同济大学学报（自然科学版）2025，Vol.53Issue(3)：368-379,12.DOI:10.11908/j.issn.0253-374x.23235
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
张锋鑫 1陈思曲 2徐大林 3唐克双 4张政3
作者信息
- 1. 同济大学 道路与交通工程教育部重点实验室,上海 201804||连云港杰瑞电子有限公司,江苏 连云港 222061
- 2. 厦门市国土空间和交通研究中心 厦门规划展览馆,福建 厦门 361012
- 3. 江苏自动化研究所,江苏 连云港 222061
- 4. 同济大学 道路与交通工程教育部重点实验室,上海 201804
- 折叠
摘要
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.关键词
交通工程/城市快速路/交通拥堵/车辆路径/溯源/深度学习Key words
traffic engineering/urban expressway/traffic congestion/vehicle path/tracing/deep learning分类
交通运输引用本文复制引用
张锋鑫,陈思曲,徐大林,唐克双,张政..基于深度学习的城市快速路交通拥堵点段车辆路径溯源[J].同济大学学报（自然科学版）,2025,53(3):368-379,12.
