网络级道路交通运行状态的深度学习识别方法  

Deep learning method for recognizing network-level road traffic state

作　　者：罗义凯 辛苡琳 徐金华 陈桂珍 李岩[1] LUO Yikai;XIN Yilin;XU Jinhua;CHEN Guizhen;LI Yan(College of Transportation Engineering,Chang’an University,Xi’an 710064,China;BYD Automobile Limited Company,Xi’an 710018,China)

机构地区：[1]长安大学运输工程学院,陕西西安710064 [2]比亚迪汽车有限公司,陕西西安710018

出　　处：《浙江大学学报(工学版)》2025年第5期1083-1091,共9页Journal of Zhejiang University:Engineering Science

基　　金：国家自然科学基金:资助项目(51408049);陕西省自然科学基础研究计划资助项目(2020JM-237)。

摘　　要：为了精准、实时、高效地掌握道路网各区域交通运行状态,基于网约车轨迹数据提取相关运行参数,对研究区域进行时空单元划分,构建将特征提取与聚类过程融合的深度聚类网络模型,对交通状态进行分类.对聚类结果量化获取类别标签,结合集成学习、贝叶斯优化和轻量梯度提升机,提出交通状态识别模型.西安市网约车数据测试的结果表明,道路运行状态可以分为畅通、缓行、轻度拥堵、中度拥堵和严重拥堵5种类型,严重拥堵路段占比在早晚高峰时段明显增加,平峰时段有所减少.所提聚类模型的效果均优于对比模型,交通状态识别模型计算的精确率、召回率、F1分数和准确率分别为0.982 1、0.984 4、0.983 3、0.983 9.The research area was divided into spatiotemporal units,and a deep clustering network model that integrated feature extraction and clustering process was constructed based on the trajectory data of online car-hailing to extract relevant operation parameters to identify traffic states in order to accurately,real-time and efficiently grasp the traffic operation state of various areas in the road network.The clustering results were quantified to obtain category labels,and a traffic state identification model was proposed combining integrated learning,Bayesian optimization and light gradient boosting machine.The test results of Xi'an online car-hailing data show that road operation states can be divided into 5 types:smooth,slow,mild congestion,moderate congestion and severe congestion.The proportion of severely congested road sections increases significantly during morning and evening peak periods and decreases during off-peak periods.The proposed clustering model performs better than the comparison models,with the precision,recall,F1-score and accuracy of the traffic state identification model being 0.9821,0.9844,0.9833 and 0.9839 respectively.

关 键 词：网络级道路 交通运行状态 深度聚类 轨迹数据 轻量梯度提升机 

分 类 号：U491[交通运输工程—交通运输规划与管理]