Anu P. Alex¹, Prinsha T² and Manju V. S^3
1Department of Civil Engineering, College of Engineering Trivandrum ²Department of Civil Engineering, Malabar Institute of Technology, Anjarakandy ³Department of Civil Engineering, College of Engineering Trivandrum

Activity based travel demand models are widely used in transportation planning to predict future demand of transportation. Disaggregate level data for the entire population is required as input to these models, which included household level and person level attributes for the entire study area. These data are usually collected by the population census, but are rarely available due to confidentiality reasons. Hence as a viable alternative, population synthesis techniques are used to supplement the microdata. An attempt has been made in this study to generate synthetic population using Markov Chain Monte Carlo Simulation method and to compare this with conventional method. Thiruvananthapuram Corporation in Kerala was selected as the study area and sample data were collected by household survey. The algorithm for population synthesis was coded in C++. The methodology was validated using 16 percentage of the collected data. Prediction accuracy of the method was compared with conventional method and was found better.

Microsimulation, Synthetic population, Beckman’s method, MCMC method.