By the same authors

A computationally efficient method for online identification of traffic incidents and network equipment failures

Research output: Contribution to conferencePaperpeer-review

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ConferenceTransport Science and Technology Congress: TRANSTEC 2010
CityNew Delhi
Conference date(s)4/04/107/04/10

Publication details

DatePublished - 4 Apr 2010
Original languageEnglish


Despite the vast wealth of traffic data available, currently there is only
limited integration, analysis and utilisation of data in the transport domain. Yet, accurate congestion and incident detection is vital for traffic network operators to allow them to mitigate the cost of traffic incidents. Recurrent (cyclical) traffic congestion tends to be managed using timetabled control measures or through the use of adaptive traffic control systems such as SCOOT and SCATS. However, for non-recurrent congestion with rapid onset, such as the congestion caused by a traffic incident or traffic equipment
failure, traffic network operators have to quickly detect the problem and then determine the likely cause before selecting the most appropriate action to both manage the traffic network and mitigate the congestion. This is a complex task requiring specialist knowledge where assistance from automated tools will help facilitate the operator tasks. Automated detection is becoming an increasingly viable option due to the increased use of traffic sensors in the road network. Therefore, the aim of the FREEFLOW project is to provide an Intelligent Decision Support (IDS) tool which is designed to complement
existing fixed-time traffic control systems and adaptive systems SCOOT and SCATS. IDS will use traffic sensor data to rapidly identify traffic problems, recommend appropriate interventions that worked in the past for similar problems and assist the traffic network operators to pinpoint the cause of the problem. Recommendations will be displayed to the network operator who will use this knowledge to select the most appropriate course of action. This paper describes and analyses the components of the IDS tool used for identifying incidents and faulty equipment.

    Research areas

  • Intelligent Decision Suppor, Traffic Management, Traffic State Estimation Modelling, Pattern Match, Incident Detec tion, Equipment Failure Detection

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