Krishnan, Rajesh, Hodge, Victoria Jane orcid.org/0000-0002-2469-0224, Austin, Jim orcid.org/0000-0001-5762-8614 et al. (1 more author) (2010) A Computationally Efficient Method for Online Identification of Traffic Control Intervention Measures. In: 42nd Annual UTSG Conference, 05-07 Jan 2010.
Abstract
Adaptive traffic control systems such as SCOOT and SCATS are designed to respond to changes in traffic conditions and provide heuristically optimised traffic signal settings. However, these systems make gradual changes to signal settings in response to changing traffic conditions. In the EPSRC and TSB funded FREEFLOW project, a tool is being designed to rapidly identify severe traffic problems using traffic sensor data and recommend traffic signal plans and UTC parameters that have worked well in the past under similar traffic conditions for immediate implementation. This paper will present an overview of this tool, called the Intelligent Decision Support (IDS),that is designed to complement adaptive traffic control systems. The IDS is essentially a learning based system. It requires an historic database of traffic sensor data and traffic control intervention data for the application area as a knowledge base. The IDS, when deployed online, will monitor traffic sensor data to determine if the network is congested using traffic state estimation models. When IDS identifies congestion in the network, the historic database is queried for similar congestion events, where the similarity is based on both the severity and the spatial pattern of congestion. Traffic control interventions implemented during similar congestion events in the historic database are then evaluated for their effectiveness to mitigate co ngestion. The most effective traffic control interventions are recommended by IDS for implementation, along with an associated confidence indicator. The IDS is designed to work online against large historic datasets, and is based on traffic state estimation models developed at Imperial College London and pattern matching tools developed at the University of York. The IDS is tested offline using Inductive Loop Detector (ILD) data obtained from the ASTRID system and traffic control intervention data obtained from the UTC system at Transport for London (TfL) during its development. This paper presents the preliminary results using TfL data and outlines future research avenues in the development of IDS
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 21 Jun 2016 13:47 |
Last Modified: | 17 Oct 2024 09:06 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89509 |