http://tags.library.upenn.edu/tag/singapore PennTags Feed http://tags.library.upenn.edu/makerecord/url/18776 http://tags.library.upenn.edu/makerecord/url/18776 <div class="storyheadline">IBM and Singapore's Land Transport Authority Pilot Innovative Traffic Prediction Tool</div> <img style="display: none" src="http://i.cnn.net/money/ssi/partners/marketwire_logo.gif" border="0" alt="Marketwire" width="200" height="33" align="right" /> <div class="storytimestamp">August 01, 2007: 09:17 AM EST</div> <!-- CONTENT --> <!--Start Body--> <p> IBM (NYSE: IBM) today announced the successful completion of a pilot test on traffic prediction in Singapore's Central Business District. </p> <p> Using historical traffic data and real-time traffic input from the Singapore Land Transport Authority (LTA)'s i-Transport system, IBM's Traffic Prediction Tool predicted traffic flows over pre-set durations (10, 15, 30, 45 and 60 minutes). Overall prediction results were well above the target accuracy of 85 percent. With these predictions, LTA's traffic controllers will be able to anticipate and better manage the flow of traffic to prevent the build-up of congestion. </p> <p> At a global level, this innovative use of technology represents another option in the response to the complexity of mega-urban congestion, especially in the developing world. </p> <p> While infrastructure growth is required in many cities, it cannot be the only solution to congestion given the significant budgetary, social and environmental costs. IBM believes innovation can and needs to be applied to the challenge of mega-urban congestion. For example, technologies such as the Traffic Prediction Tool enable more intelligent use of a city's existing infrastructure. </p> <p> The Traffic Prediction Tool was developed by IBM Research. The pilot was supported by a global IBM team, with resources from Singapore, the UK and the USA, working closely with a team from the LTA. The pilot took place from December 2006 to April 2007. </p> <p> Both speed and volume predictions covering the Central Business District were above the target accuracy of 85 percent. In addition, during peak periods where more real-time data was available, the average accuracy of the volume forecasts on the District was near or above 90 percent from 10-minutes all the way to the predictions 60-minutes into the future. </p> http://tags.library.upenn.edu/makerecord/url/10521 http://tags.library.upenn.edu/makerecord/url/10521 http://tags.library.upenn.edu/makerecord/voyager/10183 http://tags.library.upenn.edu/makerecord/voyager/10183 <div class="mlacite"><span style="text-decoration: underline">Beyond description : Singapore space historicity / Edited by Ryan Bishop, John Phillips, and Wei-Wei Yeo. </span> [0415299810 (acid-free paper) ] London ; New York : Routledge, 2004. <br />Call#: Fine Arts Library NA1530.S55 B49 2004</div><br /> <br />