Dynamic Pricing in One-Sided Autonomous Ride-Sourcing Markets

Abstract

Dynamic pricing has been used by Transportation Network Companies (TNCs) to achieve a balance between the volume of ride requests with numbers of available drivers on two-sided TNC markets. Given the desire to reduce operating costs and the emergence of Autonomous Vehicles (AVs), the introduction of TNC-owned AV fleets could convert such services into one-sided markets, where operators have full control of service supply. In this paper we investigate the impact of utility-based dynamic pricing for Autonomous TNCs (ATNCs) in one-sided markets. We test the method using an Agent-Based Model (ABM) of Greater London in conditions of monopoly and competition, focusing on a statically priced ATNC service that offers a mix of private and shared ride services. Public transport is considered as an alternative mode of transportation in both scenarios. Results indicate that in monopoly, dynamic pricing provides higher revenues than static pricing at non-peak hours when average waiting times are low. On the contrary, in competition, dynamic pricing is superior at peak hours where increased waiting times are observed, thus increasing the value of low waiting time rides. Overall, in both market structures, it is found that shared trips are more popular in dynamic pricing compared to static pricing.

Exploring the attitudes of Millennials and Generation Xers toward ridesourcing services

“…The current fare levels of ridesourcing services may present considerable barriers to frequent service adoption for many people. Considering that future AV technologies may reduce the cost of ridesourcing services by removing the cost associated with human drivers (Compostella et al 2020;Walker Johnson, 2016;Stocker and Shaheen 2018;Spieser et al 2014;Karamanis et al 2018), this study employed SP scenarios where the ridesourcing costs were decreased to be more comparable with driving costs. With this experiment, we were able to focus on other potential barriers (such as attitudes, habits, and perceptions, etc.)…”

Vehicle Redistribution in Ride-Sourcing Markets using Convex Minimum Cost Flows

“…Considering our resource allocation problem introduced in Section II-A, we can regard the vertices v ∈ A as source vertices since these constitute the initial states of all vehicles in the fleet. In a similar fashion, the vertices in C can be identified as sinks, however; their balance vectors cannot be defined in advance, as certain demand requirements might lead to violation of (5).…”

“…matching platforms to fleet operators having complete control of the supply [4]. In such a scenario, currently implemented dynamic pricing strategies would still suppress demand [5], but TNCs, as fleet owners, would need to decide vehicle redistribution operations. Generally, in the absence of drivers entering the market proactively by knowing historical surge pricing patterns [2], autonomous vehicle ride-sourcing operators would need to manage their fleet effectively, to alleviate any asymmetries of demand across road-networks.…”

Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions

“…Assuming that all vehicles will be assigned, the set of all path-vehicle allocations is Proof. We reduce an instance of MWIS, a known APX-hard 6 problem [19], to an instance of Model 2. Given a weighted graph G = (V, E, w), the MWIS objective is to find a set of pairwise disjoint nodes S ⊆ V with maximum total weight.…”

“…Vehicles (AVs) in the near future, their platforms are likely to be transformed into one-sided markets, where they will enjoy complete control of the supply [5]. Previous work by Karamanis et al [6] demonstrated that such platforms can still incentivise the use of shared rides or PT transport while remaining profitable.…”