<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author><author><style face="normal" font="default" size="100%">Nagui M. Rouphail</style></author><author><style face="normal" font="default" size="100%">B. Brian Park</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistically-Based Validation of Computer Simulation Models in Traffic Operations and Management</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Transportation and Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced traffic management systems</style></keyword><keyword><style  face="normal" font="default" size="100%">computer simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">CORSIM</style></keyword><keyword><style  face="normal" font="default" size="100%">model validation</style></keyword><keyword><style  face="normal" font="default" size="100%">transportation policy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The process of model validation is crucial for the use of computer simulation models in transportation policy, planning, and operations. This article lays out obstacles and issues involved in performing a validation. We describe a general process that emphasizes five essential ingredients for validation: context, data, uncertainty, feedback, and prediction. We use a test bed to generate specific (and general) questions as well as to give concrete form to answers and to the methods used in providing them. The traffic simulation model CORSIM serves as the test bed; we apply it to assess signal-timing plans on a street network of Chicago. The validation process applied in the test bed demonstrates how well CORSIM can reproduce field conditions, identifies flaws in the model, and shows how well CORSIM predicts performance under new (untried) signal conditions. We find that CORSIM, though imperfect, is effective with some restrictions in evaluating signal plans on urban networks.&lt;/p&gt;
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