A Polynomial-time, Truthful, Individually Rational and Budget Balanced Ridesharing Mechanism

Abstract

Ridesharing has great potential to improve transportation efficiency while reducing congestion and pollution. To realize this potential, mechanisms are needed that allocate vehicles optimally and provide the right incentives to riders. However, many existing approaches consider restricted settings (e.g., only one rider per vehicle or a common origin for all riders). Moreover, naive applications of standard approaches, such as the Vickrey-Clarke-Groves or greedy mechanisms, cannot achieve a polynomial-time, truthful, individually rational and budget balanced mechanism. To address this, we formulate a general ridesharing problem and apply mechanism design to develop a novel mechanism which satisfies all four properties and whose social cost is within 8.6% of the optimal on average.

From intelligent agents to trustworthy human-centred multiagent systems

“…In addition to EV charging, we have developed novel mechanisms for several other domains including grid computing, where tasks are allocated to compute resources [19,20] and, more recently, the related fog/edge computing [21], where computing is done close to where data is generated and services consumed. Other application areas include sponsored search [22,23] and ride sharing [24,25].…”

“…for an optimal distribution of charging stations for electric vehicles) [9,106,107]. Finally, to support the transition towards on-demand mobility and shared mobility services, we developed adaptive pricing mechanisms [108] to incentivise the relocation of shared vehicles and introduced multiagent incentive engineering methods for ridesharing (together with partners from Toyota Motor Europe) [25]. Within the shared mobility domain, we also looked at how to balance different objectives (including social and environmental) [109], how to involve riders in the routing process [110], and how to account for the cost of walking in ridesharing [111].…”