Explaining Ridesharing: Selection of Explanations for Increasing User Satisfaction

• URL: http://arxiv.org/abs/2105.12500v1
• Date: Wed, 26 May 2021 12:03:09 GMT
• Title: Explaining Ridesharing: Selection of Explanations for Increasing User Satisfaction
• Authors: David Zar, Noam Hazon, Amos Azaria
• Abstract summary: We develop an agent that provides explanations that will increase user satisfaction. A machine learning based agent selects the explanations that are most likely to increase user satisfaction. Using feedback from humans we show that our machine learning based agent outperforms the rational agent and an agent that randomly chooses explanations.
• Score: 10.86084463641286
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transportation services play a crucial part in the development of modern smart cities. In particular, on-demand ridesharing services, which group together passengers with similar itineraries, are already operating in several metropolitan areas. These services can be of significant social and environmental benefit, by reducing travel costs, road congestion and CO2 emissions. Unfortunately, despite their advantages, not many people opt to use these ridesharing services. We believe that increasing the user satisfaction from the service will cause more people to utilize it, which, in turn, will improve the quality of the service, such as the waiting time, cost, travel time, and service availability. One possible way for increasing user satisfaction is by providing appropriate explanations comparing the alternative modes of transportation, such as a private taxi ride and public transportation. For example, a passenger may be more satisfied from a shared-ride if she is told that a private taxi ride would have cost her 50% more. Therefore, the problem is to develop an agent that provides explanations that will increase the user satisfaction. We model our environment as a signaling game and show that a rational agent, which follows the perfect Bayesian equilibrium, must reveal all of the information regarding the possible alternatives to the passenger. In addition, we develop a machine learning based agent that, when given a shared-ride along with its possible alternatives, selects the explanations that are most likely to increase user satisfaction. Using feedback from humans we show that our machine learning based agent outperforms the rational agent and an agent that randomly chooses explanations, in terms of user satisfaction.

Related papers

• Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling [19.247162142334076]
  On-demand vehicle pooling services allow each vehicle to serve multiple passengers at a time. Existing algorithms often only consider revenue, which makes it difficult for requests with unusual distribution to get a ride. We propose a framework for dispatching for ride pooling tasks, which splits the city into discrete dispatching and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions.
  arXiv  Detail & Related papers  (2023-12-23T08:34:52Z)
• Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach [49.00137468773683]
  Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents. We propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning.
  arXiv  Detail & Related papers  (2023-10-26T15:42:29Z)
• Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing [49.00137468773683]
  Collaborative Vehicle Routing is where delivery companies cooperate by sharing their delivery information and performing delivery requests on behalf of each other. This achieves economies of scale and thus reduces cost, greenhouse gas emissions, and road congestion. But which company should partner with whom, and how much should each company be compensated? Traditional game theoretic solution concepts, such as the Shapley value or nucleolus, are difficult to calculate for the real-world problem of Collaborative Vehicle Routing.
  arXiv  Detail & Related papers  (2023-10-26T15:04:23Z)
• A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services [7.476901945542385]
  We propose GoTogether, a recommender system for car pooling services. GoTogether builds the list of recommended rides in order to maximise the success rate of the offered matches. To test the performance of our scheme we use real data from Twitter and Foursquare sources.
  arXiv  Detail & Related papers  (2023-07-06T09:25:38Z)
• Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
  Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
  arXiv  Detail & Related papers  (2023-04-23T16:01:36Z)
• A greedy approach for increased vehicle utilization in ridesharing networks [0.3480973072524161]
  ridesharing platforms have become a prominent mode of transportation for the residents of urban areas. We propose a k-hop-based sliding window approximation algorithm that reduces the search space from entire road network to a window. We evaluate our proposed model on real-world datasets and experimental results demonstrate superior performance by our proposed model.
  arXiv  Detail & Related papers  (2023-04-02T07:25:01Z)
• Short term prediction of demand for ride hailing services: A deep learning approach [8.61268901380738]
  This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.
  arXiv  Detail & Related papers  (2022-12-07T21:08:03Z)
• Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing [84.47891614815325]
  This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We introduce novel notions of fairness and stability in P2P ridesharing. Results suggest that fair and stable solutions can be obtained in reasonable computational times.
  arXiv  Detail & Related papers  (2021-10-04T02:14:49Z)
• Identifying synergies in private and public transportation [9.2148680438251]
  In a future with shared Autonomous Vehicles (AVs) providing cheap and efficient transportation services, such distinctions will blur. We show how exploiting existing parallels between individual bus and taxi trips in two Asian cities could lead to an increase in transportation service quality.
  arXiv  Detail & Related papers  (2020-09-21T07:53:40Z)
• Equitable and Optimal Transport with Multiple Agents [48.17429789586127]
  We introduce an extension of the Optimal Transport problem when multiple costs are involved. We aim to share equally between agents the work of transporting one distribution to another. Another point of view is when the goal is to partition equitably goods between agents according to their heterogeneous preferences.
  arXiv  Detail & Related papers  (2020-06-12T15:15:41Z)
• Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
  Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
  arXiv  Detail & Related papers  (2020-05-09T13:53:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.