Re-building of the traffic model from non-equilibrium dynamics and partial observations Alexandros Sopasakis Lund Institute of Technology The effect of traffic interactions on the model parameters can be considerable especially when equilibrium is not certain. We examine how information theory can be applied in resolving the dynamics and obtaining the needed parameter values from available although partial data. Specifically we develop two different tools, the Relative Entropy Rate and the Fisher Information Matrix whose task is to reconstruct the Markov Chain and emulate the dynamics from available but incomplete data through Monte Carlo simulations. We demonstrate this process by simulating a problem in vehicular traffic. In that respect a look-ahead potential is employed and an anisotropic simple exclusion process (ASEP) is created which moves particles in only one direction. The process is implemented using a kinetic Monte Carlo method and contrasted against other similar "state of the art" lattice-based approaches in that area. We present concrete results under rush-hour, traffic data from highway US-101 in Los Angeles, California.