通过高效的网络和优化的集体学习模型,提高城市交通拥堵预测
Ramesh Vatambeti1, N V RajaSekhar Reddy2, Shaik Hussain Shaik Ibrahim3
1Department of CSE, Tezpur University, Tezpur, Assam, 784028, India.
Scientific reports
|November 17, 2025
概括
这项研究引入了一种混合深度学习模型,用于准确预测交通流量,增强城市交通管理. 这种新的方法改善了拥堵级别的分类,并减少了智能城市的交通堵塞.
科学领域:
- 人工智能的人工智能
- 城市规划 城市规划
- 运输工程 运输工程
背景情况:
- 交通拥堵对城市基础设施和生活质量构成重大挑战.
- 准确的交通流量预测对于有效的交通管理策略至关重要.
- 现有的预测模型经常与城市交通模式的复杂性和活力作斗争.
研究的目的:
- 开发一种新的混合深度学习模型,用于增强流量预测.
- 提高实时交通管理系统的准确性和效率.
- 将交通流量严重程度分为不同的拥堵级别.
主要方法:
- 采用了一组长短期记忆 (LSTM),双向LSTM (BiLSTM) 和双向门式反复单元 (BiGRU) 模型.
- 使用EfficientNet进行了特征提取,超参数调整通过Eurygasters优化算法 (EOA) 进行了优化.
- 采用了比赛选择的光虫群优化 (TSGSO) 来进一步提高组合模型的性能. 模糊逻辑被用于交通流量严重程度的分类.
主要成果:
- 拟议的混合深度学习模型与现有方法相比,显示出更高的预测准确性.
- 用TSGSO优化的整体模型显著提高了流量预测能力.
- 模糊逻辑分类有效地将交通流量分为低,中和高拥堵级别.
- 该模型在保持合理的处理时间的同时实现了更高的准确性.
结论:
- 开发的混合深度学习方法为实时流量预测提供了可扩展和高效的解决方案.
- 这项研究有助于开发更聪明的城市,通过启用主动的交通管理和减少拥堵.
- 这些发现强调了先进的深度学习技术在解决复杂的城市交通挑战方面的潜力.
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