资源论文Balancing Efficiency and Fairness in On-Demand Ridesourcing

Balancing Efficiency and Fairness in On-Demand Ridesourcing

2020-02-20 | |  56 |   43 |   0

Abstract

We investigate the problem of assigning trip requests to available vehicles in ondemand ridesourcing. Much of the literature has focused on maximizing the total value of served requests, achieving efficiency on the passengers’ side. However, such solutions may result in some drivers being assigned to insufficient or undesired trips, therefore losing fairness from the drivers’ perspective. In this paper, we focus on both the system efficiency and the fairness among drivers and quantitatively analyze the tradeoffs between these two objectives. In particular, we give an explicit answer to the question of whether there always exists an assignment that achieves any target efficiency and fairness. We also propose a simple reassignment algorithm that can achieve any selected tradeoff. Finally, we demonstrate the effectiveness of the algorithms through extensive experiments on real-world datasets.

上一篇:State Aggregation Learning from Markov Transition Data

下一篇:Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization

用户评价
全部评价

热门资源

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Rating-Boosted La...

    The performance of a recommendation system reli...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...