A Space-time Network Based Modeling Framework for Dynamic Unmanned
Aerial Vehicle Routing in Traffic Incident Monitoring Applications
报告人：周学松副教授
时 间：5月18日（周一）下午2点
地 点：千佛山校区beat365正版唯一网址楼三层报告厅
报告人简介：
周学松博士，目前任职于美国亚利桑那州立大学副教授。担任IEEE 智能交通系统协会，交通和出行管理技术委员会的联合主席，TRB 委员会之交通网络建模（ADB30）网络平衡分会主席, 美国运筹与管理研究协会铁路应用分会副主席，国际期刊Transportation Research Part C: Emerging Technologies(运输类国际期刊排名第二)副主编，Transportation Research Part B: Methodological(运输类国际期刊排名第一)编委。研究方向包括动态交通系统的建模和模拟，利用高级感测技术估计和预测网络交通状况，以及多种模式交通系统的路径选择和协作式决策制定。曾在美国加州硅谷的Dash Navigation导航公司担任交通数据系统设计师兼高级软件工程师，设计并完成了实时交通估计和预测算法，开发了在美国第一个商业化的基于因特网的GPS导航系统。拥有“Key2SafeDriving”发明专利，获得第15届IEEE ITS大会最佳论文奖。大规模基于模拟的动态交通分配系统（DYNASMART-P）的研究成为美国联邦公路局的24项优先科技和发明之一，并独立开发软件DTALite（目前唯一可以处理2000万辆以上车辆的交通模拟软件）。
报告内容：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, non-recurring traffic conditions and special events on transportation networks. This report presents a space-time network based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and cost-effective road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed and altitude restriction 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.
