A Multiscale Framework for Forecasting Highway Traffic Flow

预测公路交通流量的多尺度框架

• 批准号: EP/E055567/2
• 负责人: Richard Wilson
• 金额: $ 35.2万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE055567%2F2
• 关键词: Multiscale; Framework; Forecasting; Highway; Traffic

Traffic jams are an annoying feature of everyday life. They also hamper our economy: the CBI has estimated that delays due to road traffic congestion cost UK businesses up to 20 billion annually. UK road traffic is forecast to grow by 30% in the period 2000-2015, so it seems that the congestion problem can only get worse. There is consequently an intense international effort in using Information and Communication Technologies to manage traffic in order to alleviate congestion --- this broad area is known as Intelligent Transport Systems (ITS). Regular motorway drivers will already be familiar with ITS. Examples include 1. the Controlled Motorways project on the M25 London Orbital (which sets temporary reduced speed limits when the traffic gets heavy); 2. Active Traffic Management on Birmingham's M42 (where the hard-shoulder becomes an ordinary running lane in busy periods); and 3. The `Queue Ahead'warning signs which are now almost ubiquitous on the English motorway network. The investment in this telematics infrastructure has been very significant --- about 100 million pounds for Active Traffic Management alone.Each of the ITS applications described above has at its heart detailed mathematical and computer models that forecast how traffic flows and how queues build up and dissipate. However, these models are far from perfect, and the purpose of this research is to improve the models by working on the fundamental science that underpins them. This a so-called multiscale challenge, since there is a whole hierarchy of models of different levels of detail, ranging from simulation models that model the behaviour of individual drivers, up to macroscopic models that draw an analogy between the flow of traffic and compressible gas. This research will establish methods for finding out which models are good and which ones are bad. Moreover, it will use modern `machine learning' techniques to combine good models so that computer-based traffic forecasting has human-like artificial intelligence.

交通堵塞是日常生活中令人讨厌的一个特征。它们还阻碍了我们的经济：CBI估计，由于道路交通拥堵导致的延误，每年给英国企业造成的损失高达200亿英镑。英国道路交通量预计在2000-2015年间将增长30%，因此拥堵问题似乎只会变得更糟。因此，国际上正在加紧努力，使用信息和通信技术来管理交通，以缓解拥堵-这一广泛的领域被称为智能交通系统(ITS)。普通的高速公路司机已经熟悉了ITS。例子包括1.伦敦轨道M25上的受控高速公路项目(当交通变得繁忙时，该项目设置临时降低的速度限制)；2.伯明翰M42上的主动交通管理(在繁忙时期，硬路肩变成普通行车道)；3.现在英国高速公路网上几乎随处可见的“排队在前”警告标志。在这种远程信息处理基础设施上的投资非常可观-仅主动交通管理一项就投入了约1亿英镑。上述每一种智能交通系统应用程序的核心都有详细的数学和计算机模型，预测交通如何流动以及如何建立和消除排队。然而，这些模型远远不是完美的，本研究的目的是通过研究支撑它们的基础科学来改进这些模型。这是一个所谓的多尺度挑战，因为有一整套不同细节级别的模型，从模拟个别司机行为的模拟模型，到将交通流量与可压缩气体进行类比的宏观模型。这项研究将建立方法来找出哪些模型是好的，哪些是不好的。此外，它还将使用现代“机器学习”技术将好的模型组合在一起，使基于计算机的交通预测具有类似人类的人工智能。