基于深度学习的城市快速路交通拥堵点段车辆路径溯源

Vehicle Path Tracing of Traffic Congestion Points and Sections on Urban Expressways Based on Deep Learning

摘要:

为突破既有研究将交通拥堵溯源问题简化为路径流量估计或拥堵关联分析的局限，构建一个更全面有效的城市快速路交通拥堵点段车辆路径溯源体系，以路径为基本分析单元，构建融合路径流量估计与拥堵关联分析的统一框架，并提出基于路径的可变形卷积长短期记忆神经网络（RSDC-LSTM）方法。该模型包含3个核心模块：基于历史路径流量数据与短时预测数据构建路径状态特征集；通过多路径卷积长短期记忆网络与软注意力机制的协同建模，量化各路径对交通拥堵的动态影响权重；采用可变形卷积神经网络捕捉拥堵点段的空间拓扑关联特征，实现时空双维度的路径重要性评估。实证研究表明，RSDC-LSTM 能有效识别交通拥堵关键路径并建立影响度排序。通过对前10%高影响路径实施调控，可实现行程速度峰值提升23.36%，停车次数与延误时间最大降幅分别达 29.41% 与 43.82%。RSDC-LSTM 方法为动态交通管控策略制定提供了可量化的决策依据，有助于提升城市快速路的交通运行效率。

Abstract:

This paper aims to overcome the limitations of existing research that simplifies the traffic congestion source-tracing problem into path flow estimation or congestion correlation analysis. It proposes a more comprehensive and effective system for tracing vehicle paths in traffic-congested sections of urban expressways. Using the path as the basic analysis unit， it develops an innovative unified framework integrating both path flow estimation and congestion correlation analysis. Additionally， it proposes a method based on the route-based deformable convolution long short-term memory neural network （RSDC-LSTM）. The model consists of three core modules： constructing a path state feature set based on historical path flow data and short-term prediction data； quantifying the dynamic influence weights of each path on traffic congestion through a collaborative modeling of the multi-path convolutional long short-term memory network and the soft-attention mechanism； and using the deformable convolutional neural network to capture the spatial-topological correlation features of congested sections and achieve the evaluation of path importance in both spatial and temporal dimensions. Empirical research shows that RSDC-LSTM effectively identifies key paths of traffic congestion and ranks their influence. By regulating the top 10% of high-influence paths， peak travel speeds can be increased by 23.36%， while the number of stops and delay time can be reduced by up to 29.41% and 43.82% respectively. The RSDC-LSTM method proposed provides a quantifiable decision-making framework for developing dynamic traffic control strategies and contributes to improving the traffic operation efficiency of urban expressways.