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Projects: Projects for Investigator
Reference Number EP/J002186/1
Title Advanced traffic flow theory and control for heterogeneous intelligent traffic networks
Status Completed
Energy Categories Energy Efficiency(Transport) 5%;
Not Energy Related 95%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields SOCIAL SCIENCES (Town and Country Planning) 65%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 10%;
UKERC Cross Cutting Characterisation Not Cross-cutting 80%;
Systems Analysis related to energy R&D (Other Systems Analysis) 10%;
Sociological economical and environmental impact of energy (Consumer attitudes and behaviour) 10%;
Principal Investigator Dr D Ngoduy
No email address given
Institute for Transport Studies
University of Leeds
Award Type Standard
Funding Source EPSRC
Start Date 08 September 2011
End Date 07 September 2016
Duration 60 months
Total Grant Value £480,598
Industrial Sectors Transport Systems and Vehicles
Region Yorkshire & Humberside
Programme NC : Engineering
Investigators Principal Investigator Dr D Ngoduy , Institute for Transport Studies, University of Leeds (100.000%)
  Industrial Collaborator Project Contact , Technical University of Delft, The Netherlands (0.000%)
Project Contact , Hong Kong Polytechnic University, China (0.000%)
Project Contact , Queensland University of Technology, Australia (0.000%)
Project Contact , University of Hong Kong (HKU), China (0.000%)
Web Site
Abstract 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 multiplesources (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
Publications (none)
Final Report (none)
Added to Database 28/11/11