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.

为了给高速公路监控网络部署的工程实践提供理论支撑和决策支持，综合回顾了相关文献，从精度导向研究、成本导向研究、可靠性导向研究三个方面总结了常用方法，进行了总结。系统梳理了常见部署方式的模型框架、算法特点及适用场景。结合当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，探讨了未来的发展方向。结果表明，通过结合传统方法，e。 g。交通波理论和规划模型，以及一些方法，例如。 g。神经网络、遗传算法和多目标动态部署模型，可以在路段层面和网络层面有效提高主要交通参数的传感器精度。围绕成本，大多数研究采用生物启发式算法引入成本约束参数或将降低成本作为优化目标，其中成本控制的过程可以体现在布局方案的制定中。为了提高监测网络的可靠性，采用了各种方法，通常遵循两个概念：引入传感器故障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可以支持工程实践中的大部分场景。