论文标题
网络级别的空间暂时交通状态预测层次 - 层次 - lstm（hierattnlstm）
Network Level Spatial Temporal Traffic State Forecasting with Hierarchical-Attention-LSTM (HierAttnLSTM)
论文作者
论文摘要
流量状态数据，例如从无处不在的流量监控传感器收集的速度，数量和旅行时间，需要高级网络级别分析，以预测和识别重要的流量模式。本文从开放基准上托管的CALTRANS性能测量系统（PEMS）利用了各种交通状态数据集，与公认的时空模型相比，它具有有希望的性能。从各种人工智能（AI）任务中的层次结构成功中汲取灵感，我们将细胞和隐藏状态从低水平到高级长期短期记忆（LSTM）网络（与人类感知系统相似）相结合。开发的层次结构旨在说明跨不同时间尺度的依赖关系，从而捕获网络级流量状态的时空相关性，从而为所有走廊而不是单个链接或路线提供了交通状态的预测。通过消融研究分析了基于注意力的LSTM的效率。基线LSTM模型的比较结果表明，分层注意力LSTM（HIERATTNLSTM）模型不仅提供了更高的预测准确性，而且可以有效预测异常的拥塞模式。数据和代码可公开使用以支持可重复的科学研究。
Traffic state data, such as speed, volume and travel time collected from ubiquitous traffic monitoring sensors require advanced network level analytics for forecasting and identifying significant traffic patterns. This paper leverages diverse traffic state datasets from the Caltrans Performance Measurement System (PeMS) hosted on the open benchmark and achieved promising performance compared to well recognized spatial-temporal models. Drawing inspiration from the success of hierarchical architectures in various Artificial Intelligence (AI) tasks, we integrate cell and hidden states from low-level to high-level Long Short-Term Memory (LSTM) networks with an attention pooling mechanism, similar to human perception systems. The developed hierarchical structure is designed to account for dependencies across different time scales, capturing the spatial-temporal correlations of network-level traffic states, enabling the prediction of traffic states for all corridors rather than a single link or route. The efficiency of designed attention-based LSTM is analyzed by ablation study. Comparative results with baseline LSTM models demonstrate that the Hierarchical Attention LSTM (HierAttnLSTM) model not only provides higher prediction accuracy but also effectively forecasts unusual congestion patterns. Data and code are made publicly available to support reproducible scientific research.
