Congestion management and prediction
Objectives:
The research aims to reduce traffic congestion & improve the throughput of key roadways. It aims to use cutting
edge crowdsourcing, cloud and cyber-physicals ystems as well as wireless sensor network technologies & data
mining techniques to improve our commute to work time (when the roadways are most stressed). The
contribution of this work is to include accurate real-time traffic prediction using a hybrid exponential smoothing
and neural-nets-based approaches and traffic flows forecasting using swarm intelligence. Among the work in this
area also include Congestion issues, impact and global view, congestion management through collective intelligence
and crowd sourcing, Logistics issues in InfrastructureStrategies, Planning and Policy, Intelligent Logistics Infrastructure
Design and Development, Intelligent strategy for planning and design (including ports, Air orSea), Intelligent strategy
for planning and design of highway, overpass, road, rail and freight corridors. The research also includes urban and
regional development model through data mining for the specific characteristics of road user background and
travelling behaviour and enablement of the study on road user demographic background and how this has affected
the local industry and urban regional business development, including the prediction of population growth, and its
impact on localbu siness and services in 5, 10 or 25 years’ time.
Description of Work:
This project involves design and development of algorithms, knowledge, information analysis techniques, good
mathematical knowledge as well as possible numerical modelling.