A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2018.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2025Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Frameworkcited by 3
- 2023Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performancecited by 2
- 2023A sequential transit network design algorithm with optimal learning under correlated beliefscited by 1
- 2025Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learningcited by 0
- 2026Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networkscited by 0
- 2026Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks22 Jun 2026
- 2025Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learning28 Oct 2025
- 2025Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework24 Jan 2025
- 2023A sequential transit network design algorithm with optimal learning under correlated beliefs16 May 2023
- 2023Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance7 Apr 2023