Dynamic pricing and matching in ride‐hailing platforms

Abstract

Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.

GPS Data in Urban Online Car-Hailing: Simulation on Optimization and Prediction in Reducing Void Cruising Distance

“…However, the performance of the optimization algorithm depends heavily on current knowledge. In real cases, time window batch is a common method for processing of matching pool in realtime dispatch [14]. e longer the time window is, the more optimal the solution is, but it is lesser applicable because it will cause longer waiting times for users.…”

Dynamic Pricing of Ride-Hailing Platforms considering Service Quality and Supply Capacity under Demand Fluctuation

“…Zha et al [48] studied the peak pricing effect on ride-hailing platforms under different assumptions of labor supply behavior. Yan et al [7] used data from Uber to show that, by jointly optimizing the dynamic pricing and dynamic waiting, price variability can be mitigated while increasing the capacity utilization, trip throughput, and welfare. Guda and Subramanian [49] considered the strategic interactions among ride-hailing platform drivers when deciding to move across regions and found that even in areas where driver supply exceeds demand, peak pricing can be profitable.…”

“…e ride-hailing platform's application usually shows that there are more than a dozen people in the queue with an average wait of 20 minutes or that the driver is far away, etc (Xinhua news [6]). To solve these difficulties, some ride-hailing platforms try to dynamically adjust the price to match the supply and demand (Yan et al [7]). For example, DiDi's Spring Festival service fee and the daily price adjustment mechanism for the morning peak are based on supply and demand.…”

“…e fluctuation of ride-hailing demand based on specific reasons was related to more people wanting to use the service (e.g., time of day, price, and service quality), and the supply of services usually be influenced by factors such whether there are drivers available, geographic areas, commissions etc. Due to the influence of weather factors (e.g., sunny, rainy, and snowy weather) and special occasions (e.g., after a concert), there can be a mismatch in supply and demand (Hall et al [9]; Cachon et al [8]; Yan et al [7]; Afèche et al [10]; Hu and Zhou [4]).…”

“…We are motivated by the dynamic pricing problems that arise when ride-hailing platforms face market demand fluctuation and supply-demand mismatch. Although there have been studies on pricing strategies, many authors (e.g., Feng et al [11]; Yan et al [7]; Hu and Zhou [12]; Sun et al [13]; Bimpikis et al [14]) have not designed their models to take into account the characteristic of demand fluctuation over time. ey also do not consider the supply function, which is sensitive to dynamic wages, in relation to time.…”