Determination of traffic responsive plan selection factors and thresholds using artificial neural networks

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dc.contributorMesser, Carroll
dc.creatorSharma, Anuj
dc.date.accessioned2004-11-15T19:50:56Z
dc.date.accessioned2017-04-07T19:49:08Z
dc.date.available2004-11-15T19:50:56Z
dc.date.available2017-04-07T19:49:08Z
dc.date.created2004-08
dc.date.issued2004-11-15
dc.description.abstractTraffic congestion has become a menace to civilized society. It degrades air quality, jeopardizes safety and causes delay. Traffic congestion can be alleviated by providing an effective traffic control signal system. Closed-loop traffic control systems are an example of such a system. Closed-loop traffic control systems can be operated primarily in either of two modes: Time of Day Mode (TOD) or Traffic Responsive Plan Selection Mode (TRPS). TRPS mode, if properly configured, can easily handle time independent variation in traffic volumes. It can also reduce the effect of timing plan aging. Despite these advantages, TRPS mode is not used as frequently as TOD mode. The reason being a lack of methodologies and formal guidelines for predicting the factors and thresholds associated with TRPS mode. In this research, a new methodology is developed for determining the thresholds and factors associated with the TRPS mode. This methodology, when tested on a closed-loop system in Odem, Texas, produced a classification accuracy of 94%. The classification accuracy can be increased to 98% with a proposed TRPS architecture.
dc.identifier.urihttp://hdl.handle.net/1969.1/1228
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectTraffic responsive
dc.subjectArtificial neural network
dc.subjecttraffic controller modes of operation.
dc.titleDetermination of traffic responsive plan selection factors and thresholds using artificial neural networks
dc.typeBook
dc.typeThesis

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