This paper tackles the real-time Railway Traffic Management Problem (rtRTMP). It is the problem of finding an optimal choice for the train schedules and routes to reduce the delays of trains due to conflicts. We present a new formulation of the rtRTMP. This new formulation is based on a previously proposed one that models railway traffic at a microscopic level with optional activities using a Constraint Based Scheduling (CBS) approach. To ease the modelling of optional activities, a new concept based on a tree data structure and a specific filtering algorithm was extended through the introduction of conditional time-interval variables in Ilog CP-optimizer library. The new formulation of the rtRTMP presented in this paper exploits the conditional time-interval variables. The formulation has been validated with experiments on a large set of instances. The experimental results demonstrate the effectiveness of this new CBS model and show its good performance compared with the state-of-the art RECIFE-MILP algorithm.