IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p2114-d500337.html
   My bibliography  Save this article

Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China

Author

Listed:
  • Bing Han

    (School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
    Suzhou Planning & Design Research Institute Co. Ltd., Suzhou 215002, China)

  • Ziheng Wu

    (Nanjing Research Institute of Electronics Engineering, Nanjing 320100, China)

  • Chaoyi Gu

    (Texas A&M Transportation Institute, College Station, TX 77843, USA)

  • Kui Ji

    (Jiangsu Institute of Urban Planning and Design, Nanjing 210096, China)

  • Jiangang Xu

    (School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China)

Abstract

A drive cycle describes the microscopic and macroscopic vehicle activity information that is crucial for emission quantification research, e.g., emission modeling or emission testing. Well-developed drive cycles capture the driving patterns representing the traffic conditions of the study area, which usually are employed as the input of the emission models. By considering the potential of large-scale GPS trajectory data collected by ubiquitous on-vehicle tracking equipment, the objective of this study is to demonstrate the capability of GPS-based trajectory data from rideshare passenger cars for urban drive cycle development. Large-scale GPS trajectory data and order data collected by an app-based transportation vehicle was used in this study. GPS data were filtered by thresholds of instantaneous accelerations and vehicle specific powers. The micro-trip selection-to-rebuild method with operating mode distribution was used to develop a series of speed-bin categorized representative drive cycles. Sensitivity of the time-of-day and day-of-week were analyzed on the developed drive cycles. The representativeness of the developed drive cycles was verified and significant differences exist when they are compared to the default light-duty drive cycles coded in MOVES. The findings of this study can be used for helping drive cycle development and emission modeling, further improving the understanding of localized emission levels.

Suggested Citation

  • Bing Han & Ziheng Wu & Chaoyi Gu & Kui Ji & Jiangang Xu, 2021. "Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2114-:d:500337
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/2114/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/4/2114/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    2. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    3. Vítor JPD Martinho, 2018. "A transversal perspective on global energy production and consumption: An approach based on convergence theory," Energy & Environment, , vol. 29(4), pages 556-575, June.
    4. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    5. Andriosopoulos, Kostas & Bigerna, Simona & Bollino, Carlo Andrea & Micheli, Silvia, 2018. "The impact of age on Italian consumers' attitude toward alternative fuel vehicles," Renewable Energy, Elsevier, vol. 119(C), pages 299-308.
    6. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.
    7. Yunna Wu & Meng Yang & Haobo Zhang & Kaifeng Chen & Yang Wang, 2016. "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method," Energies, MDPI, vol. 9(3), pages 1-20, March.
    8. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
    9. Mariusz Graba & Jarosław Mamala & Andrzej Bieniek & Andrzej Augustynowicz & Krystian Czernek & Andżelika Krupińska & Sylwia Włodarczak & Marek Ochowiak, 2023. "Assessment of Energy Demand for PHEVs in Year-Round Operating Conditions," Energies, MDPI, vol. 16(14), pages 1-19, July.
    10. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2016. "Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1351-1360.
    11. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    12. Ji, Wei, 2018. "Data-Driven Behavior Analysis and Implications in Plug-in Electric Vehicle Policy Studies," Institute of Transportation Studies, Working Paper Series qt6dw4d18t, Institute of Transportation Studies, UC Davis.
    13. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    14. Xianchun Tan & Yuan Zeng & Baihe Gu & Yi Wang & Baoguang Xu, 2018. "Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing," Sustainability, MDPI, vol. 10(6), pages 1-32, June.
    15. Wang, An & Tu, Ran & Gai, Yijun & Pereira, Lucas G. & Vaughan, J. & Posen, I. Daniel & Miller, Eric J. & Hatzopoulou, Marianne, 2020. "Capturing uncertainty in emission estimates related to vehicle electrification and implications for metropolitan greenhouse gas emission inventories," Applied Energy, Elsevier, vol. 265(C).
    16. Sun, Huaping & Geng, Yong & Hu, Lingxiang & Shi, Longyu & Xu, Tong, 2018. "Measuring China's new energy vehicle patents: A social network analysis approach," Energy, Elsevier, vol. 153(C), pages 685-693.
    17. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(C).
    18. Wang, Wanying & Zhang, Qiang & Peng, Zhanglin & Shao, Zhen & Li, Xuefang, 2020. "An empirical evaluation of different usage pattern between car-sharing battery electric vehicles and private ones," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 115-129.
    19. Qiao, Qinyu & Zhao, Fuquan & Liu, Zongwei & He, Xin & Hao, Han, 2019. "Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle," Energy, Elsevier, vol. 177(C), pages 222-233.
    20. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2114-:d:500337. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.