Data-driven Traffic Analytics for Incident Analysis and Management

用于事件分析和管理的数据驱动的流量分析

• 批准号: LP180100114
• 负责人: Prof Kai Qin
• 金额: $ 54.99万
• 依托单位: Swinburne University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-02-28 至 2024-02-28
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP180100114
• 关键词: Data; driven; Traffic; Analytics; Incident

Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.

交通事故是世界各地所有交通管理机构的主要关注点之一，因为它们在交通拥堵和延误、空气和噪音污染以及管理成本方面具有重大影响。该项目旨在通过结合交通工程和数据科学的多学科研究工作，解决复杂和多模式交通网络中的事件分析和管理问题。预期的成果将是一个创新的事件分析和管理框架，协同交通数据分析和交通模拟建模及其关键使能技术和原型系统。这将大大有助于减轻事故对日常通勤者的影响。