Authors: Ali Aalipour, Alireza Khani

Published on: February 03, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.01985

Summary

• What is new: Introduces an optimized control approach using graph theory for vehicle scheduling and rebalancing in AMoD systems.
• Why this is important: Ensuring vehicles in autonomous mobility-on-demand systems are properly distributed to meet customer demand.
• What the research proposes: A model predictive control (MPC) framework that utilizes a linear, discrete-time model for optimal rebalancing and scheduling.
• Results: The proposed MPC framework outperforms other MPC-based and state-of-the-art algorithms across all evaluation criteria.

Technical Details

Technological frameworks used: Model Predictive Control (MPC)

Models used: Linear, discrete-time model of AMoD system

Data used: Real-world case study

Potential Impact

Autonomous vehicle manufacturers, mobility service providers, urban transportation planning

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