Evaluating the Operational Performance of Road Intersections by Mining Trajectory Data Streams

交通拥堵描述的是交通网络中当需求接近道路（或道路沿线沿着）的通行能力时发生的情况。它的特点是速度较慢，行程时间较长，** 和车辆排队增加，并与重大的社会，经济和环境成本有关。道路 ** 交叉口是穿越道路网络时遇到的最复杂的配置之一，** 高比例的事故发生在这些位置。因此，改善其运营绩效至关重要，因为这可以大大提高整个运输网络的效率。传统的提高交叉口效率的方法是基于静态数据分析 ** 或专家意见。然而，今天的车辆不再是独立的运输工具。由于 ** 车对车（V2 V）和车对基础设施（V2 I）通信技术的进步，** 可以通过持续监控交通 ** 信息并实现车辆驾驶员的态势感知来增强安全性并提高道路交叉口效率。**在这个项目中，我们的目标是利用大数据挖掘和机器学习技术，通过挖掘实时V2I数据来持续监控道路交叉口的运营性能。* * 这些方法成功的关键是所提供分析的质量和及时性。该研究的预期成果是 ** 双重的：（i）提高道路交叉口的安全性和效率，以及（ii）数据驱动的方法来评估 ** 道路交叉口的运营性能。拟议的研究合作符合加拿大的创新议程。在大数据分析和机器学习的交叉领域进行研究，有可能吸引世界各地最聪明的学生，同时留住国内人才。

Traffic congestion describes a situation on transport networks that occurs when demand approaches the**capacity of a road (or of the intersections along the road). It is characterized by slower speeds, longer trip times,**and increased vehicle queues and is associated to significant social, economic and environmental costs. Road**intersections represent one of the most complex configurations encountered when traversing road networks and**a high percentage of accidents occur at these locations. It is therefore of vital importance to improve their**operational performance, as that can significantly contribute towards the efficiency of the whole transport**network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data**or expert opinions. However, today's vehicles are no longer stand-alone transportation means. Due to the**advancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies it**is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic**information and enabling situational awareness of vehicle drivers.**In this project, we aim to leverage big data mining and machine learning techniques to continuously monitor**the operational performance of road intersections through mining real-time V2I data. A key to the success of**these methods is the quality and timeliness of the analysis provided. The anticipated outcome of the research is**twofold: (i) an increased safety and efficiency of road intersections, and (ii) a data-driven approach to evaluate**road intersection operational performance.**The proposed research collaboration aligns with Canada's Innovation Agenda. Conducting research in the**intersection of big data analytics and machine learning has the potential to attract the brightest students from**around the world, while keeping domestic talent here.