考虑交通状态影响的服务区凝聚层次聚类入区判别模型

doi:10.3969/j.issn.1674-0696.2024.03.10

重庆交通大学学报（自然科学版） ›› 2024, Vol. 43 ›› Issue (3): 84-91.DOI: 10.3969/j.issn.1674-0696.2024.03.10

• 交通+大数据人工智能 • 上一篇

考虑交通状态影响的服务区凝聚层次聚类入区判别模型

章玉1，张婷婷2，姚成北2，曹鹏超2

(1. 中铁长江交通设计集团有限公司，重庆 401121； 2. 重庆交通大学交通运输学院，重庆 400074)

收稿日期:2023-05-04 修回日期:2023-10-08 发布日期:2024-03-21
作者简介:章玉（1985—），男，湖北宜昌人，正高级工程师，博士，主要从事智能交通与大数据分析方面的工作。E-mail：107615320@qq.com 通信作者：张婷婷（2001—），女，河南许昌人，硕士，主要从事智能交通与大数据分析方面的研究。E-mail:1504394628@qq.com
基金资助:
重庆市科技局面上项目（CSTB2022TIAD-GPX0024）

An Identification Model of Entering the Service Area of Agglomerative Hierarchical Clustering Considering the Influence of Traffic Status

ZHANG Yu1, ZHANG Tingting2, YAO Chengbei2, CAO Pengchao2

(1. China Railway Changjiang Traffic Design Group Co., Ltd., Chongqing 401121, China; 2. School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

Received:2023-05-04 Revised:2023-10-08 Published:2024-03-21

摘要/Abstract

摘要： 为了精准掌握高速公路服务区入区车辆特征、提升服务区运营管理水平，基于高速公路ETC门架通行和收费数据，在分析服务区路段和邻近服务区路段车辆行程时间和速度分布特征基础上，考虑路段交通运行状态影响，提出了基于凝聚层次聚类的运行状态识别方法和服务区分车型入区判别模型。以G65包茂高速大观服务区为例，通过关联上、下游门架路段交通运行状态，明确了服务区路段车辆在4种不同运行状态下的速度概率分布特性，结合聚类给出了各个运行状态下车流密度和速度变化的入区判定条件，并利用服务区视频卡口数据进行验证分析。结果表明：判别误差主要分布在拥堵时段，全日客车和货车在考虑运行状态下的相对误差分别为1.5%、7.0%，与不考虑路段运行状态情况相比分别提高了2.9%、4.1%，验证了模型的有效性，为获取高速公路服务区入区车辆特征提供了一种新的思路。

关键词: 交通工程；高速公路服务区；入区车辆判别；凝聚层次聚类；ETC数据

Abstract: To accurately grasp the characteristics of vehicles entering the expressway service area and improve the operation management level of the service area, the vehicle travel time and speed distribution characteristics of the sections in the service area and adjacent service area were analyzed based on the highway ETC gantry traffic and toll data. Considering the influence of traffic operation state in section, a method for identifying the operation state based on the agglomeration hierarchical clustering and an identification model for judging vehicles entering service area were proposed. Taking the Daguan service area on the G65 Bao-Mao highway as an example, firstly, the speed probability distribution characteristics of vehicles in the service area section under four different operation states were clarified by correlating the traffic states of the upstream and downstream gantry sections. Then, the judging criteria of entering the service area based on the change of traffic density and speed in various operating states were given by clustering algorithm. Finally, the verification and analysis were carried out by using the video bayonet data in the service area. The results show that the identification errors are mainly distributed in the congestion period, and the relative errors of all-day passenger cars and trucks with considering the operation states are 1.5% and 7% respectively, which are 2.9% and 4.1% higher than those without considering the operation states. The effectiveness of the proposed model is verified, which provides a new way to obtain the characteristics of vehicles entering the expressway service area.

Key words: traffic engineering; expressway service area; identification of vehicles entering service area; agglomerative hierarchical clustering; ETC data

中图分类号:

U412.366

章玉1，张婷婷2，姚成北2，曹鹏超2. 考虑交通状态影响的服务区凝聚层次聚类入区判别模型[J]. 重庆交通大学学报（自然科学版）, 2024, 43(3): 84-91.

ZHANG Yu1, ZHANG Tingting2, YAO Chengbei2, CAO Pengchao2. An Identification Model of Entering the Service Area of Agglomerative Hierarchical Clustering Considering the Influence of Traffic Status[J]. Journal of Chongqing Jiaotong University(Natural Science), 2024, 43(3): 84-91.

参考文献

［1］刘亚非, 陈军, 刘生涛. 陕西省高速公路服务区驶入率调查与预测研究［J］. 公路工程, 2013, 38(4): 210-213.
LIU Yafei, CHEN Jun, LIU Shengtao. Study on the entering rate research and forecasting of Shaanxi province expressway service area［J］. Highway Engineering, 2013, 38(4): 210-213.
［2］刘长明, 杨柳, 陶正文, 等. 高速公路开放式服务区规模测算研究［J］. 公路, 2022, 67(10): 278-284.
LIU Changming, YANG Liu, TAO Zhengwen, et al. Research on the calculation of scale of open service area［J］. Highway, 2022, 67(10): 278-284.
［3］王建伟, 汤毅. 高速公路服务区驶入率的交通势测度模型［J］. 中国公路学报, 2008, 21(5): 109-114.
WANG Jianwei, TANG Yi. Transportation potential calculation model of pause rate in expressway service area［J］.China Journal of Highway and Transport, 2008, 21(5): 109-114.
［4］李霞, 李霖, 崔洪军, 等. 基于驶入率的服务区合理用地量计算模型［J］. 重庆交通大学学报(自然科学版), 2016, 35(6): 127-130.
LI Xia, LI Lin, CUI Hongjun, et al. Calculation model of service areas land based on driving-into rate［J］. Journal of Chongqing Jiaotong University (Natural Science), 2016, 35(6): 127-130.
［5］ SHEN Xiaoyan, ZHANG Fan, LYU Huitao, et al. Prediction of entering percentage into expressway service areas based on wavelet neural networks and genetic algorithms［J］. IEEE Access, 2019, 7: 54562-54574.
［6］刘嘉, 沈小燕, 刘浩学. 基于BP神经网络的高速公路服务区车辆驶入率预测［J］. 公路, 2012, 57(6): 164-168.
LIU Jia, SHEN Xiaoyan, LIU Haoxue. Prediction model for percen-tage of expressway traffic entering rest area based on BP neural network［J］. Highway, 2012, 57(6): 164-168.
［7］ CAI Qiqin, YI Dingrong, ZOU Fumin, et al. Recognition of vehicles entering expressway service areas and estimation of dwell time using ETC data［J］. Entropy, 2022, 24(9): 1208.
［8］刘光雄. 基于ETC大数据的高速公路服务区入区率研究［J］. 中国交通信息化, 2021(5): 119-121.
LIU Guangxiong. Study on the entry rate of expressway service area based on ETC big data［J］. China ITS Journal, 2021(5): 119-121.
［9］ LIAO Lüchao, LI Zhengrong, LAI Shukun, et al. An expressway traffic congestion measurement under the influence of service areas［J］. PLoS One, 2023, 18(1): e0279966.
［10］刘群, 杨濯丞, 蔡蕾. 基于ETC门架数据的高速公路短时交通流预测［J］. 公路交通科技, 2022, 39(4): 123-130.
LIU Qun, YANG Zhuocheng, CAI Lei. Predicting short-term traffic flow on expressway based on ETC gantry system data［J］. Journal of Highway and Transportation Research and Development, 2022, 39(4): 123-130.
［11］林豪, 江竹, 李树彬. 基于机器学习的城市快速路速度-密度关系模型［J］. 西安科技大学学报, 2020, 40(6): 1109-1116.
LIN Hao, JIANG Zhu, LI Shubin. Speed-density relationship model of urban expressway based on machine learning［J］. Journal of Xian University of Science and Technology, 2020, 40(6): 1109-1116.
［12］翁剑成, 王媛, 袁荣亮, 等. 基于ETC交易数据的高速公路行程速度提取模型［J］. 交通信息与安全, 2017, 35(5): 76-82.
WENG Jiancheng, WANG Yuan, YUAN Rongliang, et al. A model of travel speed calculation for freeway road segments with ETC transaction data［J］. Journal of Transport Information and Safety, 2017, 35(5): 76-82.
［13］胡郁葱, 吴昊, 黄靖翔, 等. 基于支持向量回归的高速公路路段平均速度估计方法［J］. 公路交通科技, 2019, 36(3): 137-143.
HU Yucong, WU Hao, HUANG Jingxiang, et al. A method for estimating expressway section average speed based on support vector regression［J］. Journal of Highway and Transportation Research and Development, 2019, 36(3): 137-143.
［14］黄艳国, 罗云鹏. 基于改进模糊C均值聚类算法的城市道路状态判别方法［J］. 科学技术与工程, 2018, 18(9): 335-342.
HUANG Yanguo, LUO Yunpeng. Identification method of urban road condition based on improved fuzzy C-means method clustering algorithm［J］. Science Technology and Engineering, 2018, 18(9): 335-342.

[1]	姬杨蓓蓓，周金凤. 基于成本分析的高速公路收费站车道配置研究[J]. 重庆交通大学学报（自然科学版）, 2018, 37(1): 85-91.
[2]	李平. 渝长高速公路可持续发展能力分析[J]. 重庆交通大学学报（自然科学版）, 2007, 26(增刊1): 110-113.
[3]	黄平. 公路旅游运输客运量预测[J]. 重庆交通大学学报（自然科学版）, 2002, 21(4): 51-53.
[4]	潘海. 论高速公路景观设计[J]. 重庆交通大学学报（自然科学版）, 1998, 17(3): 48-53.
[5]	李淑庆. 成渝高速公路（重庆段）数据库开发简介[J]. 重庆交通大学学报（自然科学版）, 1998, 17(2): 81-89.
[6]	宋镖, 乔乐中. 试论高速公路与社会经济现代化[J]. 重庆交通大学学报（自然科学版）, 1998, 17(2): 125-130.

考虑交通状态影响的服务区凝聚层次聚类入区判别模型

An Identification Model of Entering the Service Area of Agglomerative Hierarchical Clustering Considering the Influence of Traffic Status

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics