首页|Hokkaido University Researchers Broaden Understanding of Machine Learning (Optim izing Shared E-Scooter Operations Under Demand Uncertainty: A Framework Integrat ing Machine Learning and Optimization Techniques)
Hokkaido University Researchers Broaden Understanding of Machine Learning (Optim izing Shared E-Scooter Operations Under Demand Uncertainty: A Framework Integrat ing Machine Learning and Optimization Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting from Hokkaido, Japan, by NewsRx jo urnalists, research stated, "The emergence of dockless shared e-scooters as a ne w form of shared micromobility offers a viable solution to specific urban transp ortation problems, including the first-mile-last-mile issue, parking constraints, and environmental emissions." The news reporters obtained a quote from the research from Hokkaido University: "However, this sharing service faces several challenges in daily operation, part icularly related to demand volatility, battery recharging, maintenance, and regu lations, owing to their trip and physical characteristics. Therefore, this study proposed a new data-driven rebalancing framework for dockless shared e-scooters that incorporates demand and variance prediction, and Monte Carlo sampling to s imulate the expected demand. Thus, demand uncertainty and the collection of low- battery and broken e-scooters were included in the rebalancing formulation to mi nimize user dissatisfaction and operating costs. Rebalancing optimization is an NP-hard problem; in this study, the small-size problem was solved using the inte ger linear programming (ILP) solver GNU Linear Programming Kit, and the large-si ze problem was solved using the proposed hybrid ant colony optimization-ILP algo rithm (ACO-ILP)."
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