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（或沿着道路的交叉口）的承载能力时发生的情况。它的特点是速度较慢，行程时间较长，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，提高其运行性能至关重要，因为这可以显著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。由于车辆对车辆（V2 V）和车辆对基础设施（V2 I）通信技术的进步，通过持续监测交通信息和使车辆驾驶员能够感知情况，可以提高安全性和道路交叉口效率。在这个项目中，与Fortran交通系统有限公司合作，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V2 I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。该研究的预期成果有两个方面：（一）提高道路交叉口的安全性和效率;（二）采用数据驱动的方法评估道路交叉口的运营绩效。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 thecapacity 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. Roadintersections represent one of the most complex configurations encountered when traversing road networks anda high percentage of accidents occur at these locations. It is therefore of vital importance to improve theiroperational performance, as that can significantly contribute towards the efficiency of the whole transportnetwork. Traditional approaches to improve the efficiency of intersections are based on analysis of static dataor expert opinions. However, today's vehicles are no longer stand-alone transportation means. Due to theadvancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies itis possible to enhance safety and improve road intersection efficiency by continuously monitoring trafficinformation and enabling situational awareness of vehicle drivers.In this project, in collaboration with Fortran Traffic Systems Limited, we aim to leverage big data mining andmachine learning techniques to continuously monitor the operational performance of road intersections throughmining real-time V2I data. A key to the success of these methods is the quality and timeliness of the analysisprovided. The anticipated outcome of the research is twofold: (i) an increased safety and efficiency of roadintersections, 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 theintersection of big data analytics and machine learning has the potential to attract the brightest students fromaround the world, while keeping domestic talent here.