关于如何将情境背景的公共来源纳入交通数据的描述和预测模型
Sofia Cerqueira1, Elisabete Arsenio2, Rui Henriques3
1LNEC I.P. & INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
概括
背景数据显著改善了城市交通分析和预测. 这项研究引入了一种新的方法来整合局势上下文,增强像里斯本这样的欧洲城市的公共交通规划.
科学领域:
- 城市规划和流动性
- 数据科学和数据分析
- 运输工程 运输工程 运输工程
背景情况:
- 欧洲城市越来越多地使用城市数据来优化公共交通.
- 当前的交通分析往往缺乏关键的情境背景 (事件,天气等). ) 的情况.
- 背景数据往往是私有的,分散的或无法用于城市移动管理的.
研究的目的:
- 开发一种方法来将历史和未来的情境背景纳入城市交通数据模型.
- 为了应对整合异质上下文来源以改进交通分析的挑战.
- 提高城市交通动态的描述性和预测性建模.
主要方法:
- 提出数据科学驱动的方法论,将情境背景整合到交通数据模型中.
- 专注于地理参考时间序列,原点-目的地张量和原始交通事件数据.
- 制定在线整合和标记异质上下文数据源的原则.
主要成果:
- 证明了历史和前景背景对于城市流动性分析和预测的重要性.
- 量化了背景因素 (天气,事件等) 的影响. 关于里斯本公共交通数据.
- 使用公共汽车,地铁和自行车共享数据验证了方法.
结论:
- 拟议的方法提供了一种新的方法,用于上下文丰富的交通数据分析.
- 里斯本案例研究显示了整合上下文数据的实际应用和好处.
- 这项研究为其他欧洲城市提供了一份路线图,以改善城市流动性规划.
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