基于复杂网络和信息器的铁路运输的长期客流预测
Dekui Li1, Shubo Du2, Yuru Hou1
1College of Computer Science, Liaocheng University, Liaocheng 252000, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
准确的长期客流预测对于城市铁路运输至关重要. 一个优化的Informer模型,集成复杂的网络理论,显著提高预测准确性和运输规划的效率.
科学领域:
- 城市规划和交通科学 城市规划和交通科学
- 数据科学和人工智能数据科学和人工智能
- 网络理论和复杂系统理论.
背景情况:
- 城市化推动了铁路运输客流的增加,需要对运营效率和服务质量进行准确的长期预测.
- 由于复杂的网络结构和季节性等外部因素,预测乘客流量是复杂的.
- 现有的模型与大规模,复杂的运输数据和网络依赖性作斗争.
研究的目的:
- 开发一个优化的预测模型,对城市铁路运输客流进行预测.
- 通过使用复杂的网络理论结合站际影响来提高长期预测的准确性.
- 为城市轨道交通运输管理提供强大的决策支持工具.
主要方法:
- 为长期客流预测而开发了一个优化的Informer模型.
- 整合了复杂网络理论,以考虑相互连接的站点的影响.
- 该模型的性能与ARIMA,LSTM和变压器模型进行了比较.
主要成果:
- 优化的Informer模型在处理大规模,复杂的运输数据方面表现出卓越的性能.
- 与传统方法相比,该模型在长期客流预测方面取得了更高的准确性.
- 复杂网络理论的整合有效地捕获了网络依赖.
结论:
- 拟议的预测方法显著提高了长期客流预测的准确性和效率.
- 这种方法为城市铁路运输规划和管理提供了强大的决策支持.
- 优化的Informer模型代表了智能运输系统的重大进步.
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