Base travel demand forecasting model link volumes may be accurately calibrated at a screenline level to within ±5% of traffic counts, but intersection turning movement volumes, dispersed from or converged to the calibrated link volumes, may be quite erratic. To capture traffic pattern changes at intersections with respect to transportation improvement projects or/and land use changes, one can not just "read off" raw simulated turning volumes from a planning model, but requires intersection post-processing to project future turning movements with confidence. To address the intersection post-processing problem, the City of Bellevue has used an Intersection Post-Processor for 17 years to project future intersection turning movement volumes based on an iterative and semi-automatic procedure. The procedure was initially derived by Furness and later modified by Mekky — proportionally to the base year actual turning volumes, the algorithm distributes modeled arrival link volumes while maintaining modeled departure volume constant, or the other way around iteratively until the differences between the results and link volumes desired are minimized within an accepted error. The Bellevue Post-Processor has the procedure enhanced on two levels: 1) adjust existing turns to future turn changes due to future network changes, based on several mathematical solutions; and 2) balance future re-calibrated (or adjusted) intersection link arrival and departure volumes to the Level I adjusted turns. This paper not only discusses the mathematics behind the Bellevue Intersection Post-Processor, but also presents a general and better solution out of several possibilities to intersection post-processing problems. It makes it possible to fully automate the Intersection Post-Processor, and thus save modelers' tremendous amount of manual time. The presentation will benefit transportation forecasting and analysis communities in obtaining more reasonable and accurate forecasts of future intersection turning volumes by using base year counts and travel demand forecasting models, and by designing a more efficient user program, especially when dealing with a large number of intersections in an urban setting.