Details of Grant 

EPSRC Reference:	EP/J002186/1
Title:	Advanced traffic flow theory and control for heterogeneous intelligent traffic networks
Principal Investigator:	Ngoduy, Professor D
Other Investigators:
Researcher Co-Investigators:
Project Partners:	Delft University of Technology Hong Kong Polytechnic University Queensland University of Technology The University of Hong Kong
Department:	Institute for Transport Studies
Organisation:	University of Leeds
Scheme:	Career Acceleration Fellowship
Starts:	08 September 2011	Ends:	07 September 2016	Value (£):	480,598
EPSRC Research Topic Classifications:	Transport Ops & Management
EPSRC Industrial Sector Classifications:	Transport Systems and Vehicles
Related Grants:
Panel History:	Panel Date Panel Name Outcome 21 Jun 2011 Fellowships 2011 Interviews - Panel D PES Announced
Summary on Grant Application Form
This fellowship will develop a new generation of a real-time model based control framework required for engineers to manage and control the real-time operations of a heterogeneous intelligent traffic system through Active Traffic Management (ATM) programs. In general, an ATM program, also known as managed lanes or smart lanes, is a scheme for improving traffic flow and reducing congestion on motorways. It makes use of automatic systems and human intervention to manage traffic flow and ensure the safety of road users. Information and communication technologies (ICT) have transformed many aspects of business, society and government, from healthcare to education and the economy. ICT are now in the early stages of transforming transportation systems by integrating sensors (remote sensing and positioning), control units (traffic signals, message signs) and automatic technologies with microchips to enable them to communicate with each other through wireless technologies. In many developed countries, particularly Japan and South Korea, the deployment of ICT in ATM programs has led to significant improvement of traffic network performance such as reduced congestion, increased traffic safety, enhanced environmental quality (e.g. reduced CO2) and a more reliable service to the road user. It is expected that in the coming 5 to 10 years ICT will considerably progress worldwide so that intelligent equipped vehicles, in which the driving tasks are shifted from the driver to the vehicle through autonomous vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, will make up a significant share of the traffic flow. In V2V communication, the leading equipped vehicle will issue information of its current speed, driving manoeuvre (e.g. acceleration or deceleration), etc. to further upstream vehicles while in V2I communication, the equipped vehicle will exchange information with roadside intelligent devices and receive commands from such devices for consequent driving activities. A considerable proportion of intelligent vehicles in traffic flow will create intelligent traffic networks containing a mixed composition of non-equipped (or manual) and equipped vehicles. Such traffic flow system is defined as a heterogeneous intelligent traffic system. This proposal will seek solutions for an improved ATM program to monitor and control more efficiently intelligent traffic networks. In principle, the traffic control problem for heterogeneous intelligent traffic networks is highly complex, which is characterized by the interactions between non-equipped vehicles and various types of equipped vehicles and by the interaction between equipped vehicles and the roadside intelligent devices, as well as by the interplay between different control strategies for different types of vehicles. The proposed research will tackle such complex issues and bring in a new real-time model-based intelligent traffic control framework using real-life data collected from multiple sources (loop detectors, remote sensing, mobile phones, floating cars, etc. ). The new model will predict in the short term the traffic congestion patterns (i.e. the transitions between free-flow, congestion or stop-and-go jams) and investigate the true causes of such congestion which occurs in a heterogeneous intelligent traffic network. Based on the traffic states predicted from the real-time model, a sequence of immediate control actions will be established for different types of vehicles (equipped and non-equipped) in order to reduce congestion, travel time and air pollution.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.leeds.ac.uk