Traffic congestion is a serious problem that is only expected to get worse in the future. Statistics shows that half of traffic congestion is caused by temporary disruptions like accidents. These events have dramatic impact on road network availability and cause huge delays for commuters. Also, they are usually unexpected and hard to manage by traffic authorities. State-of-the-art navigation systems started to provide real-time information about traffic conditions to help users make better routing decisions. However, traffic in the road network changes rapidly and the advice calculated now may not be valid after few minutes. This is especially critical in the presence of traffic incidents, where the impact of the incident could cause traffic to propagate to nearby roads. Thus, it is important for navigation systems to consider the evolution and future impact of traffic events. In this work, we present a navigation system that uses faster than realtime simulations to predict the evolution of traffic events and help drivers proactively avoid congestion caused by events. The system can subscribe to real-time traffic information and forecast the traffic conditions using fast simulations. We evaluate our approach through extensive experiments to test the performance and accuracy of the simulator with real data obtained from TomTom Traffic API. Also, we test the quality of navigation advice in realistic settings and show that our solution is able to help drivers avoid congested areas in cases where even real-time update methods lead drivers to congested routes.