On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

@article{AlonsoMora2017OndemandHR,
  title={On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment},
  author={Javier Alonso-Mora and Samitha Samaranayake and Alex Wallar and Emilio Frazzoli and Daniela Rus},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2017},
  volume={114},
  pages={462 - 467}
}
  • Javier Alonso-Mora, S. Samaranayake, +2 authors D. Rus
  • Published 3 January 2017
  • Engineering, Computer Science, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
Significance Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi… 
Vehicle Dispatch in On-Demand Ride-Sharing with Stochastic Travel Times
  • Cheng Li, David Parker, Q. Hao
  • Computer Science
    2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2021
TLDR
A framework for dynamic vehicle dispatch that leverages stochastic travel time models to improve the performance of a fleet of shared vehicles is proposed and shown that by considering travel time uncertainty, ride-sharing service achieves higher service rate, reliability and profit.
Emerging Micro-Communities for Ride-Sharing Enabled Mobility-on-Demand Systems
TLDR
A multi-agent model is introduced that accounts for vehicles, riders and the MoD platform to enable riders to dynamically form emergent microcommunities that physically meet, wait and share a vehicle together for part of their trip.
Optimizing Vehicle Distributions and Fleet Sizes for Shared Mobility-on-Demand
TLDR
This paper presents an offline method to optimize the vehicle distributions and fleet sizes on historical demand data for MoD systems that allow passengers to share vehicles and presents an algorithm to determine how many vehicles are needed, where they should be initialized, and how theyShould be routed to service all the travel demand for a given period of time.
Heuristics for Improving Trip-Vehicle Fitness in On-demand Ride-Sharing Systems
TLDR
This work proposes an approach to reduce the processing time it takes to assign a trip request to a vehicle by developing a trip-vehicle fitness estimation framework that is flexible enough to utilize any fitness measure and is self-adjusting through feedback loops.
High-capacity ride-sharing via shortest path clustering on large road networks
TLDR
A clustering-based request matching and route planning algorithm called Roo is proposed whose basic operations are merging requested trips on road networks and which can save up to 50% of mileage by 1000 vehicles serving around 7000 trip requests in New York City between 7-40 and 8-00 am with an average waiting time of 4 minutes.
Highly Efficient and Scalable Multi-hop Ride-sharing
TLDR
This work proposes exact and approximation algorithms that are scalable and achieve real-time responses for highly dynamic ride-sharing scenarios in large metropolitan areas and demonstrates that the proposed algorithms are more than two orders of magnitude faster than the state-of-the-art.
Optimizing Vehicle Distributions and Fleet Sizes for Mobility-on-Demand
Mobility-on-demand (MoD) systems are revolutionizing urban transit with the introduction of ride-sharing. Such systems have the potential to reduce vehicle congestion and improve accessibility of a
Hierarchical data-driven vehicle dispatch and ride-sharing
TLDR
This work proposes a hierarchical framework to implement strategies that is capable of allocating vehicles to serve passengers in different locations based on limited supply and designs a dispatch strategy that is robust against passenger demand and vehicle mobility pattern uncertainties.
Efficient Algorithms for Stochastic Ridepooling Assignment with Mixed Fleets
TLDR
Two approximation algorithms for mid-capacity and high-capacity vehicles are proposed in this paper; the respective approximation ratios are 1 p2 and e−1 (2e+o(1))p lnp, where p is the maximum vehicle capacity plus one.
Multi-Objective Analysis of Ridesharing in Automated Mobility-on-Demand
TLDR
This paper quantifies the potential of ridesharing with a fleet of autonomous vehicles by considering all possible trade-offs between the quality of service and operation cost of the system that can be achieved by sharing rides.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 43 REFERENCES
The Value of Optimization in Dynamic Ride-Sharing: A Simulation Study in Metro Atlanta
TLDR
Simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems, and it appears that sustainable populations of dynamic ride- sharing participants may be possible even in relatively sprawling urban areas with many employment centers.
Shared-Vehicle Mobility-on-Demand Systems: A Fleet Operator's Guide to Rebalancing Empty Vehicles
The authors consider the operation of automated mobility-on-demand systems, whereby users share access to a fleet of self-driving vehicles. In these systems, rebalancing, the process by which the
Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility
Interest in vehicle automation has been growing in recent years, especially with the very visible Google car project. Although full automation is not yet a reality there has been significant research
A Branch-and-Cut Algorithm for the Dial-a-Ride Problem
  • J. Cordeau
  • Mathematics, Computer Science
    Oper. Res.
  • 2006
TLDR
A mixed-integer programming formulation of the dial-a-ride problem and a branch-and-cut algorithm used for the traveling salesman, the vehicle routing, and the pick-up and delivery problems are introduced.
Quantifying the benefits of vehicle pooling with shareability networks
TLDR
The notion of shareability network is introduced, which allows to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets, and demonstrates the feasibility of a shareable taxi service in New York City.
T-share: A large-scale dynamic taxi ridesharing service
TLDR
The dynamic ridesharing problem is formally defined, a large-scale taxi ridesh sharing service is proposed that efficiently serves real-time requests sent by taxi users and generates rideshared schedules that reduce the total travel distance significantly.
Fleet scheduling and dispatching for demand-responsive passenger services
Abstract This paper describes a software system designed to manage the deployment of a fleet of demand-responsive passenger vehicles such as taxis or variably routed buses. Multiple modes of
Empirical evaluation of a dynamic and distributed taxi-sharing system
TLDR
A large-scale and empirical evaluation of a dynamic and distributed taxi-sharing system that takes advantage of nowadays widespread availability of communication and computation to convey a cost-efficient, door-to-door and flexible system, offering a quality of service similar to traditional taxis.
Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore
TLDR
Using actual transportation data, this analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.
An Adaptive Variable Neighborhood Search Algorithm for a Vehicle Routing Problem Arising in Small Package Shipping
TLDR
An effective Variable Neighborhood Search algorithm based on the use of cyclic-exchange neighborhoods that incorporates an adaptive mechanism to bias the random shaking step is developed and successfully used to solve MDVRPPC.
...
1
2
3
4
5
...