FREEFLOW

Lead Research Organisation: Imperial College London
Department Name: Civil & Environmental Engineering

Abstract

FREEFLOW aims to fundamentally change how we use transport data, by using it to generate transport intelligence. Currently we are collecting more (and better) data about our transport networks, such as journey times and the location of buses. We process data in different ways and then supply it to users / individual travellers and service suppliers as well as network operators and travel information providers. But our present techniques for handling data are not good enough to help us actively manage transport. Instead, we often just react to network problems. Both the public and those responsible for networks want new intelligence to help them make better decisions - instead of being told about a queue, drivers want to know how to avoid it and network managers want to know why it is there and how to reduce it. This is a challenge, even with the relatively small data volume today. But soon there will be many new sources of data and so there is a clear opportunity to actively fill a market gap, before we are overwhelmed with data. This is a global problem, so has great potential for the UK to exploit. And this is not just a transport problem / the military already have sharp awareness of situations their data implies and make critical decisions from it. FREEFLOW therefore fuses transport policy requirements of better services with innovation from outside transport, to generate transport intelligence for urban areas. We want to deliver services that users will really want to use and so dramatically improve how they travel / no just make small changes. We will look for new sources of data, such as from CCTV, to help services evolve. We aim to research how to use intelligent decision support to deliver market ready products. These will deliver policy outcomes of improved safety, reduced congestion and safeguarding the environment, and measure the changes we make through demonstrations in York and London. We aim to make a noticeable change that will stimulate a global market and make life easier for travellers too. Hence our vision is to deliver: Improving transport user decisions and performance, by turning data into intelligence

Publications

10 25 50

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Guo F (2018) Predictor fusion for short-term traffic forecasting in Transportation Research Part C: Emerging Technologies

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Han J (2010) On the estimation of space-mean-speed from inductive loop detector data in Transportation Planning and Technology

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Hodge V (2014) Short-term prediction of traffic flow using a binary neural network in Neural Computing and Applications

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Krishnan R (2010) Intelligent decision support for traffic management in 17th ITS World Congress

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Petri G (2009) Global and local information in traffic congestion in EPL (Europhysics Letters)

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Zhu L (2018) Urban link travel time estimation using traffic states-based data fusion in IET Intelligent Transport Systems

 
Description FREEFLOW was a Consortium of commercial companies, academics and local authorities established to develop new tools for managing and optimising road networks and informing and guiding travellers. FREEFLOW comprised Transport for London (TfL), Kent County Council and City of York Council, as demonstrator sites. The partners providing the technology and innovation are QinetiQ, Trapeze, Mindsheet, Vix ACIS, Trakm8, Imperial College London, Loughborough University and the University of York. Imperial was the lead academic partner.

The key outcomes for future exploitation are:

* intelligent decision support tools in the three demonstration sites, for example in York saving bus journey time without impacting existing traffic and in London identifying incidents earlier than without FREEFLOW;

* showing to real users in the London and Kent demonstration sites multiple tools for traffic management in a single operator interface ;

* better use of existing data with new tools for open access, for example in the Journey Time Estimator and Data Warehouse to publish intelligence to various for of travel information service and device; and

• the real world deployment of standards like UTMC XML and SIRI to provide data for services in a secure and accessible way, to support the policy of making public data freely available for users.
Exploitation Route The project produced a number of exploitable assets for the partners and policy outcomes of value to UK PLC. For example adopting the bus gating demonstrated in York across say 40 UK towns might save £3m to as much as £16m a year in passenger time, while TfL has demonstrated the value of a
"single login and single logging" approach for its control rooms to allow operators to save time managing problems. IDS alerts could be worth £1,000 when an incident occurs, worth potentially millions of pounds in delay cost savings in UK and international cities. These outcomes use existing data that would be relatively straightforward additions to existing systems (such as UTMC). Several partners have identified potential revenues from future sales of products that could be derived from the outputs of FREEFLOW of over £1m per annum.
Sectors Environment,Transport

 
Description Some of the tools and methods developed by the FREEFLOW project were used by Transport for London during the 2012 Olympic Games, to help smooth the flow of traffic in central London, in particular in and around Hyde Park Corner.
First Year Of Impact 2012
Sector Transport
Impact Types Societal,Economic,Policy & public services

 
Description China Scholarship Council PhD studentship
Amount £120,000 (GBP)
Organisation University of Leeds 
Department China Scholarship Council
Sector Academic/University
Country United Kingdom
Start 09/2017 
End 04/2020
 
Description Demonstrating the Value of Mobile Phone Data for Transport Analytics
Amount £32,669 (GBP)
Funding ID TS/I00374X/1 
Organisation Innovate UK 
Sector Public
Country United Kingdom
Start 11/2011 
End 04/2013
 
Description Digital City Exchange
Amount £5,910,480 (GBP)
Funding ID EP/I038837/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 05/2010 
End 10/2016
 
Description SmartEN
Amount £560,000 (GBP)
Funding ID 238726 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 05/2010 
End 04/2014
 
Description Tell Us When
Amount £25,711 (GBP)
Funding ID TS/I000259/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 11/2011 
End 03/2013
 
Description CYBULA LTD 
Organisation Cybula
Country United Kingdom 
Sector Private 
PI Contribution The collaboration with this company is very close as it is a spin out of research group. Technology of the research group is marketed and developed by the company. Currently they sell software and hardware from the University. We provide time for staff to help this transfer. Technology is related to diagnostics and prognostics (SDE), archival data storage and software in the cloud (Carmen), hardware for data logging (N a T) and a number of other technologies.
Collaborator Contribution The company provides a sales for the technology resulting in royalty income. Average this is about £10,000 a year. And is likely to grow in the future. They also provide a basis for commercialisation experience to staff. This helps them to direct their research more fully towards real-world problems. We also use some test instruments from the company for example 1 GHz oscilloscopes costing over £11,000. More likely the company is putting in a link to the University which will allow access via a 1 Gb ethernet network to a new computer that the company is building. The cool computer will help to develop the diagnostics and prognostics research as well as of the work within the department and university.
Impact The output from this collaboration is in the form of commercialisation of the University's research this provides a funding income for the group in addition it provides commercial experience and direction. The main areas of collaboration is in software, theory, hardware relating to computer architectures and neural networks as well as computer vision.