Adaptive Decentralized Traffic Forecasting for Intelligent Transportation

自动驾驶汽车将是未来的主导技术。近年来，驾驶员辅助技术有了很大的发展。用不了多久，汽车上的自动化技术就会取代人类司机。自动驾驶汽车将为残疾人、非司机和老年人提供极大的独立性。加拿大统计局2016年的人口普查报告称，自联邦成立以来，加拿大老年人的数量首次超过了儿童。随着成年人口的增加，车辆的数量也将趋于增加。未来，自动驾驶和非自动驾驶车辆并存。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，在某些情况下还会导致道路愤怒。 我的研究计划设想，交通拥堵将是城市地区交通行业面临的主要挑战之一，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、增加的可用计算能力和创新的通信协议，向移动中的司机通知交通和路况。ITS将包括道路网络(静态)、车辆与附近车辆使用无线连接进行通信的车辆对车辆技术，以及车辆与联网的路边设备进行通信的车辆对基础设施技术。这种类型的通信创建了一个称为车载自组织网络(VANET)的高度分散、动态、自组织的移动网络。 一个主要的目标将是开发一种交通感知的路由算法，该算法将同时考虑道路网络和动态VANET数据。假设你正在一条街道上开车，在街道尽头的某个地方发生了一起事故。街道上的车辆速度将会降低。当然，这会影响街道上的其他车辆，并造成交通堵塞。拥堵街道周围的道路显然也会受到影响。在预测交通流量时，有趣而尚未考虑的是，在一段时间内，其他不一定靠近拥堵道路的道路也可能受到影响。我建议考虑所有受拥堵道路影响的道路，并将它们归类在一起？我们可以使用机器学习技术来训练这些簇，以使用路边设备提供的实时数据来发现它们之间的模式和关系。然后，训练后的簇可用于预测任何时间演变的交通，以反映当前状况。这些集群将供VANET车辆使用，以预测交通并在必要时改变路线。 我的研究计划将在计算机科学和工程的核心领域培训HQP，为他们在科学领域的需求职业生涯做好准备。在我的探索助学金的这个周期中，我预计将培训2名UG，3名硕士和3名博士。

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.