The project is led by Dr Simon Hu to develop methods of real-time traffic and air quality management and decision support systems for urban traffic operation. We propose an urban traffic decision support system which will rely on statistical learning and cloud computing to process massive, multi-source, and heterogeneous traffic and pollutant data. Such system will be able to generate nonlinear but computationally efficient real-time relationships between multiresolution urban road traffic states and environmental measures of effectiveness to support proactive traffic control (e.g. strategies for urban intersections, expressways, and bus lanes). It will use a combination of offline simulation and online decision support tool (DST) to integrate these data and yield timely, robust,and globally optimal solutions that minimize traffic congestion, emission, and fuel consumption in an uncertain environment.
The proposed systems are needed because existing ones have limited capability to utilize the aforementioned ‘big data’ sources, nor produce fast decisions that are globally optimal, and address multiple objectives simultaneously. This DST based on a comprehensive traffic and environmental monitoring targets largescale and multi-modal traffic in cities in the UK and China, while addressing the pressing issues of saturated traffic and environmental deterioration now facing most cities in the world.
This project will produce data management paradigms and decision support tools that go beyond state-of-the-art. The underlying methodology is transferable to other transport scenarios such as public transport and aviation, and potentially to topics beyond transport such as economics and supply chain. The project is likely to stimulate further collaboration between and beyond the partner institutions.