Traffic prediction is a critical component in intelligent traffic control systems to project future vehicle evolution in order to make optimal decisions for all competing demand in real-time. In this paper, we study the use of cell transmission model for traffic prediction in signalized urban network. Existing models treat traffic flow on a link as a single commodity until the flow reaches diverge point or turn bay. Turning ratio is applied at the diverge point to compute the flow going to individual movement. Lane blockage and spill-back happens due to insufficient green time or imperfect road structure such as short turn bay or road incidents. Most likely the effect of lane blockage and spill-back will not have equal influence on every movement. To address this, we extend
cell transmission model to keep track of vehicle route intent in every cell and perform vehicle progression at the movement level. To evaluate feasibility and accuracy of the model for use in real-time, this study has developed a complete modeling framework comprising connected vehicle traffic simulation platform, real-time traffic tracking and prediction platforms and has conducted extensive simulation experiments with a segment of Corporation Road and Boon Lay Way in Jurong West, Singapore.