Deep learning method for recognizing network-level road traffic state

doi:10.3785/j.issn.1008-973X.2025.05.021

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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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.

URL:

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

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

Tab.2 Evaluation of spatiotemporal unit clustering by different models

Fig.9 Distribution characteristic of corresponding traffic state indicator

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

Tab.4 Evaluation result of every traffic state identification model


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