Recent travel demand modeling practices focus on micro, disaggregate, and activity level travel behavior and patterns. The application of such practices requires detailed population information in socio-economic and demographic data. For example, in a four-step travel demand model total household and employment at Traffic Analysis Zone (TAZ) level are sufficient for trip generation. However, in an activity based model more detailed information in the small area (TAZ), such as population by different age categories and employment type, is required to produce trip chaining and other details in the population synthesis step. Conventionally many studies have used Iterative Proportional Fitting (IPF) to generate such detailed information. But, IPF suffers from severe drawbacks and is blind to detailed synthesis of variables. In this paper, a novel approach is presented where population by age category evolves over time period using logistic regression technique. The methodology is presented in three steps: coefficient estimation, forecast and validation. First, the 1990 census data is used to model population by age group in 2000 at the TAZ level. The model result is applied to forecast 2010 data for validation. The methodology is applied to Baltimore Metropolitan Council (BMC) region and the results show that the proposed model produces and forecasts reasonably well. The experiences gained from this study are: (1) population evolution pattern in city area should be treated separately from other, e.g., Baltimore City has a special population structure from other surrounding counties; (2) this model provides a good estimation and prediction for the age group 0-24 and 35-64 and the problems occurs in 25-34 and 65+ groups, whose migration trend is not consistent over time and cannot be captured by the current parameters alone. Though in this paper population by age is considered for demonstration, the proposed methodology can be used for other variables of interest such as household type, householder’s age, employment type, occupation, etc. The proposed tool can be adapted by small and large scale planning agencies for preparing detailed socio economic and demographic input data for travel demand modeling practices.