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

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Title: Determination of traffic responsive plan selection factors and thresholds using artificial neural networks
Author: Sharma, Anuj
Abstract: Traffic 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 .
URI: http : / /hdl .handle .net /1969 .1 /1228
Date: 2004-11-15

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Determination of traffic responsive plan selection factors and thresholds using artificial neural networks. Available electronically from http : / /hdl .handle .net /1969 .1 /1228 .

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