OCAIMAJan 25, 2016

Pricing Vehicle Sharing with Proximity Information

arXiv:1601.06672v16 citations
Originality Synthesis-oriented
AI Analysis

This work addresses vehicle distribution inefficiencies for users and operators in sharing systems, but it is incremental as it builds on existing pricing strategies with limited assumptions.

The paper tackles the problem of optimizing vehicle distribution in sharing schemes with flexible drop-off locations by introducing a pricing model based on proximity to the nearest parked vehicle, and demonstrates that this approach achieves socially optimal vehicle spread under specific restrictive assumptions.

For vehicle sharing schemes, where drop-off positions are not fixed, we propose a pricing scheme, where the price depends in part on the distance between where a vehicle is being dropped off and where the closest shared vehicle is parked. Under certain restrictive assumptions, we show that this pricing leads to a socially optimal spread of the vehicles within a region.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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