Understanding ride-on-demand service: Demand and dynamic pricing

Abstract

Emerging ride-on-demand services (e.g., Uber or Uber-like) are vying to penetrate into the market of traditional taxi service, and they are ubiquitous in the nature, by using smart mobile devices like on-car GPS and mobile phone. These ubiquitous services are also beneficial for the environment by increasing the utilization of cars and improving travel efficiency. Through collaboration with a leading service provider in China, we are able to collect vast amount of accurate data and analyze the nature of the demand and dynamic pricing mechanisms that match the supply with demand. We consider the analysis as an important step towards making the ubiquitous service more efficient and beneficial to the sustainability of future smart cities. We collect datasets of passengers' orders and payment information, and focus on the analysis of demand and dynamic pricing. In demand analysis, we discuss its general characteristics, passenger grouping and demand clustering; in dynamic pricing analysis, we discuss the pattern and determination of dynamic pricing multipliers. Our findings pave the way for future study on system optimization, dynamic pricing and policy considerations.