A Multiscale Framework for Forecasting Highway Traffic Flow

交通堵塞是日常生活中令人烦恼的一件事。它们还阻碍了我们的经济：英国工业联合会估计，由于道路交通拥堵造成的延误每年给英国企业造成高达200亿英镑的损失。英国的道路交通量预计在2000-2015年期间将增长30%，因此拥堵问题似乎只会变得更糟。因此，国际上都在努力利用信息和通信技术来管理交通，以减轻拥挤-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ITS了。示例包括1. M25伦敦轨道上的受控高速公路项目（在交通繁忙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（在忙碌时段，路肩变成普通的行车道）;以及3.现在在英国高速公路网上几乎无处不在的“前方排队”警告标志。对远程信息处理基础设施的投资非常巨大--仅主动交通管理就约为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它们的基础科学来改进模型。这是一个所谓的多尺度挑战，因为有一个完整的层次结构的模型的不同层次的细节，从模拟模型的行为，个别司机，宏观模型之间的交通流和可压缩气体的类比。这项研究将建立方法，找出哪些模型是好的，哪些是坏的。此外，它还将利用现代“机器学习”技术，将联合收割机的良好模式结合起来，使计算机交通预测具有类似人类的人工智能。

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.