# Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility

arXiv: https://arxiv.org/abs/2503.08472 · HTML: https://mlsanity.com/paper/2503.08472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Large Scale Real-time Ridesharing with Service Guarantee on Road Networks](https://mlsanity.com/paper/1302.6666/) (2013)

## Built on by (most cited first, 1 of 1)

- [One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms](https://mlsanity.com/paper/2507.15351/) (2025)

## Abstract (arXiv)

The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such as pickup delays, detour limits, and vehicle capacity. Most existing RMP solutions assume passengers are picked up and dropped off at their original locations, neglecting the potential for passengers to walk to nearby spots to meet vehicles. This assumption restricts the optimization potential in ride-pooling operations. In this paper, we propose a novel matching method that incorporates extended pickup and drop-off areas for passengers. We first design a tree-based approach to efficiently generate feasible matches between passengers and vehicles. Next, we optimize vehicle routes to cover all designated pickup and drop-off locations while minimizing total travel distance. Finally, we employ dynamic assignment strategies to achieve optimal matching outcomes. Experiments on city-scale taxi datasets demonstrate that our method improves the number of served requests by up to 13% and average travel distance by up to 21% compared to leading existing solutions, underscoring the potential of leveraging passenger mobility to significantly enhance ride-pooling service efficiency.