In this paper, an innovative agent-based model is developed to simulate emergent patterns arising from individual actions to analyze opportunities for modal shift in Denmark (ABMoS-DK). The modeling process simulates the interaction between travelers (agents) and the network, and applies a heuristic algorithm to model travelers’ rational decision making process based on tangible costs (ticket price, fixed and variable operation and maintenance costs) and Value of Travel Time (VOTT) explained by Value of Time (VoT), travel time and level of service. The traveler is described by a set of socio-economic attributes (income, family structure, place of residence, car/bike ownership) and the utility is derived from properties of alternative modes to determine whether to use non-motorized, public or private transport. Fluctuation of tangible costs and value of travel time can provide comparative advantages to the alternative modes and has the potential to affect the utility of the mode derived by traveler and change mode choice decision. A set of “pull” and “push” policy scenarios are formulated to help us understand how different factors affect mode choice in transport. We find that disincentivizing private cars has the highest potential for shifting from car use followed by incentives for sustainable modes and expansion of public transport infrastructure. The paper concludes that capturing rational behavioural features of consumers with fine level of heterogeneity in modelling will help to better understand the dynamics of the transportation system and consequently assist policy makers to better identify and target consumer groups with the highest shift potential.