The disclosed embodiments illustrate methods and systems for predicting demand of vehicles in a transportation network. The method includes determining demand events at each of one or more locations for time intervals based on historical demand data. The demand events correspond to a demand of vehicles at one or more locations during plurality of time intervals throughout a day. The method includes creating a graph comprising nodes, and edges connecting nodes, each node being representative of a demand event from demand events. An edge is representative of dependency between two demand events from demand events. The method includes predicting demand of vehicles at a location from one or more locations during a predetermined time interval based on the graph and a real time demand of vehicles associated with other demand event. The method includes displaying demand prediction on a computing device at one or more locations of transportation network.