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/E055567/1
• 负责人: Richard Wilson
• 金额: $ 76.8万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE055567%2F1
• 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烦恼的一个现象。它们还阻碍了我们的经济：英国工业联合会 (CBI) 估计，道路交通拥堵造成的延误每年给英国企业造成高达 200 亿美元的损失。预计2000年至2015年间英国道路交通量将增长30%，因此拥堵问题似乎只会变得更加严重。因此，国际上在利用信息和通信技术来管理交通以缓解拥堵方面做出了巨大的努力——这个广泛的领域被称为智能交通系统（ITS）。普通高速公路司机已经熟悉 ITS。例子包括 1. M25 伦敦轨道上的受控高速公路项目（当交通拥堵时设置临时降低的速度限制）； 2.伯明翰M42公路的主动交通管理（繁忙时段硬路肩变成普通车道）； 3. “前方排队”警告标志现在在英国高速公路网络上几乎随处可见。对该远程信息处理基础设施的投资非常巨大——仅用于主动交通管理就投入了约 1 亿英镑。上述每个 ITS 应用程序的核心都有详细的数学和计算机模型，可以预测交通流量以及队列如何建立和消散。然而，这些模型远非完美，这项研究的目的是通过研究支撑模型的基础科学来改进模型。这是所谓的多尺度挑战，因为存在不同细节级别的整个模型层次结构，从模拟单个驾驶员行为的模拟模型，到在交通流和可压缩气体之间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，哪些模型是坏的。此外，它将利用现代“机器学习”技术来结合良好的模型，使基于计算机的交通预测具有类似人类的人工智能。

项目成果

Criteria for convective versus absolute string instability in car-following models

• DOI: 10.1098/rspa.2010.0437
• 发表时间: 2011-08
• 期刊: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
• 作者: J. A. Ward;R. E. Wilson

Number of Lane Changes Determined by Splashover Effects in Loop Detector Counts

• DOI: 10.1109/tits.2012.2190403
• 发表时间: 2012-12-01
• 期刊: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
• 影响因子: 8.5
• 作者: Knoop, Victor L.;Wilson, R. Eddie;van Arem, Bart
• 通讯作者: van Arem, Bart

Techniques for the inference of mileage rates from MOT data

从 MOT 数据推断里程率的技术

• DOI: 10.1080/03081060.2012.745768
• 发表时间: 2013
• 期刊: Transportation Planning and Technology
• 影响因子: 1.6
• 作者: Wilson R

On the estimation of temporal mileage rates

关于时间里程率的估计

• DOI: 10.1016/j.tre.2013.06.004
• 发表时间: 2013
• 期刊: Logistics and Transportation Review
• 作者: Wilson R