对全球节能路线规划的动态交通流量预测方法的审查
Pengyang Qi1,2, Chaofeng Pan1, Xing Xu1
1Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
动态交通流量预测和节能路线规划对于减少城市排放至关重要. 本研究通过开发一个框架来整合这些,该框架使用先进的模型来优化路线,平衡距离,时间和能源消耗.
科学领域:
- 智能运输系统 智能运输系统
- 交通工程是交通工程.
- 数据科学数据科学数据科学
背景情况:
- 城市化和越来越多的汽车所有权导致了显著的能源消耗和碳排放.
- 动态交通流量预测和节能路线规划对于减轻这些环境影响至关重要.
- 当前的研究往往孤立预测和规划,缺乏系统的模型比较,无法利用异构的数据集成.
研究的目的:
- 系统地审查和比较动态流量预测和节能路线规划的方法.
- 探索预测模型与路线规划算法的集成.
- 通过提出一个综合框架和分析未来的挑战来解决现有研究的局限性.
主要方法:
- 全球研究 (2020-2025) 关于交通预测和路线规划的系统文献综述.
- 分析统计,机器学习,深度学习和交通预测混合方法,评估LSTM和图形神经网络等模型.
- 开发一个综合框架,将实时交通预测与使用多目标成本函数的路径查找算法 (A*,Dijkstra) 连接起来.
主要成果:
- 确定了各种模型 (LSTM,GNN) 在捕捉时空交通动态方面的优势.
- 突出了异质图形神经网络在多源数据集成方面的未被充分探索的潜力.
- 证明了综合框架在为路线优化平衡距离,时间和能源消耗方面的有效性.
结论:
- 对动态交通流量预测和节能路线规划的综合方法对于可持续的智能交通系统至关重要.
- 进一步的研究应集中在异质图神经网络上,以增强数据融合和开发标准化评估平台.
- 解决数据质量,算法效率和多模式适应方面的挑战是未来进步的关键.
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