# Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance

arXiv: https://arxiv.org/abs/2305.02209 · HTML: https://mlsanity.com/paper/2305.02209/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Quantifying the benefits of vehicle pooling with shareability networks](https://mlsanity.com/paper/1310.2963/) (2013)
- [Predictive Positioning and Quality Of Service Ridesharing for Campus Mobility On Demand Systems](https://mlsanity.com/paper/1609.08116/) (2016)
- [A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers](https://mlsanity.com/paper/1901.00760/) (2018)
- [Data Driven Validation Framework for Multi-agent Activity-based Models](https://mlsanity.com/paper/1502.07601/) (2015)
- [The Impact of Ridesharing in Mobility-on-Demand Systems: Simulation Case Study in Prague](https://mlsanity.com/paper/1807.03352/) (2018)
- [Impact of Mobility-on-Demand on Traffic Congestion: Simulation-based Study](https://mlsanity.com/paper/1708.02484/) (2017)

## Built on by (most cited first, 2 of 2)

- [Large-scale Ridesharing DARP Instances Based on Real Travel Demand](https://mlsanity.com/paper/2305.18859/) (2023)
- [Optimal Chaining of Vehicle Plans with Time Windows](https://mlsanity.com/paper/2401.02873/) (2024)

## Abstract (arXiv)

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20 % reduction in vehicle distance driven and 5 % lower average passenger travel delay compared to a system that uses insertion heuristic.