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周学松副教授学术报告
来源:控制学院   发布日期:2015/5/11

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

  间:518日(周一)下午2

  点:千佛山校区控制科学与工程学院楼三层报告厅

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15IEEE 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.