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: 544429-2019
• 负责人: Papagelis, Manos
• 金额: $ 0.52万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=674584
• 关键词: Evaluating; Operational; Performance; Road; Intersections

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.

交通拥堵是指当交通需求接近道路(或道路交叉口)的通行能力时，交通网络上出现的一种情况。它的特点是速度较慢，出行时间较长，车辆排队增加，并与巨大的社会、经济和环境成本相关。道路交叉口是穿越公路网时遇到的最复杂的布局之一，很高比例的事故发生在这些位置。因此，提高它们的运营性能至关重要，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对静态数据或专家意见的分析。然而，今天的车辆不再是独立的交通工具。由于车辆到车辆(V2V)和车辆到基础设施(V2I)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了解情况，可以增强道路交叉口的安全性和提高道路交叉口的效率。在这个项目中，我们与Fortran交通系统有限公司合作，旨在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。这些方法成功的一个关键是所提供分析的质量和及时性。这项研究的预期结果有两个：(I)提高了道路交叉口的安全性和效率，以及(Ii)以数据为导向的方法来评估道路交叉口的运行性能。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。