Driving Characteristics of Heavy-Duty Urban Transit Vehicles in Seoul: Insights from Real-Time Data and Annual Statistics

被引:0
|
作者
Kim, Seongsu [1 ]
Kim, Junghwan [2 ]
机构
[1] Chung Ang Univ, Dept Energy Syst Engn, Seoul 06974, South Korea
[2] Chung Ang Univ, Sch Energy Syst Engn, 84 Heukseokro Dongjakgu, Seoul 06974, South Korea
关键词
Seoul bus; Driving characteristics; Bus driving data; k-means clustering; Traffic conditions; CONSUMPTION;
D O I
10.1007/s12239-024-00176-7
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This study examined Seoul City bus-driving behavior using an extensive dataset with real-time driving data and detailed annual statistics to determine distinct patterns in bus operation. The study focused on four distinct scenarios near bus stations, offering insight into the complex interaction between diverse traffic conditions and station locations. Moreover, the study explored standard speed profiles around ten strategically selected stations derived from cases of buses approaching and departing without external disturbances, providing a baseline for understanding typical bus movement dynamics. Furthermore, the analysis was extended to the speed profiles of 12 consecutive units, revealing insights into traffic conditions and route quality. Furthermore, k-means clustering was applied, and three unique driving categories were distinguished. By integrating these findings with broader traffic flow theories and urban mobility, this study provides valuable insights for improving bus operations and traffic management in metropolitan areas, offering practical recommendations for enhancing public transportation systems and urban mobility in Seoul and other cities with similar challenges.
引用
收藏
页码:391 / 398
页数:8
相关论文
共 50 条
  • [41] Spatial–temporal distribution characteristics of pollutants of heavy-duty diesel vehicles in urban road networks: a case study of Kunming City
    Jiachen Xu
    Chao He
    Jiaqiang Li
    Longqing Zhao
    Yanlin Chen
    Yangyang Bai
    Ju Li
    Hao Wang
    Zhenyu Chen
    Zhenyu Qiu
    Environmental Science and Pollution Research, 2023, 30 : 126072 - 126087
  • [42] Laboratory measurement and machine learning-based analysis of driving factors for brake wear particle emissions from light-duty electric vehicles and heavy-duty vehicles
    Yin, Jiawei
    Xu, Zhou
    Wei, Wendi
    Jia, Zhenyu
    Fang, Tiange
    Jiang, Zhiwen
    Cao, Zeping
    Wu, Lin
    Wei, Ning
    Men, Zhengyu
    Guo, Quanyou
    Zhang, Qijun
    Mao, Hongjun
    JOURNAL OF HAZARDOUS MATERIALS, 2025, 488
  • [43] Study on Real Driving Fine Particles Emission Characteristics for a Heavy-duty Diesel Vehicle Based on Engine-in-the-Loop Methodology
    Wang X.
    Jing X.
    Gao T.
    Gu X.
    Zhang Y.
    Wu L.
    Qiche Gongcheng/Automotive Engineering, 2022, 44 (01): : 58 - 63
  • [44] Insights into the Fusion Correction Algorithm for On-Board NOx Sensor Measurement Results from Heavy-Duty Diesel Vehicles
    Wu, Chunling
    Pei, Yiqiang
    Liu, Chuntao
    Bai, Xiaoxin
    Jing, Xiaojun
    Zhang, Fan
    Qin, Jing
    ENERGIES, 2023, 16 (16)
  • [45] Real-world particulate, GHG, and gaseous toxic emissions from heavy-duty diesel and natural gas vehicles
    Toumasatos, Zisimos
    Zhu, Hanwei
    Durbin, Thomas D.
    Johnson, Kent C.
    Cao, Sam
    Karavalakis, Georgios
    ATMOSPHERIC ENVIRONMENT, 2024, 327
  • [46] Real-Time Black Carbon Emission Factor Measurements from Light Duty Vehicles
    Forestieri, Sara D.
    Collier, Sonya
    Kuwayama, Toshihiro
    Zhang, Qi
    Kleeman, Michael J.
    Cappa, Christopher D.
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2013, 47 (22) : 13104 - 13112
  • [47] Spatial-temporal distribution characteristics of pollutants of heavy-duty diesel vehicles in urban road networks: a case study of Kunming City
    Xu, Jiachen
    He, Chao
    Li, Jiaqiang
    Zhao, Longqing
    Chen, Yanlin
    Bai, Yangyang
    Li, Ju
    Wang, Hao
    Chen, Zhenyu
    Qiu, Zhenyu
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (60) : 126072 - 126087
  • [48] Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks
    Hongqing Chu
    Xiaoxiang Na
    Huan Liu
    Yuhai Wang
    Zhuo Yang
    Lin Zhang
    Hong Chen
    Chinese Journal of Mechanical Engineering, 2024, 37 (02) : 311 - 334
  • [49] M-Estimator Application in Real-Time Sensor Fusion for Smooth Position Feedback of Heavy-Duty Field Robots
    Liikanen, Henri
    Aref, Mohammad M.
    Mattila, Jouni
    PROCEEDINGS OF THE IEEE 2019 9TH INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS (CIS) ROBOTICS, AUTOMATION AND MECHATRONICS (RAM) (CIS & RAM 2019), 2019, : 368 - 373
  • [50] Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks
    Chu, Hongqing
    Na, Xiaoxiang
    Liu, Huan
    Wang, Yuhai
    Yang, Zhuo
    Zhang, Lin
    Chen, Hong
    CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2024, 37 (01)