2026-10-21 チャルマース工科大学
＜関連情報＞
- https://news.cision.com/chalmers/r/driverless-electric-taxi-fleets-adapt-to-the-unexpected,c4405207
- https://www.sciencedirect.com/science/article/pii/S0968090X2600389X
- https://www.sciencedirect.com/science/article/pii/S2772424723000082
ネストされたベンダーズ分解を用いた、電気自動車によるオンデマンド自律移動のための確率的最適化とロバスト最適化の組み合わせ Combined stochastic and robust optimization for electric autonomous mobility-on-demand with nested Benders decomposition
Sten Elling Tingstad Jacobsen, Balázs Kulcsár, Anders Lindman
Transportation Research Part C: Emerging Technologies Available online: 12 August 2026
DOI:https://doi.org/10.1016/j.trc.2026.105903
Highlights
- Stochastic and robust control model manages demand, energy, time, and charging uncertainties.
- Bayesian neural networks generate multivariate travel and charging demand scenarios.
- Scenario reduction via soft clustering preserves key patterns of future uncertainty.
- Tailored Nested Benders decomposition efficiently solves the large-scale MILP model.
- Simulation results show reduced costs and improved reliability over baseline methods.
Abstract
The electrification and automation of mobility are reshaping how cities operate on-demand transport systems. Managing Electric Autonomous Mobility-on-Demand (EAMoD) fleets effectively requires coordinating dispatch, rebalancing, and charging decisions under multiple uncertainties, including travel demand, travel time, energy consumption, and charger availability. We address this challenge with a combined stochastic and robust model predictive control (MPC) framework. The framework integrates spatio-temporal Bayesian neural network forecasts with a multi-stage stochastic optimization model, formulated as a large-scale mixed-integer linear program. To ensure real-time applicability, we develop a tailored Nested Benders Decomposition that exploits the scenario tree structure and enables efficient parallelized solution. Stochastic optimization is employed to anticipate demand and infrastructure variability, while robust constraints on energy consumption and travel times safeguard feasibility under worst-case realizations. We evaluate the framework using high-fidelity simulations of San Francisco and Chicago. Compared with deterministic, reactive, and robust baselines, the combined stochastic and robust approach reduces median passenger waiting times by up to 36% and 95th-percentile delays by nearly 20%, while also lowering rebalancing distance by 27% and electricity costs by close to 35%. We also conduct a sensitivity analysis of battery size and vehicle efficiency, finding that energy-efficient vehicles maintain stable performance even with small batteries, whereas less efficient vehicles require larger batteries and greater infrastructure support. Our results emphasize the importance of jointly optimizing predictive control, vehicle capabilities, and infrastructure planning to enable scalable, cost-efficient EAMoD operations.
オンデマンドモビリティサービスに対する予測的な確率制約再調整アプローチ A predictive chance constraint rebalancing approach to mobility-on-demand services
Sten Elling Tingstad Jacobsen, Anders Lindman, Balázs Kulcsár
Communications in Transportation Research Available online: 19 July 2023
DOI:https://doi.org/10.1016/j.commtr.2023.100097
Abstract
This paper considers the problem of supply-demand imbalances in Mobility-on-Demand (MoD) services. These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high. To achieve this, we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing. More precisely, first travel demand is predicted using Gaussian Process Regression (GPR) which provides uncertainty bounds on the prediction. We then formulate a stochastic model predictive control (MPC) for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds. In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction, we employ a probabilistic constraining method with user-defined confidence interval, using Chance Constrained MPC (CCMPC). The benefits of the proposed method are twofold. First, travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework, allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability. Second, CCMPC can be relaxed into a Mixed-Integer-Linear-Program (MILP) and the MILP can be solved as a corresponding Linear-Program, which always admits an integral solution. Our transportation simulations show that by tuning the confidence bound on the chance constraint, close to optimal oracle performance can be achieved, with a median customer wait time reduction of 4% compared to using only the mean prediction of the GPR.
