A Collaborative Multiagent Taxi-Dispatch System

Seow, Kiam Tian; Dang, Nam Hai; Lee, Der-Horng

doi:10.1109/tase.2009.2028577

Cited by 160 publications

(22 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study by Bischoff and Maciejewski (2016) uses a variant of this assignment heuristic that accounts for whether demand exceeds supply (or vice versa), as well as vehicles' locations within the service region. Seow et al (2009) propose a decentralized heuristic that gathers multiple requests and allows vehicles to negotiate with one another to determine which vehicles serve the new requests. They show that it outperforms heuristics like those in Lee et al (2004) and Bischoff and Maciejewski (2016).…”

Section: Related Literaturementioning

confidence: 99%

Dynamic Ride-Hailing with Electric Vehicles

Kullman

Cousineau

Goodson

et al. 2022

Transportation Science

View full text Add to dashboard Cite

We consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests. To solve this problem, we employ deep reinforcement learning, developing policies whose decision making uses [Formula: see text]-value approximations learned by deep neural networks. We compare these policies against a reoptimization-based policy and against dual bounds on the value of an optimal policy, including the value of an optimal policy with perfect information, which we establish using a Benders-based decomposition. We assess performance on instances derived from real data for the island of Manhattan in New York City. We find that, across instances of varying size, our best policy trained with deep reinforcement learning outperforms the reoptimization approach. We also provide evidence that this policy may be effectively scaled and deployed on larger instances without retraining.

show abstract

Section: Related Literaturementioning

confidence: 99%

Dynamic Ride-Hailing with Electric Vehicles

Kullman

Cousineau

Goodson

et al. 2022

Transportation Science

View full text Add to dashboard Cite

show abstract

“…A lot of previous work models the taxi dispatching into multi-agent learning, like [1], it divides the city into many dispatching areas, and regards an area as an agent, then uses self-organization techniques to decrease the total waiting time and increase the taxi utilization. NTuCab [27] is a collaborative multi-agent taxi dispatching system which a empts to increase custom satisfaction more globally, and it can dispatch multiple orders to taxis in the same geographical regions. NTuCab thinks that it is not feasible to compute the shortest-time path for each of a possibly large number of available taxis nearby a customer location since it is computationally costly.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching

Zhou

Jin

Zhang

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Improving the e ciency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems. Most of the existing solutions for order-dispatching are centralized controlling, which require to consider all possible matches between available orders and vehicles. For large-scale ride-sharing platforms, there are thousands of vehicles and orders to be matched at every second which is of very high computational cost. In this paper, we propose a decentralized execution order-dispatching method based on multi-agent reinforcement learning to address the large-scale order-dispatching problem. Di erent from the previous cooperative multi-agent reinforcement learning algorithms, in our method, all agents work independently with the guidance from an evaluation of the joint policy since there is no need for communication or explicit cooperation between agents. Furthermore, we use KL-divergence optimization at each time step to speed up the learning process and to balance the vehicles (supply) and orders (demand). Experiments on both the explanatory environment and real-world simulator show that the proposed method outperforms the baselines in terms of accumulated driver income (ADI) and Order Response Rate (ORR) in various tra c environments. Besides, with the support of the online platform of Didi Chuxing, we designed a hybrid system to deploy our model. CCS CONCEPTS•Computing methodologies → Multi-agent reinforcement learning; • eory of computation → Multi-agent reinforcement learning; •Applied computing → Transportation;

show abstract

“…Indeed, vehicle routing from a central depot is a classical operations research problem [2]- [4]. Recent studies have taken advantage of real-time taxi information to fit system models and minimize passengers' waiting times and vehicle cruising times [5]- [8]. For instance, Miao et.al [1], [9] designed a Receding Horizon Control (RHC) framework, which incorporates a demand/supply model and real-time GPS location and occupancy information.…”

Section: A Related Workmentioning

confidence: 99%

MOVI: A Model-Free Approach to Dynamic Fleet Management

Oda

Joe‐Wong

2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of uncertainty, including vehicles' travel times to their dispatched locations, as well as coordinating between vehicles so that they do not attempt to pick up the same passenger. While prior works have developed models for this uncertainty and used them to optimize dispatch policies, in this work we introduce a model-free approach. Specifically, we propose MOVI, a Deep Qnetwork (DQN)-based framework that directly learns the optimal vehicle dispatch policy. Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles. We then formulate a centralized receding-horizon control (RHC) policy to compare with our DQN policies. To compare these policies, we design and build MOVI as a large-scale realistic simulator based on 15 million taxi trip records that simulates policy-agnostic responses to dispatch decisions. We show that the DQN dispatch policy reduces the number of unserviced requests by 76% compared to without dispatch and 20% compared to the RHC approach, emphasizing the benefits of a model-free approach and suggesting that there is limited value to coordinating vehicle actions. This finding may help to explain the success of ridesharing platforms, for which drivers make individual decisions.

show abstract

A Collaborative Multiagent Taxi-Dispatch System

Cited by 160 publications

References 21 publications

Dynamic Ride-Hailing with Electric Vehicles

Dynamic Ride-Hailing with Electric Vehicles

Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching

MOVI: A Model-Free Approach to Dynamic Fleet Management

Contact Info

Product

Resources

About