Abstract: Aiming to provide theoretical support and a decision-making reference for the engineering practice of freeway monitoring network deployment, the relevant literatures were 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 sensoring accuracy of main traffic parameters can be effectively improved at both section levels and network-level. Focused on cost, most of the existing researches used biological heuristic algorithms, e.g., bee colony and ant colony, 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. However, 3 common problems remain, which are strong model assumptions, high data requirements, and insufficient pertinence. Considering the new requirements of intelligent freeway construction and the management plan in China, the utilization of deep learning algorithms, dynamic parameter calibration, and the universality of optimal deployment models may become hot research issues in the future.