We Are on the Way: Analysis of On-Demand Ride-Hailing Systems

Abstract

Problem definition: Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smartphones to submit trip requests and match them to drivers based on their locations and drivers’ availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system—in particular, whether it will help reduce passengers’ average waiting time compared with traditional street-hailing systems. Academic/practical relevance: The on-demand ride-hailing problem has gained much academic interest recently. The results we find in the ride-hailing system have a significant deviation from classic queueing theory where en route time does not play a role. Methodology: In this paper, we shed light on this question by building a stylized model of a circular road and comparing the average waiting times of passengers under various matching mechanisms. Results: We discover the inefficiency in the on-demand ride-hailing system when the en route time is long, which may result in nonmonotonicity of passengers’ average waiting time as the passenger arrival rate increases. After identifying key trade-offs between different mechanisms, we find that the on-demand matching mechanism could result in lower efficiency than the traditional street-hailing mechanism when the system utilization level is medium and the road length is long. Managerial implications: To overcome the disadvantage of both systems, we further propose adding response caps to the on-demand ride-hailing mechanism and develop a heuristic method to calculate a near-optimal cap. We also examine the impact of passenger abandonments, idle time strategies of taxis, and traffic congestion on the performance of the ride-hailing systems. The results of this research would be instrumental for understanding the trade-offs of the new service paradigm and thus enable policy makers to make more informed decisions when enacting regulations for this emerging service paradigm.

A new ride‐sharing model incorporating the passengers' efforts

“…Some incorporate customer attitude (rider ratings) and driver strategic behavior (selective or strategic idling) into the matching process (Chu et al, 2018;Mai et al, 2018). Furthermore, platform-level research on competition in ride-sharing markets (Cohen & Zhang, 2017;Nikzad, 2017) and on-demand ride-hailing versus street hailing (Feng et al, 2017) are conducted. These publications provide various perspectives to study the ride-sharing problem and inspire our research.…”

Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities

“…Current matching algorithms for ODM services can be grouped into three categories -first-dispatch protocol, batching, and dynamic matching (Yan et al, 2019). The first-dispatch protocol (FDP) assigns a customer request instantaneously to one of the idle vehicles, which has the shortest estimated arrival time at the pickup location (Feng et al, 2020). On the other hand, the batching algorithm is a generalization of FDP, where the ride requests are accumulated for a short period and then assigned to an idle vehicle based on an optimization model (Ashlagi et al, 2018;Korolko et al, 2018).…”

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

“…e arrival of passengers is regarded as a Poisson process, and drivers are regarded as servers in the queuing system (Bai et al [51]; Taylor [52]; Hu and Zhou [4]), with queuing theory used to study the supply and demand matching. Feng et al [11] compared the efficiency of online supply and demand matching with the average waiting time of traditional taxi hailing. Hu and Zhou [12] considered an intermediary's problem of dynamically matching demand and supply of heterogeneous types in a periodic review.…”

“…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.…”