Advanced traffic flow theory and control for heterogeneous intelligent traffic networks

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.

1. Who will benefit from the research
a) The transport practitioners and government agencies.
b) The road users in the long term as pollution and vehicle emission due to traffic congestions or stop-start traffic patterns will be reduced through a better ATM program, hence, the health of individuals, quality of life will be enhanced.
c) The social science research community.
2. How might they benefit?
a) The proposed research will provide the transport practitioners and government agencies with a new traffic simulation tool for the design and evaluation of new traffic management schemes. The new theoretical and empirical results from this research will directly contribute towards the improvement of current ATM programs or model-based dynamic traffic management systems to better monitor and control the dynamics of intelligent traffic flow, which will considerably operate in the near future. These practical applications will be integrated into the current research projects in Delft for comparison, and facilitated in the current incident traffic management program in Hong Kong and in the on-line simulation platform for the dynamic traffic management of a large scale traffic network of Brisbane Metropolitan area through the research partners (see letters of support). Moreover, the experimental results of this research will be shared with software industry for the further improvement of the current microscopic simulation tool to enable more realistic (microscopic) operations of intelligent traffic systems.
b) The calibration and validation methodologies proposed in this proposal will contribute towards a benchmarking procedure for both on-line and off-life calibration of simulation tools. The computer coded calibration and validation procedures developed in this proposal will be made available for free for any commercial consultants working with traffic simulation models (for example, AIMSUM, PARAMICS and VISSIM). The developed calibration tool will provide such consultants with better traffic simulation results from their own simulation tools so that more accurate design and evaluation of traffic schemes will be achieved.
c) Our approach to methodological synthesis and the adaptive use of real-time data from multiple sources challenges both disciplinary and practice boundaries. It offers new lines of communication between engineering, mathematics and social science community working in the fields of drivers' psychological research through the exchange of knowledge in understanding the psychology of drivers in an integrated intelligent traffic environment.