Powering Urban Smart Mobility with Data Analytics (PUBLIC)

Abstract

The rapid urbanisation and wide adoption of motor vehicles in Guangdong, China has increased traffic and resulted in congestions, loss of productivity, and negative effects to the environment. The project aims to respond to these challenges by using big data analytics to characterise and predict the spatial-temporal (ST) traveller mobility and traffic patterns, to develop data analytics platform and applications to enhance smart mobility (SM), e.g., public transportation scheduling, and seamless connection between public transportation and shared bikes.

The improved efficiency/ optimised scheduling of public transports, using SM solutions, will benefit the working class who rely on such transports by reducing their cost on travel and trip time. This project will also provide environmental benefits such as reducing congestions and therefore CO2 emission, resulting in better air quality and improving health of the residents in large cities.

Lead Participant

Project Cost

Grant Offer

Ranplan Wireless Network Design Ltd, Cambridge £458,543 £ 320,980
 

Participant

University of Sheffield, United Kingdom £27,401 £ 27,401

Publications

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