Abstract:

An integrated model based on the neural network and Markov chain was established to predict the real estate price along the urban rail transit under the condition of limited a priori data and a host of uncertainties. A BP neural network was used to fit roughly the real estate price curve along the urban rail transit based on a little sample data. Markov chain was applied to achieve the state transition probability matrix of the system to modify the predieted result. The model takes the randomness of the real estate price into account, and it stll available regardless of that the statistics of the real estate prices are scarce. The real estate price along the rail transit No.13 in Beijing was taken as an example to illustrate the application of the model. The calculation results showed that the model is characterized by good accuracy and reliability of the predicition.

Key words: engineering of communications and transportation system, urban rail transit, real estate, price, forecast, BP neural network, Markov chain