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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (5): 1083-1091    DOI: 10.3785/j.issn.1008-973X.2025.05.021
    
Deep learning method for recognizing network-level road traffic state
Yikai LUO1(),Yilin XIN2,Jinhua XU1,Guizhen CHEN1,Yan LI1,*()
1. College of Transportation Engineering, Chang’an University, Xi’an 710064, China
2. BYD Automobile Limited Company, Xi’an 710018, China
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Abstract  

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 0.982 1, 0.984 4, 0.983 3 and 0.983 9 respectively.

Key words： network-level road      traffic operation state      deep clustering      trajectory data      light gradient boosting machine     
Received: 27 February 2024      Published: 25 April 2025
CLC:  U 491  
Fund:  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）.
Corresponding Authors: Yan LI     E-mail: lyk@chd.edu.cn;lyan@chd.edu.cn
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Yikai LUO
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Cite this article:

Yikai LUO,Yilin XIN,Jinhua XU,Guizhen CHEN,Yan LI. Deep learning method for recognizing network-level road traffic state. Journal of ZheJiang University (Engineering Science), 2025, 59(5): 1083-1091.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.05.021     OR     https://www.zjujournals.com/eng/Y2025/V59/I5/1083


网络级道路交通运行状态的深度学习识别方法

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.


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 
Fig.1 Flowchart of network-level road traffic state recognition method
Fig.2 Flowchart of sliding window map matching algorithm with heading and distance constraint
Fig.3 Illustration of missing and anomalous trajectory point in ride-hailing data
Fig.4 Deep self-coding neural network based on LSTM
算法1　TS-DEC深度聚类
输入：经数据处理的高维时序数据
步骤：
//依据DEC算法中KL散度(Kullback-Leibler divergence, KL)的聚类方法
1）通过LSTM深度自编码神经网络，获取特征分布空间$ \boldsymbol{Z} $及特征映射$ {f}_{\theta } $.
2）采用K-means算法，确定$ \boldsymbol{Z} $中的初始聚类中心$ {\boldsymbol{\mu }}_{{j}}\left\{{\boldsymbol{\mu }}_{{j}}\in \boldsymbol{Z},j=1,2, \cdots ,k\right\} $.
3） while 总损失$ L $未趋于稳定 do
4）计算特征空间中提取的特征与聚类中心的软分配$ q $，使用学生t分布来衡量特征向量$ {\boldsymbol{z}}_{{i}} $与聚类中心$ {\boldsymbol{\mu }}_{{j}} $之间的相似性，采用$ {\boldsymbol{z}}_{{i}} $与$ {\boldsymbol{\mu }}_{{j}} $之间的归一化相似性进行软分配.
5）计算软分配分布$ q $与辅助目标分布$ p $之间的KL散度，将二者的KL散度作为聚类损失$ {L}_{{\mathrm{C}}} $，以KL散度最小化作为聚类目标.
6）模型的总损失$ L $由特征提取过程产生的重构误差$ {L}_{\mathrm{R}\mathrm{E}} $和聚类过程产生的聚类误差$ {L}_{{\mathrm{C}}} $组成.
7） 迭代更新模型.
8） end while
输出：交通状态特征矩阵、聚类类别
 
算法2　Bagging-Bo-LightGBM识别模型
输入：LightGBM超参数向量$ \boldsymbol{x}=[{\mathrm{num}}\_{\mathrm{leaves,max}}\_ {\mathrm{depth}},\eta ,{\mathrm{n}}\_ {\mathrm{estimators}}] $、训练数据集$ D $（包含提取后的特征和标签向量）
步骤：
//贝叶斯优化lightGBM超参数过程
1）初始化：超参数、贝叶斯网络结构、迭代次数$ T $.
2） 定义目标函数：准确率$ f\left(\boldsymbol{x}\right) $ .
3）定义超参数空间联合先验分布$ P\left(\boldsymbol{x}\right)={\prod }_{i=1}^{d}P\left({x}_{i}\right) $.
4）选择初始参数点$ ({x}_{1},{x}_{2}{,\cdots ,x}_{n}) $, 计算目标函数值.
5）使用高斯过程(Gaussian process, GP)拟合代理模型，$ \hat{f}\left(\boldsymbol{x}\right)\sim{{\mathrm{G}}}{{\mathrm{P}}}\left(m\left(\boldsymbol{x}\right),k\left(\boldsymbol{x},\bar{\boldsymbol{x}}\right)\right) $.
6） for $ t=\mathrm{1,2},\cdots ,T $ do
7） 依据高斯过程后验分布找到下一个参数点 $ {\boldsymbol{x}}_{\boldsymbol{n}+1} $.
8） $ {\boldsymbol{x}}_{\boldsymbol{n}+1}={\mathrm{argmax}}\;\left\{\mu \left(\boldsymbol{x}\right)+k\sigma \left(\boldsymbol{x}\right)\right\} $.
9） 使用$ {\boldsymbol{x}}_{\boldsymbol{n}+1} $计算目标函数$ f\left(\boldsymbol{x}\right) $值.
10） 更新代理模型.
11）end for
//在优化超参数的基础上，使用Bagging优化分类过程
12）初始化：子采样率、子模型数量$ m $.
13）for $ i=\mathrm{1,2}, \cdots ,m $ do
14） 对训练集$ D $中所有样本进行i次有放回的抽样，生成训练子集$ {D}_{i} $.
15） 在子训练集$ {D}_{i} $上训练基本模型$ {M}_{i} $.
16） end for
17）使用所有基本模型$ {M}_{i} $进行预测，并统计预测结果.
18）采用软投票(soft voting, SV)，对分类结果加权平均.
19）最终的分类结果$ {C}_{\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{l}} $可以表示为$ {C}_{\mathrm{fi}\mathrm{n}\mathrm{a}\mathrm{l}}={\mathrm{argmax}}_{{{C}}} \left(\sum _{i=1}^{m}{m}^{-1}{P}_{i}\left({{C}}\right)\right) $，其中$ {P}_{i}\left({{C}}\right) $为预测概率.
输出：Bagging和Bo共同优化后的LightGBM集成模型
 
Fig.5 Collection area of ride-hailing trajectory data
订单编号车辆编号经度/(°)纬度/(°)时间戳
79b55f7533……e14c06bccc0bcb8012……f81a5827108.9460134.25298153912798
79b55f7533……e14c06bccc0bcb8012……f81a5827108.9460234.25296153912801
79b55f7533……e14c06bccc0bcb8012……f81a5827108.9460734.25293153912804
79b55f7533……e14c06bccc0bcb8012……f81a5827108.9460834.25292153912807
79b55f7533……e14c06bccc0bcb8012……f81a5827108.9460834.25293153912811
Tab.1 Sample data of online car-hailing trajectory
Fig.6 Loss function of deep embedded clustering model
Fig.7 Number of cluster for spatio-temporal unit
Fig.8 Three-dimensional visualization of deep embedded clustering feature
模型$ {S}_{{\mathrm{c}}} $$ {C}_{{\mathrm{h}}} $
K-means0.492 265 713.11
FCM0.469 168 899.26
RF-K-means0.684 592 241.37
AE-K-means0.711 6117 002.29
TS-DEC0.886 3204 168.57
Tab.2 Evaluation of spatiotemporal unit clustering by different models
Fig.9 Distribution characteristic of corresponding traffic state indicator
类别等级运行状态
A4中度拥堵
B1畅通
C5严重拥堵
D3轻度拥堵
E2缓行
Tab.3 Quantified ranking result of traffic state
Fig.10 Distribution characteristic of time proportion by every level road
Fig.11 Global spatial distribution contour map of road traffic state
Fig.12 Confusion matrix of every traffic state identification model
分类模型PR$ {F}_{1-{\mathrm{score}}} $$ {A}_{{\mathrm{cc}}} $
SVM0.815 40.819 20.817 30.817 7
DT0.868 50.883 20.873 80.874 9
XGBoost0.908 30.919 60.913 60.915 4
所提模型0.982 10.984 40.983 30.983 9
Tab.4 Evaluation result of every traffic state identification model
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