Personalised pricing in ride pooling to maximise expected profit

Abstract

Perception of the ride-pooling service highly depends on individual preferences. Fitting a proper discount for a ride endorses its attractiveness for travellers and increases the profit of the operator. We analyse a scenario where individual heterogeneous behavioural traits remain latent. We introduce the individual pricing strategy, which balances ride’s profit with its attractiveness perceived by the travellers. Our method finds optimal sharing discounts individually tailored at the ride level such that the product of platform’s profit and traveller’s satisfaction (i.e. acceptance probability) is maximised. To understand the potential impact of personalised pricing on pooling systems’ performance we run NYC experiment answering the questions on: the optimal sharing discounts, their impact on travellers perception and operator’s profit. Our method outperforms flat discount strategy from both perspectives: travellers are more satisfied with the service and the operator increases own profit.