Abstract: Present studies of the pricing of new transportation services mainly focus on a single feature, and there is a lack of a overall pricing model considering different features, and the effects of the interaction among features on the pricing of new transportation services. Several CART trees are integrated and optimized to construct the TPCBoost model. Then the ride-hailing data of New York City from Google Cloud and Coursera,including the time, distance and number of passengers, are used to train and test the TPCBoost model. The TPCBoost model is also used to analyze the relationships between different features and their influences on pricing. The robustness and stability of the TPCBoost model are verified, and it is found that:(1) features nonlinearly affects pricing, short distance, short time, and few passengers make the price stable, but pricing is volatile under the conditions of long distances, long hours, and many passengers; (2) When a specific ride distance is combined with a specific ride time, a pricing sensitivity period occurs, and market competition heats up in the pricing sensitive period, and the specific ride time inhibits the price rise; (3) The number of riders does not directly affect pricing, but its combination with other features can indirectly influence pricing; (4) Ride distance positively affects the pricing, but it does not directly affect the pricing during the pricing sensitive period; (5) Daily ride time periodically affects pricing. In particular, there are several pricing transitions each day,when the peak pricing time pointsare generally more than the trough pricing time points. For China, online taxi-hailing service has its own features, including fierce competition, large market space, and intelligence. Therefore, according to the research results of this paper, it is shown that Chinese companies can lower their prices by introducing preferential measures when taking special trips, such as time or distance, to show their attraction to passengers.Since there exist pricing trough stage and pricing peak stage, and the peak stage of pricing presents fierce market competition, Chinese enterprises need to adjust pricing accurately and timely at the peak stage; the trough stage of pricing means market competition slows down, and thus Chinese enterprises can properly improve the pricing to obtain revenue profits. When the travel distance and travel time are combined, the new transportation service market in China enters the stage of fierce competition, and Chinese regulators need to monitor pricing fluctuations in the market in real time and prevent wide pricing fluctuations, which leads to the phenomenon that vicious competition destroys the market.The TPCBoost model developed in this paper is verified by the actual data to conform to the pricing law, and can provide a useful reference for the transportation decision-making of operating companies, regulatory departments and passengers.It also contributes to the new transportation services pricing literature by providing a tool with high accuracy and robustness, and by revealing the comprehensive effects of distance, time, and number of passengers.

Key words: TPCBoost model; new transport services; pricing sensitive period; CART trees