Artificial Intelligence for Autonomic Urban Traffic Control

用于自主城市交通控制的人工智能

• 批准号: MR/T041196/1
• 负责人: Mauro Vallati
• 金额: $ 138.53万
• 依托单位: University of Huddersfield
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT041196%2F1
• 关键词: Artificial; Intelligence; Autonomic; Urban; Traffic

Over half of the world's population now lives in cities, and global urbanisation continues at a steady pace. As this trend continues, mobility is becoming an increasingly critical problem. In the UK alone, the cost of congestion reached nearly £8 billion in 2018 in lost time and fuel consumption, and has become a major health threat.At the same time, new modes of transport, such as Connected Autonomous Vehicles, and new business models (e.g., Mobility as a Service) are disrupting the transportation sector. The traffic control industry has to reinvent itself to operate in a world of decentralised control (vehicles making decisions), ubiquitous sensor information, increasing urbanisation pressure, and large-scale interconnectivity.Artificial Intelligence provides a range of approaches that can leverage the growing volume of available data, and the knowledge gained by traffic authorities in the past decades, to support urban mobility. In particular, the proposed line of research aims at designing and creating an autonomic urban traffic management and control framework. The autonomic framework will have the capability to self-manage and self-configure, and will have an holistic view of the condition of the controlled region to proactively act to prevent environmental and mobility issues, or react to mitigate observed problems.

目前，世界上一半以上的人口居住在城市，全球城市化继续稳步发展。随着这一趋势的继续，流动性正成为一个日益严重的问题。仅在英国，2018年拥堵损失的时间和燃料消耗就达到近80亿英镑，并已成为主要的健康威胁。与此同时，新的交通模式，如互联自动驾驶汽车，和新的商业模式（例如，移动即服务（Mobility as a Service）正在扰乱交通部门。交通控制行业必须重塑自身，才能在分散控制（车辆决策）、无处不在的传感器信息、不断增加的城市化压力和大规模互联互通的世界中运营。人工智能提供了一系列方法，可以利用不断增长的可用数据量和交通当局在过去几十年中获得的知识来支持城市移动性。特别是，拟议的研究路线旨在设计和创建一个自主的城市交通管理和控制框架。自主框架将具有自我管理和自我配置的能力，并将全面了解受控区域的状况，以主动采取行动来防止环境和移动性问题，或做出反应以缓解观察到的问题。

项目成果

Automated Planning for Generating and Simulating Traffic Signal Strategies

生成和模拟交通信号策略的自动规划

• DOI: 10.24963/ijcai.2023/830
• 发表时间: 2023
• 作者: Bhatnagar S

On-the-Fly Knowledge Acquisition for Automated Planning Applications: Challenges and Lessons Learnt

自动化规划应用程序的动态知识获取：挑战和经验教训

• DOI: 10.5220/0010857100003116
• 发表时间: 2022
• 作者: Bhatnagar S

Centralised Vehicle Routing for Optimising Urban Traffic: A Scalability Perspective

用于优化城市交通的集中式车辆路线：可扩展性角度

• DOI: 10.1109/iv55152.2023.10186707
• 发表时间: 2023
• 作者: Chrpa L

Logics in Artificial Intelligence - 18th European Conference, JELIA 2023, Dresden, Germany, September 20-22, 2023, Proceedings

人工智能中的逻辑 - 第 18 届欧洲会议，JELIA 2023，德国德累斯顿，2023 年 9 月 20-22 日，会议记录

• DOI: 10.1007/978-3-031-43619-2_16
• 发表时间: 2023
• 作者: Chrpa L

Knowledge Reformulation and Deception as a Defense Against Automated Cyber Adversaries

知识重组和欺骗作为防御自动化网络对手的手段

• DOI: 10.32473/flairs.v35i.130675
• 发表时间: 2022
• 期刊: The International FLAIRS Conference Proceedings
• 作者: Alford R