Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance

Haoyang Chen, Peiyan Sun, Qiyuan Song, Wanyuan Wang, Weiwei Wu, Wencan Zhang, Guanyu Gao, Yan Lyu

TL;DR

This work tackles supply-demand imbalance in ride-hailing by accounting for driver heterogeneity and autonomy in accepting reposition recommendations. It introduces i-Rebalance, a sequential repositioning framework with dual DRL agents: Grid Agent to determine an optimal reposition order and Vehicle Agent to deliver personalized recommendations, trained through interaction with a driver-behavior simulator that includes a data-driven acceptance model. The approach, validated on a real Chengdu taxi dataset, improves driver acceptance by about 38% and total driver income by about 10% compared with baselines, while maintaining efficient action-space complexity. The combination of driver preference modeling and sequential learning yields a practical, driver-friendly method for improving supply-demand balance in dynamic ride-hailing markets.

Abstract

Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%.

i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance

TL;DR

This work tackles supply-demand imbalance in ride-hailing by accounting for driver heterogeneity and autonomy in accepting reposition recommendations. It introduces i-Rebalance, a sequential repositioning framework with dual DRL agents: Grid Agent to determine an optimal reposition order and Vehicle Agent to deliver personalized recommendations, trained through interaction with a driver-behavior simulator that includes a data-driven acceptance model. The approach, validated on a real Chengdu taxi dataset, improves driver acceptance by about 38% and total driver income by about 10% compared with baselines, while maintaining efficient action-space complexity. The combination of driver preference modeling and sequential learning yields a practical, driver-friendly method for improving supply-demand balance in dynamic ride-hailing markets.

Abstract

Ride-hailing platforms have been facing the challenge of balancing demand and supply. Existing vehicle reposition techniques often treat drivers as homogeneous agents and relocate them deterministically, assuming compliance with the reposition. In this paper, we consider a more realistic and driver-centric scenario where drivers have unique cruising preferences and can decide whether to take the recommendation or not on their own. We propose i-Rebalance, a personalized vehicle reposition technique with deep reinforcement learning (DRL). i-Rebalance estimates drivers' decisions on accepting reposition recommendations through an on-field user study involving 99 real drivers. To optimize supply-demand balance and enhance preference satisfaction simultaneously, i-Rebalance has a sequential reposition strategy with dual DRL agents: Grid Agent to determine the reposition order of idle vehicles, and Vehicle Agent to provide personalized recommendations to each vehicle in the pre-defined order. This sequential learning strategy facilitates more effective policy training within a smaller action space compared to traditional joint-action methods. Evaluation of real-world trajectory data shows that i-Rebalance improves driver acceptance rate by 38.07% and total driver income by 9.97%.
Paper Structure (23 sections, 1 theorem, 6 equations, 4 figures, 4 tables, 1 algorithm)

This paper contains 23 sections, 1 theorem, 6 equations, 4 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Statement
  4. System Overview
  5. Driver Behavior Modeling
  6. Driver Cruising Preference
  7. Driver Acceptance to Reposition Recommendation
  8. Sequential Reposition with Dual DRL Agents
  9. Order Determination by Grid Agent
  10. Personalized Recommendation by Vehicle Agent
  11. Reposition Algorithm
  12. Evaluation
  13. Experimental Settings
  14. Environment Simulator
  15. Datasets
  16. ...and 8 more sections

Key Result

Theorem 1

Given the state at the grid $g$, $S_G=\langle \Delta_{t+1},\rho_t^1,\rho_t^2,\dots,\rho_t^n\rangle$, the proposed sequential reposition framework learns reposition order and reposition policy can achieve the same reward with that of the optimal policy of assigning vehicles to locations simultaneousl

Figures (4)

  • Figure 1: Impact of recommendation order. a) Initial scenario: Recommending four idle vehicles in Grid 5. Each prefers two neighboring grids, rejecting non-preferred options to stay at the current grid. The supply-demand gap $\delta$ is color-coded: red signifies shortage, and green indicates excess idle vehicles. b) Two recommendation orders $C\succ D\succ B \succ A$ (top row) and $A\succ B\succ C\succ D$ (bottom row) lead to different supply-demand balances due to drivers’ diverse preferences.
  • Figure 2: Overview of i-Rebalance. i-Rebalance comprises two phases: 1) Driver Behavior Modeling simulates realistic driver decision-making by predicting their cruising preferences and reposition acceptance probabilities. 2) Sequential Vehicle Reposition with Dual DRL Agents interacts with the simulator. Grid Agent observes nearby supply-demand gap $\Delta$ and driver preference $\mathbf{\rho}$ and determines the repositioning order of idle vehicles within the grid, e.g., $C\succ D \succ B \succ A$. By this order, Vehicle Agent observes individual preference $\rho^i$ of driver $i$ and real-time updated supply-demand gap $\Delta^i$, and recommends reposition destinations for this vehicle. It receives rewards of supply-demand balance and preference satisfaction for each recommendation, while Grid Agent receives the average rewards after all recommendations.
  • Figure 3: Cruising preference prediction network. The network takes a sequence of the features including driver states $W_t$, POI distances $L_t$, traffics $T_t$, and supply-demand features $V_t$ from time $t-h+1$ to $t$ as input and predicts probabilities $\rho_t$ of the driver visiting the neighboring $3 \times 3$ grids.
  • Figure 4: Partial dependence plot of independent variables.

Theorems & Definitions (2)

  • Theorem 1
  • proof