Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

Core Concepts

Introducing a Multi-Agent Feudal Network (MFuN) framework to enhance coordination in dispatching decisions among agents for efficient vehicle resource allocation.

Abstract

The content discusses the challenges faced by intercity bus services and proposes a two-level framework using reinforcement learning. The upper level utilizes MFuN for fleet dispatching, while the lower level employs ALNS heuristic for vehicle routing. The study aims to improve system profit and order fulfillment ratio in intercity ride-pooling services within city clusters.

Introduction

Decline in traditional intercity bus services.
Rise of demand-responsive intercity ride-pooling.

Advantages of Ride-Pooling

Dynamic ride-hailing service.
Fully flexible routes for door-to-door service.
Allocation of transportation resources at city cluster level.

Challenges

Building effective fleet operations strategy.
Microscopic order-matching and vehicle routing complexities.

Proposed Framework

Two-level approach: Fleet dispatching and vehicle routing.
Upper level: MFuN for cooperative assignment of idle vehicles.
Lower level: ALNS heuristic for dynamic pooled-ride routing.

Literature Review

Existing methods in urban demand-responsive fleet management.

Modeling

Problem settings and modeling framework explained.

Solution Methodologies

Upper level: Multi-agent Feudal Networks structure described.

Stats

"Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances."
"The possible reasons for decline in intercity bus industry include increase in car ownership, infrastructure development, and changing travel behaviors."

Quotes

"The integrated development of city clusters has given rise to an increasing demand for intercity travel."
"The attractiveness of traditional intercity buses is diminishing due to heightened competition from alternative modes of intercity transportation."

Key Insights Distilled From

Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services

by Jinhua Si,Fa... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2307.06742.pdf

Deeper Inquiries

How can the proposed framework adapt to varying traffic conditions

The proposed framework can adapt to varying traffic conditions through the use of reinforcement learning and hierarchical modeling. The multi-agent feudal network (MFuN) structure allows for coordination among agents in fleet management, enabling them to adjust their actions based on real-time data and feedback. The manager module sets goals for the worker agents, guiding them towards optimal decisions in response to changing traffic patterns. By incorporating historical data and predictive analytics, the system can learn from past experiences and make informed choices in dynamic environments. Additionally, the adaptive large neighborhood search heuristic used in vehicle routing allows for flexibility in adjusting routes based on current traffic conditions, ensuring efficient navigation even as situations change.

What are potential drawbacks or limitations of using reinforcement learning in fleet management

While reinforcement learning offers several advantages in fleet management, there are potential drawbacks and limitations to consider. One limitation is the complexity of training models with multiple agents operating simultaneously. Coordinating actions among decentralized agents can lead to challenges such as non-stationarity during training or suboptimal convergence due to conflicting objectives. Another drawback is the need for extensive computational resources and time-consuming training processes when dealing with large-scale problems like fleet dispatching across a city cluster. Moreover, reinforcement learning models may struggle with generalization to unseen scenarios or unexpected events that deviate significantly from trained data.

How might advancements in autonomous vehicles impact the effectiveness of this approach

Advancements in autonomous vehicles could have a significant impact on the effectiveness of using reinforcement learning approaches in fleet management. Autonomous vehicles offer capabilities such as self-driving technology, real-time data processing, and adaptive decision-making algorithms that align well with the principles of reinforcement learning. These advancements could enhance route optimization, reduce human error factors affecting driving behavior consistency over time while adapting quickly to changing road conditions or demand fluctuations efficiently.