用量子模型预测共享单车需求，提前调度缓解拥堵
Quantum generative model on bicycle-sharing system and an application
- 用量子时间演化拟合骑行数据，捕捉站点间关联
- 模拟增车后系统总租赁量提升，验证调度效果
- 基于蒙特卡洛模拟，适合城市交通优化场景
近年来，共享单车系统在众多城市广泛应用，成为日常出行的重要组成部分。然而，通勤高峰时段常出现特定区域和时段的车辆短缺问题。为解决此挑战，我们采用一种新型量子机器学习模型，通过将量子时间演化拟合到观测序列，分析时间序列数据。该模型能够捕捉各站点自行车数量的实际变化趋势，并识别不同站点间的相关性。利用训练好的模型，我们模拟了在高需求站点提前投放自行车对整个系统租赁总量的影响。由于该方法的核心是蒙特卡洛模拟，预计具有广泛的工业应用前景。
原文摘要 · Abstract (English)
Recently, bicycle-sharing systems have been implemented in numerous cities, becoming integral to daily life. However, a prevalent issue arises when intensive commuting demand leads to bicycle shortages in specific areas and at particular times. To address this challenge, we employ a novel quantum machine learning model that analyzes time series data by fitting quantum time evolution to observed sequences. This model enables us to capture actual trends in bicycle counts at individual ports and identify correlations between different ports. Utilizing the trained model, we simulate the impact of proactively adding bicycles to high-demand ports on the overall rental number across the system. Given that the core of this method lies in a Monte Carlo simulation, it is anticipated to have a wide range of industrial applications.
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