How do long combination vehicles perform in real traffic? A study using Naturalistic Driving Data

被引:0
|
作者
Behera, Abhijeet [1 ,2 ]
Kharrazi, Sogol [1 ,2 ]
Frisk, Erik [2 ]
机构
[1] Swedish Natl Rd & Transport Res Inst, Linkoping, Sweden
[2] Linkoping Univ, Div Vehicular Syst, Elect Engn, Linkoping, Sweden
来源
关键词
Naturalistic driving data; Long combination vehicles; A-double; DuoCAT; Performance-based standards; Rearward amplification; High-speed transient offtracking; Low-speed swept path; High-speed steady-state offtracking; Steering reversal rate; HEAVY VEHICLES; BEHAVIOR;
D O I
10.1016/j.aap.2024.107763
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
This paper evaluates the performance of two different types of long combination vehicles (A-double and DuoCAT) using naturalistic driving data across four scenarios: lane changes, manoeuvring through roundabouts, turning in intersections, and negotiating tight curves. Four different performance-based standards measures are used to assess the stability and tracking performance of the vehicles: rearward amplification, high-speed transient offtracking, low-speed swept path, and high-speed steady-state offtracking. Also, the steering reversal rate metric is employed to estimate the cognitive workload of the drivers in low-speed scenarios. In the majority of the identified cases of the four scenarios, both combination types have a good performance. The A-double shows slightly better stability in high-speed lane changes, while the DuoCAT has slightly better manoeuvrability at low-speed scenarios like roundabouts and intersections.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Application of dynamic traffic flow map by using real time GPS data equipped vehicles
    Shi Xiaohui
    Xing Jianping
    Zhang Jun
    Zhou Lei
    Li Weiyi
    2006 6TH INTERNATIONAL CONFERENCE ON ITS TELECOMMUNICATIONS PROCEEDINGS, 2006, : 1191 - +
  • [42] Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level
    Wu, Kun-Feng
    Aguero-Valverde, Jonathan
    Jovanis, Paul P.
    ACCIDENT ANALYSIS AND PREVENTION, 2014, 72 : 210 - 218
  • [43] How real-world driving cycle differs in heterogeneous traffic conditions: a case study in Delhi
    Kumar, Ravindra
    Parida, Purnima
    Durai, Bhujang Kanga
    Saleh, Wafaa
    WORLD JOURNAL OF SCIENCE TECHNOLOGY AND SUSTAINABLE DEVELOPMENT, 2013, 10 (01): : 66 - 80
  • [44] Exploring the combined effects of driving situations on freeway rear-end crash risk using naturalistic driving study data
    Wu, Kun-Feng
    Wang, Lan
    ACCIDENT ANALYSIS AND PREVENTION, 2021, 150
  • [45] Evaluation of the Impact of Work Zone Traffic Control Devices on Change of Speed Using the SHRP 2 Naturalistic Driving Study
    Hallmark, Shauna
    Basulto-Elias, Guillermo
    Oneyear, Nicole
    Goswamy, Amrita
    Thapa, Raju
    Chrysler, Susan T. T.
    Smadi, Omar
    TRANSPORTATION RESEARCH RECORD, 2023, 2677 (10) : 757 - 765
  • [46] Examining driver distraction in the context of driving speed: An observational study using disruptive technology and naturalistic data
    Iio, Kentaro
    Guo, Xiaoyu
    Lord, Dominique
    ACCIDENT ANALYSIS AND PREVENTION, 2021, 153
  • [47] Using Naturalistic Driving Study Data to Investigate Driver Behavior at Highway-Rail Grade Crossings
    Salim, Alawudin
    Jeon, Myounghoon
    Lautala, Pasi
    Nelson, David
    PROCEEDINGS OF THE ASME JOINT RAIL CONFERENCE, 2018, 2018,
  • [48] A comprehensive methodology for developing and evaluating driving cycles for electric vehicles using real-world data
    Lee, Gwangryeol
    Yeon, Jehwi
    Kim, Namwook
    Park, Suhan
    ETRANSPORTATION, 2025, 24
  • [49] Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data
    Chang, Yan
    Yang, Weiqing
    Zhao, Ding
    2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2018, : 2058 - 2063
  • [50] Characterization of Prospective Charging Locations of Plug-in Vehicles Using Real-World Driving Data
    Ghiasnezhad, Nima
    Filizadeh, Shaahin
    2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,