Highway Network Digital Twin for Traffic Management

用于交通管理的公路网络数字孪生

• 批准号: 96507
• 金额: $ 8.6万
• 依托单位: MOTT MACDONALD LIMITED
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=96507
• 关键词: Highway; Network; Digital; Twin; Traffic

Public descriptionInefficiencies in road networks reduce capacity and increase journey times for users and goods. Road capacity is not used evenly over space or time: usage peaks during morning and evening rush hours lead to inefficiencies; incidents reduce capacities unexpectedly. The ability to forecast traffic patterns in real-time, linked to traffic control systems, can improve network efficiency.Computer-based traffic models that calculate routes that individuals take allow us to replicate real-world conditions across a traffic network for a single point in time. However, these models are not well-suited to responding to unforeseen incidents, which require rapid operational decision-making to mitigate their impacts.Traffic conditions at specific points on the network are increasingly being captured by roadside sensors that monitor traffic volumes, vehicle emissions and other information in real-time. These data are very valuable, and many are routinely used for traffic management; however, they tell us only about conditions at specific points in space and time, and not conditions across the entire network.Techniques for processing and identifying patterns in such data, using Artificial Intelligence techniques like Machine Learning are improving, and offer opportunities to apply these data in ever more complex real-time applications.Traffic management can be significantly improved if there is a reliable, accurate representation of the whole of the existing road network and the real-world conditions it is operating under -- a 'digital twin'. Such a digital twin becomes a powerful forecasting tool when the routes that individuals take (or should take) are accounted for.Our proposal is to build a real-time digital twin based on existing traffic models, this growing array of roadside sensors, and state-of-the-art Machine Learning techniques in order to be able to model traffic conditions on networks in real-time.The digital twin will allow traffic to be actively managed, increasing road capacity and facilitating smoother traffic flow, particularly during "rush hour" and during unanticipated incidents. A digital twin could, for example, be used to predict the effects on traffic flow (and therefore capacity) of changing signs or opening/closing lanes before implementing such measures. In order to develop and operate a real-time digital twin to manage and increase road capacity, we must first demonstrate that we can automatically integrate real-time sensor data, a road network representation and traffic model. We will assess the feasibility of this level of integration along with the practicality of running a real-time solution in terms of calculation speed.

公共描述道路网络的低效率降低了通行能力，增加了用户和货物的旅行时间。道路容量在空间或时间上的使用并不均匀：早高峰和晚高峰时间的使用高峰导致效率低下；事故意外地减少了通行能力。与交通控制系统相关联的实时预测交通模式的能力可以提高网络效率。基于计算机的交通模型计算个人所走的路线，使我们能够在单个时间点复制整个交通网络的真实情况。然而，这些模型不能很好地应对突发事件，这些突发事件需要快速的运营决策来减轻其影响。越来越多的路边传感器实时监控交通量、车辆排放和其他信息，以捕获网络上特定点处的交通状况。这些数据非常有价值，其中许多经常用于交通管理；然而，它们只告诉我们特定时空点的情况，而不是整个网络的情况。使用机器学习等人工智能技术处理和识别此类数据中的模式的技术正在改进，并提供了将这些数据应用于更复杂的实时应用的机会。如果能够可靠、准确地表示整个现有道路网络及其运行的真实世界条件，交通管理将得到显著改善。当考虑到个人所走(或应该走)的路线时，这样的数字孪生兄弟成为一种强大的预测工具。我们的建议是基于现有的交通模型、越来越多的路边传感器和最先进的机器学习技术来构建实时数字孪生兄弟，以便能够实时地对网络上的交通状况进行建模。数字孪生兄弟将允许交通被主动管理，增加道路容量并促进更顺畅的交通流量，特别是在高峰时间和意外事件期间。例如，在实施这些措施之前，可以使用数字孪生系统来预测改变标志或开放/关闭车道对交通流量(从而对通行能力)的影响。为了开发和运行实时数字孪生系统来管理和增加道路通行能力，我们必须首先证明我们可以自动集成实时传感器数据、道路网络表示和交通模型。我们将评估这种集成级别的可行性，以及在计算速度方面运行实时解决方案的可行性。