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

通过挖掘轨迹数据流评估道路交叉口的运营绩效

• 批准号: 530694-2018
• 负责人: Papagelis, Manos
• 金额: $ 1.82万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661486
• 关键词: Evaluating; Operational; Performance; Road; Intersections

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.

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（V2V）和车对基础设施（V2I）通信技术的进步，通过持续监控交通信息并使车辆驾驶员能够感知情况，可以提高安全性并提高道路交叉口的效率。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。