交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (2): 354-362.DOI: 10.16097/j.cnki.1009-6744.2026.02.033

• 工程应用与案例分析 • 上一篇    

短行程时长阈值对行驶工况构建的影响研究

杨阳*1 ，卫倩1 ，于谦2 ，吴芳1   

  1. 1. 太原科技大学，车辆与交通工程学院，太原030024；2.长安大学，运输工程学院，西安710064
  • 收稿日期:2025-12-08 修回日期:2026-01-01 接受日期:2026-01-12 出版日期:2026-04-25 发布日期:2026-04-21
  • 作者简介:杨阳（1988―），女，河南南阳人，副教授。
  • 基金资助:
    山西省回国留学人员科研资助项目(2024-126)；陕西省自然科学基础研究计划项目(2025JC-YBMS-446)。

Impact of Short Trip Duration Thresholds on Driving Cycle Construction

YANG Yang*1, WEI Qian1, YU Qian2, WU Fang1   

  1. 1. College of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China; 2. College of Transportation Engineering, Chang'an University, Xi'an 710064, China
  • Received:2025-12-08 Revised:2026-01-01 Accepted:2026-01-12 Online:2026-04-25 Published:2026-04-21
  • Supported by:
    Research Funding Program for Returning Overseas Scholars of Shanxi Province, China(2024-126)；Basic Research Program for Natural Sciences of Shaanxi Province, China (2025JC-YBMS-446)。

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摘要： 行驶工况构建的核心在于精准捕捉瞬态驾驶行为，现有研究多聚焦聚类算法优化与数据拼接方法，却对短行程时长阈值（tmin）的选取缺乏系统性分析。本文旨在深入探究tmin对行驶工况代表性的影响机制，为地域化tmin优化提供科学量化依据。基于西安市200辆乘用车一年的车载实测数据，提出融合主成分分析、k-means++聚类与马尔可夫链蒙特卡洛算法的工况构建方法，并构建多维度验证体系，系统分析tmin取20、60、100、140、180 s时对工况构建的作用规律。结果表明：tmin=20 s时聚类效果最优，可精细区分7类多样化驾驶模式，所构建工况能最大程度保留瞬态驾驶特征，其平均相对误差（δPV）仅4.48%，KL散度( DKL )为0.36，比功率（Ps）全区间总偏差9.10%，均显著优于其他阈值工况；20s工况的平均急动度为0.11 m·s-3，远低于0.20 m·s-3的行业实操阈值，具备良好台架复现性。本文明确tmin=20 s为西安市及同类拥堵城市乘用车工况构建的优选阈值，研究成果可为地域化工况开发、车辆性能评估及节能技术研发提供理论与实践支撑。

关键词: 交通工程, 行驶工况, 短行程时长阈值, 乘用车, 马尔可夫链蒙特卡洛, 瞬态特征

Abstract: Accurately capturing transient driving behaviors is essential for constructing driving cycles. Existing research primarily focuses on clustering algorithms and data stitching methods, lacking systematic analysis of the short trip duration threshold ( tmin ). This paper aims to investigate the mechanism by which tmin influences the representativeness of driving conditions, providing a scientifically quantifiable basis for regionalized tmin optimization. Based on one year of in-vehicle measurement data from 200 passenger vehicles in Xi'an city, this paper proposes a driving condition construction method integrating principal component analysis, k-means++ clustering, and Markov chain Monte Carlo algorithms. A multi-dimensional verification system is established to systematically analyze the effects of tmin values (20, 60, 100, 140, 180 s) on driving condition construction. The results indicate that tmin =20 s yields optimal clustering performance, enabling precise differentiation of seven diverse driving modes. The constructed driving conditions maximally preserve transient driving characteristics, with an average relative error ( δPV ) of 4.48%, KL divergence ( DKL ) of 0.36, and total variance in specific power ( Ps ) across the range of 9.10%, which all significantly outperformed other threshold conditions. Thresholds below 20 s introduced fragmented noise, degrading clustering stability. The 20 s operating condition exhibited an average jerk of 0.11 m·s-3, significantly below the industry-practiced threshold of 0.20 m·s-3, demonstrating excellent bench reproducibility. This study confirms tmin =20 s as the optimal threshold for constructing passenger vehicle operating conditions in Xi'an and similar congested cities. The findings provide theoretical and practical support for regionalized operating condition development, vehicle performance evaluation, and energy-saving technology research.

Key words: traffic engineering, driving cycle, short trip duration threshold, passenger vehicle, Markov chain Monte Carlo, transient characteristics

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