Abstract:

The algorithmic personalized pricing of online taxi-hailing platforms has produced complex market impacts, and compared with traditional cab service, the order cancellation rate of online taxi passengers reaches about 30%. Therefore, it is worth exploring the impact mechanism of algorithmic personalized pricing on passenger cancellation rates and the key characteristics of whether passengers fulfill their orders. This paper tried to establish the causal mechanism between algorithmic personalized pricing and passengers’ order cancellation rate using rectangular Hotelling model. Using a Stackelberg game model between two taxi-hailing platforms, it revealed the relationship among discriminatory pricing, passenger cancellation rate, and competition intensity between two platforms. Furthermore, based on the big data of online taxi-hailing platform orders, this paper applied some inductive learning tools such as Bhattacharyya distance, Gradient Boosting Decision Tree (GBDT) and improved Las Vegas method for wrapper-method feature selection to data mining of millions of orders on online taxi-hailing platforms to find out the key features that determine whether passengers take the orders or not. Analysis results show that the final consumption choice of passengers mainly depends on the price factors. And improving the match and dispatch strategies to reduce passengers’ waiting time can significantly improve fulfillment rate. The results are helpful for taxi-hailing platform to appropriately design the pricing and operation strategies to maintain the number of customers in the two-sided markets, which ensures the sustainable and successful operation of the platform. Meanwhile, it will provide a theoretical basis for antitrust authorities to intervene in platform personalized pricing.

Key words: online taxi-hailing, algorithmic personalized pricing, feature engineering, order compliance rate, regulation