科学、合理的布局交通检测设备可以提高交通检测系统的交通监控能力，现有研究主要面向高流量的城市道路和高速公路。本研究以低流量的稀疏道路为研究对象，将无人飞机和地面交通检测设备相结合，开展协同事件检测。首先，明确稀疏道路的交通事件检测机制，协调优化布局交通检测设备，建立两阶段的交通检测设备布局优化模型；其次，派出无人飞机侦察，建立动态的无人飞机巡航路径多目标优化模型，设计多目标优化求解算法，研究不同的无人飞机侦察需求、多重优化目标、算法参数对无人飞机路径优化结果的影响；接着，建立多情景数值模拟仿真方法，分析无人飞机的交通事件检测效果，开展实证研究，并评价无人飞机的事件检测经济性。研究成果可为稀疏道路的交通检测设备布局规划提供科学依据，对改善稀疏道路的交通监控水平具有重要的实用价值。

Deploying traffic detection equipment scientifically and rationally can improve the traffic monitoring ability of traffic detection system, and the current studies mainly focus on high-volume urban roads and highways. This study focuses on low-volume sparse road, and introduces unmanned aerial vehicle (UAV) to detect sparse road traffic incident, combining with ground traffic detection equipment. Firstly, the traffic incident detection mechanism for sparse road is demonstrated, and a two-phase optimization model of deploying ground traffic detection equipment is established. Secondly, UAVs are employed to cruise sparse road for incident detection, then a dynamic multi-objective optimization model of planning UAV routes is proposed with the consideration of different UAV cruising scenarios and multi-objective optimizations, then the corresponding algorithm for solving UAV route optimization is given, which analyzes the impact of UAV detection demand, multi-objective optimization, algorithm parameters on the optimization effect. Thirdly, a multi-scenario numerical simulation method is proposed to the UAV incident detection effect, and the cost-benefit evaluation of using UAV to detect traffic incident is conducted based on field experiment. The research results can provide a scientific basis for the layout planning of traffic detection equipment, which has important practical value to improve sparse road traffic surveillance level.