Evaluation and Classification of Travel Value of Passengers between Wuhan and Yichang based on Large Samples

Abstract

Through the evaluation and classification of the travel behavior characteristics of different groups of passengers, it is of great significance for railway transportation enterprises to formulate different marketing strategies and products. Based on the idea of Big Data, this paper takes all the passengers of between Wuhan and Yichang who taking high-speed train as the research samples, uses the fuzzy clustering (FCM) and artificial neural network (ANN) algorithm to calculate and evaluate the price sensitivity of all the passengers, and divides all the passengers into six categories according to the potential economic value and price sensitivity of each passenger. According to the final value of six groups of passengers, make targeted marketing strategies, provide personalized products, and realize the basic concept of Revenue Management. Finally, in the peak period and the low period of passenger flow, sort six groups of passengers according to the travel value of passengers.