Route guidance systems based on real-time information

Traditional route guidance systems usually model the transportation network as a static graph, however, this model is not close to reality. Due to time-varying and stochastic properties, we establish the mathematical model of a transportation network as a stochastic time-dependent network. Then we define the mathematical formulation of our knowledge about the network, which can be divided into two categories, prior information and real-time information. Under the framework developed by us, we propose two solutions to solve the problem of route guidance based on real-time information. The solutions are, respectively, a fastest path finding algorithm and two time-adaptive decision rules based on greedy strategy. At last, simulation results on an artificial stochastic time-dependent network with multidimensional normal distribution are given and the performance of our algorithms is evaluated and compared.