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一种深度强化学习和图形卷积方法,以在街上停车搜索导航
Xiaohang Zhao1,2, Yangzhi Yan2,3
1School of Civil Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了多代理强化学习 (MARL) 框架,用于动态的停车分配,通过有效地解决实时需求和空间分布挑战来改善城市交通管理.
科学领域:
- 城市规划和交通管理
- 人工智能和机器学习
- 运营研究 运营研究
背景情况:
- 高效的停车位分配对于城市交通管理至关重要,但由于需求变化和空间差异,它面临着挑战.
- 目前的研究往往侧重于本地优化,忽视了大都市地区的实时分配复杂性.
- 关键问题包括动态供需失衡以及需要空间资源优化以提高系统性能和用户满意度.
研究的目的:
- 开发一个动态停车分配的新框架,以解决实时需求波动和空间效率低下的问题.
- 改善整体停车系统性能和复杂的城市环境中的用户满意度.
- 在管理可变停车需求方面克服静态分配解决方案的局限性.
主要方法:
- 一个多代理强化学习 (MARL) 框架,集成自适应优化和智能协作.
- 一个基于强化学习的时间决策机制,用于实时调整停车位分配.
- 一个基于图形神经网络 (GNN) 的空间模型来分析和优化相互停车关系.
主要成果:
- 在管理需求变化方面,MARL框架显著优于FIFO和SIRO等传统方法.
- 在优化停车资源分配方面取得了实质性的改进.
- 通过使用现实世界的数据,验证了框架在各种城市环境中的强度和灵活性.
结论:
- 拟议的MARL框架为城市环境中的动态停车位分配提供了有效的解决方案.
- 时间和空间优化模型的整合提高了停车场管理效率.
- 这种方法为改善城市交通流量和用户体验提供了强大且可适应的方法.
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