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

为了给高速公路监控网络部署的工程实践提供理论支持和决策依据，综合查阅了相关文献，从精度导向研究、成本导向研究和可靠性导向研究三个方面总结了常用的方法。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。结合当前理论研究与工程实践的差距，以及高速公路智能化建设与管理的新要求，探讨了未来的发展方向。结果表明，将交通波理论、规划模型等传统方法与神经网络、遗传算法、多目标动态部署模型等方法相结合，可有效提高路段级和网络级主要交通参数的传感器精度。以成本为研究重点，多数研究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，将成本控制过程反映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，一般遵循两个概念：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能够支持工程实践中的大多数场景。

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.