Dynamic mode choice is essential to understand the potential effectiveness of policies aiming to achieve desirable modal split targets or to manage the demand for resource-limited systems such as shared mobility services. In this paper, we propose an estimation of dynamic modal split for work-related trips, including mode- and time-specific costs, with activity participation based on utility maximization. In order to obtain an accurate profile while remaining at an aggregate level, three types of work activities are described (full time, morning and afternoon shift). The estimated modal split concerns motorized vehicles, soft modes but also train and urban public transport. Based on utility maximization principles, the accumulated utility is formulated within a departure time choice model. A Markov Chain Monte Carlo procedure is used to evaluate the marginal utility function parameters which are used in a joint departure time and mode choice evaluation. Mode specific travel speed for each time of the day is used to estimate also travelled distance distribution per mode. The methodology is applied and tested, using data collected in Ghent in 2008. 16.749 work-related trips have been considered in a simplified estimation where two successive trips are constrained to be done with the same mode. This methodology is characterized by low data requirements and the model is shown to be flexible to include all available type of information in order to refine or accelerate the estimation. The proposed method is easy to implement using only dynamic trip counts, without the need for simulation or traffic assignment.