并结合实际应用，提出了一些方法，如：G.神经网络、遗传算法和多目标动态部署模型，可以有效提高路段和路网两级主要交通参数的传感器精度。大多数研究以成本为重点，采用生物启发式算法引入成本约束参数或以降低成本为优化目标，其中成本控制过程可以体现在布局方案制定中。为了提高监测网的可靠性，采用了各种方法，一般遵循两个概念：引入传感器失效概率或面向可靠性的全局优化。经过多年的发展，高速公路监控网络的研究已经能够支持工程实践中的大多数场景。