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Real-time Analytics on Urban Trajectory Data for Road Traffic Management (2019-2024)

Abstract

This project aims to develop advanced data management and predictive analytics capabilities to enable road operators and traffic managers to apply insights from multi-modal mobility data to inform decision making in transport network management. Traditional traffic data collected from fixed sensors provide a limited view of network traffic and are unable to capture how a small disruption in traffic movements may ripple through the whole network. This project will demonstrate how transport operators can leverage emerging urban trajectory data from mobile sensors to obtain a holistic multi-modal view of transport networks, better understand the network-wide impacts of their decisions, and, thus, enable city-wide optimization of network flows.

Experts

Associate Professor Jiwon Kim

Associate Professor
School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
Jiwon Kim
Jiwon Kim

Dr Mehmet Yildirimoglu

ARC DECRA
School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
Mehmet Yildirimoglu
Mehmet Yildirimoglu

Professor Mark Hickman

Affiliate of Dow Centre for Sustainable Engineering Innovation
Dow Centre for Sustainable Engineering Innovation
Faculty of Engineering, Architecture and Information Technology
Deputy Head of School of Civil Engineering
School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
Professor & Chair of Transport Eng
School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
Mark Hickman
Mark Hickman