Adaptive Decentralized Traffic Forecasting for Intelligent Transportation

智能交通的自适应分散交通预测

• 批准号: RGPIN-2019-05881
• 负责人: Thulasiraman, Parimala
• 金额: $ 2.04万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738563
• 关键词: Adaptive; Decentralized; Traffic; Forecasting; Intelligent

Autonomous vehicles will be a dominating technology in the future. Driver assistive technologies have evolved substantially over recent years. It will not be long before automated technology in cars replace human drivers. Self-driving vehicles will provide tremendous independence to people with disabilities, non drivers and senior citizens. Statistics Canada's 2016 census reported that for the first time since confederation, seniors outnumbered children in Canada. As the adult population increases, the number of vehicles will also tend to increase. In the future, both self driving and non-self driving vehicles with co-exist. Traffic congestion increases air pollution and travel driving time causing driver frustration, stress and in some cases road rage. My research program envisions that traffic congestion will be one of the major challenges for the transportation industry in urban areas and my program will take proactive steps to address this and other transportation issues through Intelligent Transportation Systems (ITS). ITS will exploit state-of-the art technology including motion sensors, increased available computation power and innovative communication protocols to inform drivers on the move about traffic and road conditions. ITS will include road network (static),  vehicle-to-vehicle technology in which vehicles communicate with nearby vehicles using wireless connectivity and vehicle-to-infrastructure technology where vehicles communicate with networked road-side devices. This type of communication creates a highly decentralized, dynamic, ad hoc, mobile network called a vehicular ad hoc network (VANET). A major objective will be to develop a traffic aware routing algorithm that will consider both the road network and dynamic VANET data. Assume you are driving on a street and there is an accident somewhere towards the end of the street. The speed of the vehicles on the street will decrease. This, of course, affects other vehicles on the street and creates a traffic jam. The roads surrounding the congested street will obviously also be affected. What is interesting and not yet considered in predicting traffic is that over a period of time, other roads not necessarily close in proximity to the congested road may also get affected. I proposed to consider all the roads that are influenced by the congested road and cluster them together? We can train these clusters using machine learning techniques to find the patterns and relationship between them using real time data provided by road-side devices. The trained clusters can then be used to predict traffic any time evolving to reflect current conditions. The clusters will be made available to VANET vehicles to predict traffic and re-route if necessary. My research program will train HQP in cores areas of computer science and engineering that will prepare them for in-demand careers in science. In this cycle of my Discovery Grant, I expect to train 2 UG, 3 MSc and 3 PhD.

自动驾驶汽车将成为未来的主导技术。近年来，驾驶辅助技术有了很大的发展。用不了多久，汽车中的自动化技术就会取代人类驾驶员。自动驾驶汽车将为残疾人、非驾驶员和老年人提供巨大的独立性。加拿大统计局2016年人口普查报告称，自联邦成立以来，加拿大老年人人数首次超过儿童。随着成年人口的增加，车辆的数量也将趋于增加。在未来，自动驾驶和非自动驾驶汽车将共存。交通拥堵增加了空气污染和旅行驾驶时间，导致司机沮丧，压力和在某些情况下的道路愤怒。 我的研究计划设想，交通拥堵将是城市地区交通行业的主要挑战之一，我的计划将采取积极措施，通过智能交通系统（ITS）解决这一问题和其他交通问题。ITS将利用最先进的技术，包括运动传感器，增加可用的计算能力和创新的通信协议，以通知司机关于交通和道路状况的移动。智能交通系统将包括道路网络（静态）、车对车技术（其中车辆使用无线连接与附近车辆通信）和车对基础设施技术（其中车辆与联网的路边设备通信）。这种类型的通信创建了一个高度分散的、动态的、自组织的、移动的网络，称为车载自组织网络（VANET）。 一个主要目标将是开发一个交通感知路由算法，将考虑道路网络和动态VANET数据。假设您正在街道上行驶，并且街道尽头的某个地方发生了事故。街道上的车辆速度会降低。这当然会影响街上的其他车辆，造成交通堵塞。这条拥挤的街道周围的道路显然也会受到影响。有趣的是，在预测交通时尚未考虑的是，在一段时间内，其他不一定靠近拥挤道路的道路也可能受到影响。我提议考虑所有受拥堵道路影响的道路，并将它们集中在一起？我们可以使用机器学习技术来训练这些集群，以使用路边设备提供的真实的时间数据来找到它们之间的模式和关系。然后，经过训练的集群可以用于预测任何时间的流量，以反映当前的状况。这些集群将提供给VANET车辆，以预测交通状况，并在必要时改变路线。 我的研究项目将在计算机科学和工程的核心领域培训HQP，这将为他们在科学领域的需求职业做好准备。在我的发现资助的这个周期中，我希望培养2个UG，3个MSc和3个PhD。