FREEFLOW Project

The FREEFLOW Project aims to develop decision support tools for traffic network operators and individual travellers and to demonstrate these tools in a number of case studies.

At present, traffic monitoring systems are collecting ever more detailed and timely data about transport networks. These data will be integrated and mined using tools that: detect patterns and anomalies in traffic data; and, use pattern matching to find similar historical patterns. We then take suitable measures to assist traffic network operators and to improve the current traffic flow using both historical knowledge and traffic modelling.

The FREEFLOW Project involves 15 partners from academia, industry and government.

The FREEFLOW Project will operate on three sites.

1. In London, FREEFLOW will analyse Park Lane and Hyde Park Corner, a key London route which is as busy as many UK motorways, and select appropriate traffic signal settings and other measures to mitigate congestion.
2. In Maidstone, the project focuses on managing the urban / inter-urban interface and displaying sets of messages on variable message signs to improve travel times.
3. Work in York will focus on bus punctuality and selecting the most appropriate traffic signal plans and messages to display to maintain punctuality.

York FREEFLOW work

At University of York, we are developing a k-nearest neighbour based pattern matching tool [HKetal_UTSG11, KHetal_ITS10, ABJH_CM10, KHAPL_TRB10, KHAP_UTSG10, HKAP_TT10, ABJH_CM10] using the Advanced Uncertain Reasoning Architecture (AURA). AURA is based on Correlation Matrix Memories (CMMs) which are binary associative neural networks. CMMs can store large amounts of data and allow fast searches.

We convert traffic data variables (such as data from sensors embedded in the road or from buses) into vectors using a quantisation process. These vectors are then stored in a historical database of vectors in the CMM.

As new traffic data are generated, we turn these new data into query vectors using the quantisation process. A query vector is applied to AURA to find the k best matching historical time periods through vector similarity using kernels and incorporating spatio-temporal aspects.

Finally, we provide advice to the traffic operator by cross-referencing operator logs for traffic control interventions made during the k best matching time periods; calculate a quality score for each of these interventions (how well it worked); and, thus, recommend to the operator the intervention likely to be most effective for the current situation. Alternatively, AURA can predict variable values to plug gaps in the data; to overcome a sensor failure; or to look ahead and anticipate congestion problems. We extrapolate and produces a prediction of the future traffic value by averaging the variable value across the set of matches retrived by AURA.

Garry Hollier, Mike Smith, Victoria J. Hodge, Jim Austin [HSHA_ITS2011].
Cumulatives and Errors in Pattern Matching for Intelligent Transport Systems.
In, Proceedings of 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, Leuven, Belgium, 22nd-24th, June 2011

Mike Smith, Richard Mounce, Garry Hollier, Victoria J. Hodge & Jim Austin [SMHHA_ITS2011].
Splitting Rate Modelling for Intelligent Transport Systems.
In, Proceedings of 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, Leuven, Belgium, 22nd-24th, June 2011

Victoria J. Hodge, Rajesh Krishnan, Tom Jackson, Jim Austin & John Polak [HKetal_UTSG11].
Short-Term Traffic Prediction Using a Binary Neural Network.
Presented at, 43rd Annual UTSG Conference, Open University, Milton Keynes, UK, January 5-7, 2011.
download pdf (.pdf).

Rajesh Krishnan, Victoria J. Hodge, Jim Austin, John Polak, Tom Jackson, Mike Smith & Tzu-Chang Lee [KHetal_ITS10].
Intelligent Decision Support for Traffic Management.
In, Proceedings of 17th ITS World Congress: (CD-ROM), Busan: Korea, October 25-29, 2010.
download pdf (.pdf).

Jim Austin, Grant Brewer, Tom Jackson & Victoria J. Hodge [ABJH_CM10].
AURA-Alert: The use of binary associative memories for condition monitoring applications.
In, Proceedings of 7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies: (CM 2010 and MFPT 2010), Stratford-upon-Avon, England, 22-24 June, 2010. ISBN: 978-1-61839-013-4, Vol. 1: pp. 699-711.
download pdf (.pdf).

Victoria J. Hodge, Rajesh Krishnan, Jim Austin & John Polak [HKAP_TT10].
A computationally efficient method for online identification of traffic incidents and network equipment failures.
Presented at, Transport Science and Technology Congress: TRANSTEC 2010, Delhi, April 4-7, 2010.
download pdf (.pdf).

Rajesh Krishnan, Victoria J. Hodge, Jim Austin, John Polak & Tzu-Chang Lee [KHAPL_TRB10].
On Identifying Spatial Traffic Patterns using Advanced Pattern Matching Techniques.
In, Proceedings of Transportation Research Board (TRB) 89th Annual Meeting, Washington, D.C., January 10-14, 2010. (DVD-ROM: 2010 TRB 89th Annual Meeting: Compendium of Papers).

Rajesh Krishnan, Victoria J. Hodge, Jim Austin & John Polak [KHAP_UTSG10].
A Computationally Efficient Method for Online Identification of Traffic Control Intervention Measures.
Presented at, 42nd Annual UTSG Conference, Centre for Sustainable Transport, University of Plymouth, UK: January 5-7, 2010.
download pdf (.pdf).

Victoria J. Hodge, Mike Smith & Jim Austin [HSA_EWGT09].
Data, Intelligent Decision Support and Pattern Matching.
In, Proceedings of the XIII Meeting of the Euro Working Group on Transportation (EWGT'2009), Advances in Transportation Systems Analysis, Padua, Italy: Sept 23-25, 2009, Padova University Press, Padua, Italy, ISBN 978-88-97385-20-2.

Victoria J. Hodge, Tom Jackson & Jim Austin [HJA_ITS09].
Optimising Activation of Bus Pre-signals.
In, Models and Technologies for Intelligent Transportation Systems. Proceedings of the International Conference, Rome, June 22-23, 2009) (G. Fusco, ed.), Aracne Editrice, ISBN 978-88-548-3025-7, pp. 344-353.
download pdf (.pdf).

Victoria J. Hodge, Mike Smith & Jim Austin [HSA_ITS09].
Intelligent Car Park Routeing for Road Traffic.
In, Models and Technologies for Intelligent Transportation Systems. Proceedings of the International Conference, Rome, June 22-23, 2009) (G. Fusco, ed.), Aracne Editrice, ISBN 978-88-548-3025-7, pp. 326-333.
download pdf (.pdf).