Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning

• URL: http://arxiv.org/abs/2008.05890v2
• Date: Tue, 13 Oct 2020 20:40:08 GMT
• Title: Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning
• Authors: Jiyao Li, Vicki H. Allan
• Abstract summary: We study the challenging problem of how to balance taxi distribution across a city in a dynamic ridesharing service. We propose a hybrid solution involving a series of algorithms: the Correlated Pooling collects correlated rider requests, the Adjacency Ride-Matching based on Demand Learning assigns taxis to riders, and the Greedy Idle Movement aims to direct taxis without a current assignment to the areas with riders in need of service.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the challenging problem of how to balance taxi distribution across a city in a dynamic ridesharing service. First, we introduce the architecture of the dynamic ridesharing system and formally define the performance metrics indicating the efficiency of the system. Then, we propose a hybrid solution involving a series of algorithms: the Correlated Pooling collects correlated rider requests, the Adjacency Ride-Matching based on Demand Learning assigns taxis to riders and balances taxi distribution locally, the Greedy Idle Movement aims to direct taxis without a current assignment to the areas with riders in need of service. In the experiment, we apply city-scale data sets from the city of Chicago and complete a case study analyzing the threshold of correlated rider requests and the average online running time of each algorithm. We also compare our hybrid solution with multiple other methods. The results of our experiment show that our hybrid solution improves customer serving rate without increasing the number of taxis in operation, allows both drivers to earn more and riders to save more per trip, and all with a small increase in calling and extra trip time.

Related papers

• GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching. This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• 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)
• 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)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms [57.21078336887961]
  Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day. We propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
  arXiv  Detail & Related papers  (2021-05-18T19:22:24Z)
• H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch [9.35511513240868]
  H-TD2 is a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynamic urban environment. We derive a regret bound and design the trigger condition between the two behaviors to explicitly control the trade-off between computational complexity and the individual taxi policy's bounded sub-optimality. Unlike recent reinforcement learning dispatch methods, this policy estimation is adaptive and robust to out-of-training domain events.
  arXiv  Detail & Related papers  (2021-05-05T15:42:31Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network [1.599072005190786]
  We formulate the problem of passenger-vehicle matching in a sparsely connected graph. We propose an algorithm to derive an equilibrium policy in a multi-agent environment.
  arXiv  Detail & Related papers  (2021-02-13T03:18:44Z)
• Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning [79.61517670541863]
  How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS) We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
  arXiv  Detail & Related papers  (2021-01-19T03:09:51Z)
• A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning [32.0512015286512]
  We present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework. Our framework is validated using the New York City Taxi dataset. Experimental results show the effectiveness of our approach in real-time and large scale settings.
  arXiv  Detail & Related papers  (2020-10-05T03:13:47Z)

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.