综合移动和运输解决方案的数据源和模型
Pierfrancesco Bellini1, Stefano Bilotta1, Enrico Collini1
1DISIT Lab, University of Florence, 50139 Florence, Italy.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本文回顾了移动数据模型和标准,强调了它们的复杂性和整合潜力. 它探索使用这些模型进行城市交通管理和规划,利用Snap4City平台.
科学领域:
- 运输科学 运输科学
- 数据管理数据管理
- 城市规划 城市规划
背景情况:
- 在移动和运输领域数据源和模型的扩散导致了整合和管理的复杂性.
- 现有的数据模型经常重叠,并且可以在类似的创新解决方案中互换使用.
- 这种复杂性阻碍了智能城市应用程序对数据的有效利用.
研究的目的:
- 提供移动领域的数据模型和标准及其相互关系的概述.
- 探索这些数据模型对城市运输管理的潜在利用.
- 研究数据模型在基础设施规划的料模拟和优化过程中的使用.
主要方法:
- 对运输行业现有数据模型和标准的文献综述.
- 对数据模型重叠的分析和替代利用的可能性.
- 在Snap4City平台的运营过程中应用案例研究.
主要成果:
- 确定了各种移动数据模型之间显著的复杂性和重叠.
- 证明了使用数据模型用于运营城市交通管理的可行性.
- 展示了用于模拟,优化和规划过程的数据模型的集成.
结论:
- 移动数据模型的标准化和整合对于高效的智能城市解决方案至关重要.
- Snap4City平台为增强运输管理提供了各种数据模型的利用.
- 有效的数据模型利用支持城市交通基础设施的战术控制和战略规划.
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