手机信令不均匀定位下出行端点自适应识别方法

doi:10.16097/j.cnki.1009-6744.2025.04.005

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (4): 44-52.DOI: 10.16097/j.cnki.1009-6744.2025.04.005

• 智能交通系统与信息技术 • 上一篇下一篇

手机信令不均匀定位下出行端点自适应识别方法

姚振兴^*1a，刘贤^1a ，赵一飞^1b ，王亮² ，王彦琛³

1. 长安大学，a.运输工程学院，b.公路学院，西安710064；2.中国电建集团西北勘测设计研究院有限公司，西安710065； 3. 四川智慧高速科技有限公司，成都610041

收稿日期:2024-05-29 修回日期:2024-08-26 接受日期:2025-03-13 出版日期:2025-08-25 发布日期:2025-08-25
作者简介:姚振兴(1989—)，男，浙江金华人，副教授，博士。
基金资助:
陕西省自然科学基础研究计划项目(2025JC-YBMS-428)；陕西省哲学社会科学研究专项(2025YB0433)；中央高校基本科研业务费专项资金项目(300102344604)。

Adaptive Trip Ends Identification Method Under Uneven Positioning of Mobile Signaling

YAO Zhenxing^*1a, LIU Xian^1a, ZHAO Yifei^1b, WANG Liang², WANG Yanchen³

1a. School of Transportation Engineering, 1b. School of Highway, Chang'an University, Xi'an 710064, China; 2. PowerChina Northwest Engineering Corporation Limited, Xi'an 710065, China; 3. Sichuan Intelligent Expressway Technology Co Ltd, Chengdu 610041, China

Received:2024-05-29 Revised:2024-08-26 Accepted:2025-03-13 Online:2025-08-25 Published:2025-08-25
Supported by:
Natural Science Foundation of Shaanxi Province, China(2025JC-YBMS-428)；Special Program for Philosophy and Social Sciences Research of Shaanxi Province, China (2025YB0433)；Fundamental Research Funds for the Central Universities CHD (300102344604)。

摘要/Abstract

摘要： 准确的出行端点信息采集是保障交通规划方案有效性的重要基础。4G/5G通信技术能够连续、动态追踪个体全过程出行轨迹，为精细化出行端点采集带来了新契机。然而手机信令数据固有的不均匀时空定位特性对出行端点识别效果造成了巨大挑战，本文提出一种适用于手机信令不均匀时空定位轨迹的自适应出行端点识别方法。首先，构建U-DBSCAN(UnevenPositioning Density Based Spatial Clustering of Applications with Noise)算法用于不同密度数据下个体出行端点识别，该算法同步考虑信令数据时空双重不均匀约束特性，可有效弥补稀疏信令数据造成的停留点漏识别和错误识别问题；其次，基于K-平均最近邻算法建立U-DBSCAN参数自适应协同框架，实现了数据密度可调可变环境下模型参数自适应最优匹配，促进出行端点识别效果与技术普适性提升。在贵阳市开展大量同步对比实证试验，结果表明：不均匀时空定位环境下个体出行端点识别精度达90.98%，平均坐标误差为344.13m，出行端点到达与离开时间误差均小于3min；相较于KANN-DBSCAN(K-Average Nearest Neighbor Density Based Spatial Clustering of Applications with Noise)、ST-DBSCAN(Spatial Temporal Density Based Spatial Clustering of Applications with Noise)和DBSCAN(Density Based Spatial Clustering of Applications with Noise)等算法，准确率提升9.62%~ 23.45%，说明本文方法的精确性和稳定性更佳。本文能够为分析居民出行活动与需求特征，提升交通规划方案有效性提供有力支撑。

关键词: 智能交通, 出行端点识别, U-DBSCAN聚类算法, 手机信令数据, 自适应调参框架

Abstract: Accurate capture of trip ends information is crucial to guarantee the efficacy of transportation planning strategies. The 4G/5G communication technology enables continuous and dynamic tracking of individuals' entire travel process, offering novel opportunities for enhanced trip ends extraction. However, the inherent uneven spatiotemporal positioning characteristics of mobile signaling will pose considerable challenges to the efficacy of trip ends identification. Therefore, this study introduces an adaptive trip ends identification approach tailored for uneven spatiotemporal location trajectories derived from mobile phone signaling. Firstly, the U-DBSCAN algorithm is designed to accurately identify individual's trip ends across varying data densities. This method incorporates the dual non-uniform constraints of spatiotemporal signaling data, enabling precise discrimination of trip ends, which effectively mitigates the challenges associated with missed or erroneous identifications that frequently arise due to sparse signaling data. Secondly, leveraging the K-average nearest neighbor algorithm, a parameter adaptive framework for U-DBSCAN model is established. This framework is designed to achieve optimal adaptive matching of model parameters in a variable data density environment, thereby enhancing the effectiveness and technical universality of trip ends identification. A significant number of synchronous comparative empirical experiments have been conducted in Guiyang City. The results demonstrate that, within an environment characterized by uneven spatiotemporal positioning, the effective accuracy of individual's trip ends identification reaches 90.98%, with an average coordinate distance error of 344.13 meters. Both the starting and ending time errors of trip ends are maintained below 3 minutes. In comparison to KANN-DBSCAN, ST-DBSCAN, and DBSCAN algorithms, the accuracy has undergone a significant enhancement, ranging from 9.62% to 23.45%, resulting in better accuracy and stability. This study can offer robust support in analyzing the characteristics of residents' travel activities and demand, while simultaneously enhancing the effectiveness of transportation planning schemes.

Key words: intelligent transportation, trip ends identification, U-DBSCAN clustering algorithm, mobile phone signaling data; model parameter adaptive framework

中图分类号:

U491.1

姚振兴, 刘贤, 赵一飞, 王亮, 王彦琛. 手机信令不均匀定位下出行端点自适应识别方法[J]. 交通运输系统工程与信息, 2025, 25(4): 44-52.

YAO Zhenxing, LIU Xian, ZHAO Yifei, WANG Liang, WANG Yanchen. Adaptive Trip Ends Identification Method Under Uneven Positioning of Mobile Signaling[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(4): 44-52.

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http://www.tseit.org.cn/CN/Y2025/V25/I4/44

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手机信令不均匀定位下出行端点自适应识别方法

Adaptive Trip Ends Identification Method Under Uneven Positioning of Mobile Signaling

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