One performance pledge by Hong Kong Government is to respond to an emergency call within several minutes from the time of call to the arrival of a road transport for emergency services (e.g., an ambulance, a fire vehicle, and a police car) at an incident location. In this regard, ensuring the efficiency of a road transport routing by planning the fastest path for emergency services is important for this performance pledge in Hong Kong. A major obstacle to this performance pledge is frequent unanticipated occurrence of traffic congestion on the planned fastest path in some Hong Kong roads while the design of many of these roads is not feasible for air or sea transport. Detouring is unavoidable when traffic congestion is encountered. To ensure a time-effective detour, backup routes for the detour from the planned path (or simply, bypasses) should be set up beforehand. This paper presents the design of a machine learning model for constructing the optimal bypass structure on top of the planned fastest path from a source to a destination for a road transport for emergency services in Hong Kong. In doing so, Internet of things are installed at each block of a road in heavy traffic-congested areas in Hong Kong for sensing the transit time of each vehicle passing through the block. These large amounts of transit time formulate a big training data set for machine learning to generate a probabilistic model of traffic congestion.