.png){kind=icon}
.png){kind=icon}
Return
A smart predict-then-optimize framework for vehicle rebalancing problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.trb.2026.103411.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• A regional-level vehicle rebalancing model is proposed, incorporating operational constraints and supply–demand uncertainty. • A piecewise-linear response function is designed to capture the dynamic supply–demand conditions. • A Smart Predict-then-Optimize (SPO) framework is introduced to bridge predictive models and optimization layers. • The effectiveness of the proposed framework is demonstrated through numerical and simulator experiments on real-world ride-hailing data.
Keywords:
vehicle rebalancing
supply-demand uncertainty
piecewise-linear response function
Smart Predict-then-Optimize framework
ride-hailing data
Journal
T
IF:
0
Papers:
78
Citations:
0
Organization
View more organizations