Zhang Jisheng, Jia Limin, Niu Shuyun, Zhang Fan, Tong Lu, Zhou Xuesong
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Rd., Haidian District, Beijing 100088, China.
Sensors (Basel). 2015 Jun 12;15(6):13874-98. doi: 10.3390/s150613874.
It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.
对于交通管理中心而言，配备并管理一个由固定和移动传感器组成的网络至关重要，以便快速检测交通事件并进一步监测相关影响区域，特别是对于具有严重交通拥堵传播的高影响事故。随着新兴的小型无人机开始拥有更加灵活的监管环境，充分探索使用无人机监测交通网络上的经常性和非经常性交通状况以及特殊事件的潜力至关重要。本文提出了一种基于时空网络的集成固定和移动传感器网络建模框架，以提供一种快速且系统的道路交通监测机制。通过构建离散化的时空网络来不仅表征无人机的速度，还表征交通拥堵的时间敏感影响区域，我们将该问题表述为线性整数规划模型，以在可行飞行路线约束下最小化检测延迟成本和运营成本。开发了拉格朗日松弛求解框架，将原始复杂问题分解为一系列计算效率高的时间相关和最小成本路径查找子问题。使用几个示例来展示所提出模型在中小型网络无人机路线规划中的结果。
