# A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

arXiv: https://arxiv.org/abs/1901.00760 · HTML: https://mlsanity.com/paper/1901.00760/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework](https://mlsanity.com/paper/2501.14199/) (2025)
- [Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance](https://mlsanity.com/paper/2305.02209/) (2023)
- [A sequential transit network design algorithm with optimal learning under correlated beliefs](https://mlsanity.com/paper/2305.09452/) (2023)
- [Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learning](https://mlsanity.com/paper/2510.25796/) (2025)
- [Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks](https://mlsanity.com/paper/2606.23257/) (2026)

## Abstract (arXiv)

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted.