High-speed rail (HSR) has developed rapidly in China over the recent years, for the less pollution, faster speed, comfort, and safety. However, there is still an issue on how to improve the seat occupancy rates for some HSR lines. This research analyzes the pricing strategy for HSR in Wuhan-Guangzhou corridor based on the competition among different transport modes with the aim of improving occupancy rates. It starts with the theoretical analysis of relationship between market share and ticket fare, and then disaggregate choice models with nested structure based on stated preference (SP) data are established to obtain the market share of HSR under specific ticket fare. Finally, a pricing strategy is proposed to improve the occupancy rates for Wuhan-Guangzhou HSR. The results confirm that a pricing strategy with floating fare should be accepted to improve the profit of HSR; to be specific, the ticket fare should be set in lower level on weekdays and higher level on holidays. 1. Introduction High-speed rail (HSR) is currently regarded as one of the most significant technological breakthroughs in passenger transport developed in the second half of the 20th century [1]. Due to the advantages of rapidness, comfort, convenience, safety, and reliability [2], China has witnessed rapid development of HSR over the past years. However, its performance in operation is still restricted by the pricing strategy under the intense competition among various transport modes. The traditional fixed pricing strategy gives up the induced passenger flow generated by fares change [3] and the high pricing of HSR leads to low occupancy rates and resources waste. For example, statistics shows that the average occupancy rates of Wuhan-Guangzhou HSR can be as low as 20% except for the spring festival which is not satisfactory as expected. The lower the occupancy rate is, the less the profit is. Therefore reasonable pricing strategy should be researched to solve the pricing problems for HSR. In order to solve the problem of pricing strategy, a number of recent researchers have been devoted to studying reasonable methodology for passenger transport pricing. Li and Tayur [4] and Labbé et al. [5] applied the Bilevel programming to the optimal pricing. Zeng et al. [6] put forward a new thought of combining the value of travel time and Bilevel programming to maximize the benefit of the railway agencies and the passengers’ utility. Hsu et al. [7] and Adler et al. [8], based on game theory, analyzed the competition between two modes of transport and get optimal pricing in order to maximize
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