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Smart Cities Case Study

    Case Study added by KTN on 29 Mar 2019

The fifth-generation mobile network, aka 5G, is set to revolutionise the way we communicate, collaborate and exchange information, as we continue to consume ever-increasing amounts of data and utilise more and more connected devices. It will enable us to work, play and move more efficiently, save us time and money, as well as access more services at higher capacity and increased connectivity.

5G roll-out in the UK will start this year, with EE, Vodafone, Three and O2 all confirming 5G launches by the end of 2019. But as 5G phones won’t hit the market until the end of the year, its effects won’t be felt until 2020, though experts predict that we may not see widespread 5G coverage in the UK until 2022 or later. In the meantime, UK companies are working away behind the scenes, forming collaborations, making connections and creating partnerships to facilitate the demands that5G technology will create and provide the services it will enable.

The project

One of those companies is Cambridge-based Ranplan Wireless, which was recently awarded a £320,000 grant from Innovate UK to build a data analytics platform that can predict and manage congestion in smart cities linking into 5G-powered technology.

Smart Cities - case study graphic

A collaboration between business and academic interests in the UK and Asia, Ranplan’s partners in this project include the University of Sheffield, Jinan University, East China University of Science and Technology and China Unicom.

Objectives

The project, Powering Urban Smart Mobility with Data Analytics, sets out four key objectives for its plan to develop a data-based platform and application to power urban social mobility:

  • Build deep learning (DL) models to characterise spatial-temporal (ST) patterns of travellers and vehicular traffic
  • Develop natural language processing (NLP) techniques to identify poor traveller experience in terms of location and time scale, and detect and classify urban events in real-time
  • Develop a data analytics platform that can be used to improve efficiency of transport systems to plan smart city infrastructure
  • Create a smartphone-based journey planner for urban travellers.

Leveraging recent advances in big data analytics and machine learning to predict the spatial-temporal distribution of traveller and traffic patterns will enable forecasting of traffic congestion in space and time, as well as public transportation and shared bike requirements. Data from mobile, Wi-Fi and social networks will be used to predict traveller mobility, traffic demands and provide relevant information to the government, police, transport operators and road network controllers to facilitate smart mobility in urban areas, including intelligent traffic light control, public transport scheduling, policing for large events, and seamless connection between different modes of public transports.

Overall it will improve the efficiency of public transport and bike-share programmes, reduce congestion and reduce travellers’ time and cost on travel, improving quality of life for citizens. It will help to keep smart cities moving smoothly, utilising 5G technology as it starts to power smart cities, keeping millions of machines from cars to traffic lights connected with minimal latency.

Background

Ranplan Wireless co-founder Professor Jie Zhang also holds the Chair in Wireless Systems at the Department of Electronic and Electrical Engineering at the University of Sheffield and has previously studied and worked at Imperial College London, Oxford University and the University of Bedfordshire. His research interests include 5G radio access technologies, data analytics for social and mobile networks and the modelling and design of smart environments.

Jie Zhang

He set up Ranplan to produce a suite of world leading design and optimisation tools for combined indoor and outdoor wireless networks, supporting 3G, 4G (LTE), 5G, Wi-Fi, IoT, and Public Safety. Ranplan Professional, Tablet and Collaboration Hub have already been used by the world’s largest telecom equipment manufacturers and mobile operators across the globe.

Technology and collaboration

Speaking about the technology that will be used in the project, Professor Zhang confirms that it will use machine learning and natural language processing techniques to analyse information. This platform will provide a framework that will allow for distributed storage and processing of big data.

The collaboration works through scheduled monthly meetings and progress monitoring every three months. Each group has a specialism to bring to the overall project. “For example, Ranplan’s focus is on developing a smart phone-based route planner for urban travellers,” Professor Zhang explains. “Meanwhile the team at the Sheffield University will focus on developing the data analytics methodology. Each team has been assigned specific tasks but works closely to ensure it all fits together. What’s great is that everyone is enthusiastic about this project and everyone brings their own expertise.” Sheffield is also one of the leading sites in the UK for natural language processing, machine learning and data analytics, with a lot of key projects taking place here, Professor Zhang confirms.

Challenges

“One of the most crucial challenges is to gather different data from heterogeneous sources, from social media, transport authorities to wifi and mobile networks and also how to link these datasets together and use them properly,” he says. Privacy laws around user identity are also a challenge to gathering data, as well as creating the algorithms to process it.

Project scope

The first and second objectives, building deep learning models to characterise spatial-temporal patterns of travellers and vehicles, and developing natural language processing techniques to classify events and experiences, will be mainly completed by the end of the first year. By the end of the second year the team will have created a big data analytics platform for smart mobility that can be used by transport authorities and city planners. The following years will see the project commercialised and exploited in different countries, Professor Zhang confirms.

The future

“Combining data analytics and machine learning techniques to analyse traveller experiences is one of key aims of the project,” says Professor Zhang. “Technologies developed in this project can be applied to our wider portfolio of network planning and optimisation solutions so that we can learn and understand user experiences with 5G applications. There are many technical challenges, but this is what makes it exciting and very interesting. Ultimately, we want to play a part in making the world a better place.”

Alastair Williamson, CEO Ranplan Group AB, adds: “Advanced data analytics, the Internet of Things (IoT) and ultra-fast and reliable 5G networks are the building blocks for tomorrow’s smart cities. Smart cities are all about people and 5G is a core enabling technology to empower society. The results of this research project will enable us to commercialise a new smart mobility solution to complement our existing in-building and outdoor network planning tools and offer cities a robust 5G and IoT planning tool.”

Words: Bernadette Fallo

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