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A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

Pengbo Zhu, Isik Ilber Sirmatel, Giancarlo Ferrari-Trecate, Nikolas Geroliminis

TL;DR

The vehicle rebalancing problem is formulated as a coverage control problem for the deployment of a fleet of mobile agents for AMoD operation in urban areas and the results reveal the potential of the proposed method in improving service rates and decreasing passenger waiting times.

Abstract

As an emerging mode of urban transportation, Autonomous Mobility-on-Demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services. However, the spatiotemporal imbalances between mobility demand and supply may lead to inefficiencies and a low quality of service. Vehicle rebalancing (i.e., dispatching idle vehicles to high-demand areas), is a potential solution for efficient AMoD fleet management. In this paper, we formulate the vehicle rebalancing problem as a coverage control problem for the deployment of a fleet of mobile agents for AMoD operation in urban areas. Performance is demonstrated via microscopic simulations representing a large urban road network of Shenzhen, China. Results reveal the potential of the proposed method in improving service rates and decreasing passenger waiting times.

A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

TL;DR

The vehicle rebalancing problem is formulated as a coverage control problem for the deployment of a fleet of mobile agents for AMoD operation in urban areas and the results reveal the potential of the proposed method in improving service rates and decreasing passenger waiting times.

Abstract

As an emerging mode of urban transportation, Autonomous Mobility-on-Demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services. However, the spatiotemporal imbalances between mobility demand and supply may lead to inefficiencies and a low quality of service. Vehicle rebalancing (i.e., dispatching idle vehicles to high-demand areas), is a potential solution for efficient AMoD fleet management. In this paper, we formulate the vehicle rebalancing problem as a coverage control problem for the deployment of a fleet of mobile agents for AMoD operation in urban areas. Performance is demonstrated via microscopic simulations representing a large urban road network of Shenzhen, China. Results reveal the potential of the proposed method in improving service rates and decreasing passenger waiting times.
Paper Structure (25 sections, 29 equations, 13 figures, 3 tables, 2 algorithms)

This paper contains 25 sections, 29 equations, 13 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Mathematical Preliminaries
  3. Voronoi Partition
  4. Centroidal Voronoi Configuration
  5. The Centroidal Coverage Control Law
  6. Problem Formulation and Method Overview
  7. Coverage control for Vehicle Rebalancing (CVR)
  8. Method Implementation
  9. Experiment Setup and Simulation Results
  10. O-D imbalance
  11. Imbalance parameter
  12. Simulator Setup and Performance Metrics
  13. Results and Analysis
  14. Extension and Comparisons
  15. Problem Reformulation
  16. ...and 10 more sections

Figures (13)

  • Figure 1: Illustration of Voronoi partition.
  • Figure 2: Finite-state machine schematic of an autonomous taxi. ('Passenger' is abbreviated as 'Pax' for brevity.)
  • Figure 3: A sequence of screenshots of vehicle rebalancing with CVR in action. 'o' marks the current positions of the vehicles, while '+' indicates their rebalancing goal positions (i.e., Voronoi centroids). The trajectories of these vehicles are shown by dotted lines.
  • Figure 4: A snapshot of the simulator. The idle/empty, passenger-assigned, and passenger-carrying AVs are dots in blue, green, and red, respectively. A demo video is available on YouTube: https://youtu.be/JlBs0CfuJ_c.
  • Figure 5: Contour map of Estimated Gaussian mixture models for the trip origin and destination distributions. More orders begin/end at the darker colored/red areas.
  • ...and 8 more figures