# Adaptive Optimisation of Ride-Pooling Personalised Fares in a Stochastic Framework

arXiv: https://arxiv.org/abs/2508.20723 · HTML: https://mlsanity.com/paper/2508.20723/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Quantifying the benefits of vehicle pooling with shareability networks](https://mlsanity.com/paper/1310.2963/) (2013)
- [Balancing Profit and Traveller Acceptance in Ride-Pooling Personalised Fares](https://mlsanity.com/paper/2411.03370/) (2024)

## Abstract (arXiv)

Ride-pooling systems, to succeed, must provide an attractive service, namely compensate perceived costs with an appealing price. However, because of a strong heterogeneity in a value-of-time, each traveller has his own acceptable price, unknown to the operator. Here, we show that individual acceptance levels can be learned by the operator (over 90% accuracy for pooled travellers in 10 days) to optimise personalised fares. We propose an adaptive pricing policy, where every day the operator constructs an offer that progressively meets travellers' expectations and attracts a growing demand. Our results suggest that operators, by learning behavioural traits of individual travellers, may improve performance not only for travellers (increased utility) but also for themselves (increased profit). Moreover, such knowledge allows the operator to remove inefficient pooled rides and focus on attractive and profitable combinations.