基于污染本体学的事件生成框架的设计,用于动态应用交通限制
David Eneko Ruiz de Gauna1, Luís Enrique Sánchez2, Almudena Ruiz-Iniesta1
1International University of La Rioja, Logroño, La Rioja, Spain.
PeerJ. Computer science
|September 14, 2023
概括
这项研究介绍了绿色城市的动态交通管理框架. 它通过将智能汽车数据与空气污染协议集成来优化路线,提高城市交通效率.
科学领域:
- 城市规划和环境科学
- 智能运输系统 智能运输系统
- 数据科学和人工智能数据科学和人工智能
背景情况:
- 城市交通造成的空气污染是一个主要的环境问题,促使城市政策专注于绿色城市倡议.
- 目前的交通政策正在努力整合智能车辆数据,以优化路线和控制污染.
- 车辆路由问题 (VRP) 对于城市环境中的车队优化至关重要.
研究的目的:
- 为了解决将智能汽车与空气污染协议联系起来,以实现最佳路线规划的挑战.
- 在城市环境政策的背景下,研究车辆路线问题 (VRP) 的重要性.
- 根据现实世界的数据,提出一个动态实施交通约束的框架.
主要方法:
- 使用马德里交通数据生成数据集,应用深度学习技术.
- 开发了一个本体模型和一个事件生成系统来分析交通,天气,空气质量和当地事件之间的相互依赖关系.
- 使用来自马德里的真实世界交通数据验证了拟议的框架.
主要成果:
- 确定了天气,空气质量,当地事件和驾驶员行为之间的显著相互依赖.
- 证明了深度学习在分析复杂的城市交通动态方面的有效性.
- 拟议的框架成功预测了变化,并动态重组了交通限制.
结论:
- 开发的框架通过动态适应环境和事件驱动因素,使城市交通系统更有效.
- 整合智能车辆数据和预测建模对于有效的城市空气污染管理至关重要.
- 该研究为旨在改善交通流量和减少环境影响的城市提供了可扩展的解决方案.
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