IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v101y2017icp213-227.html
   My bibliography  Save this article

On the distribution of individual daily driving distances

Author

Listed:
  • Plötz, Patrick
  • Jakobsson, Niklas
  • Sprei, Frances

Abstract

Plug-in electric vehicles (PEV) can reduce greenhouse gas emissions. However, the utility of PEVs, as well as reduction of emissions is highly dependent on daily vehicle kilometres travelled (VKT). Further, the daily VKT by individual passenger cars vary strongly between days. A common method to analyse individual daily VKT is to fit distribution functions and to further analyse these fits. However, several distributions for individual daily VKT have been discussed in the literature without conclusive decision on the best distribution. Here we analyse three two-parameter distribution functions for the variation in daily VKT with four sets of travel data covering a total of 190,000 driving days and 9.5 million VKT. Specifically, we look at overall performance of the distributions on the data using four goodness of fit measures, as well as the consequence of choosing one distribution over the others for two common PEV applications: the days requiring adaptation for battery electric vehicles and the utility factor for plug-in hybrid electric vehicles. We find the Weibull distribution to fit most vehicles well but not all and at the same time yielding good predictions for PEV related attributes. Furthermore, the choice of distribution impacts PEV usage factors. Here, the Weibull distribution yields reliable estimates for electric vehicle applications whereas the log-normal distribution yields more conservative estimates for PEV usage factors. Our results help to guide the choice of distribution for a specific research question utilising driving data and provide a methodological advancement in the application of distribution functions to longitudinal driving data.

Suggested Citation

  • Plötz, Patrick & Jakobsson, Niklas & Sprei, Frances, 2017. "On the distribution of individual daily driving distances," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 213-227.
    • Handle: RePEc:eee:transb:v:101:y:2017:i:c:p:213-227
    DOI: 10.1016/j.trb.2017.04.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261516309067
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2017.04.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Greene, David L., 1985. "Estimating daily vehicle usage distributions and the implications for limited-range vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 347-358, August.
    2. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    3. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation," Ecological Economics, Elsevier, vol. 107(C), pages 411-421.
    4. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data: German market and policy options," Working Papers "Sustainability and Innovation" S12/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    5. Millo, Federico & Rolando, Luciano & Fuso, Rocco & Mallamo, Fabio, 2014. "Real CO2 emissions benefits and end user’s operating costs of a plug-in Hybrid Electric Vehicle," Applied Energy, Elsevier, vol. 114(C), pages 563-571.
    6. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    7. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data. Part I: Model structure and validation," Working Papers "Sustainability and Innovation" S4/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesco Simmini & Tommaso Caldognetto & Mattia Bruschetta & Enrico Mion & Ruggero Carli, 2021. "Model Predictive Control for Efficient Management of Energy Resources in Smart Buildings," Energies, MDPI, vol. 14(18), pages 1-19, September.
    2. Han Su & Qian Zhang & Wanying Wang & Xiaoan Tang, 2021. "A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    3. Bogdanov, Dmitrii & Breyer, Christian, 2024. "Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level," Energy, Elsevier, vol. 301(C).
    4. Francesco Lo Franco & Mattia Ricco & Riccardo Mandrioli & Gabriele Grandi, 2020. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization," Energies, MDPI, vol. 13(19), pages 1-25, September.
    5. Laurent Franckx, 2019. "Working Paper 01-19 - Future evolution of the car stock in Belgium: CASMO, the new satellite of PLANET," Working Papers 1901, Federal Planning Bureau, Belgium.
    6. Montserrat Guillen & Ana M. Pérez-Marín & Manuela Alcañiz, 2020. "Risk reference charts for speeding based on telematics information," IREA Working Papers 202003, University of Barcelona, Research Institute of Applied Economics, revised Apr 2020.
    7. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    8. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    9. Jan K. Kazak, 2018. "The Use of a Decision Support System for Sustainable Urbanization and Thermal Comfort in Adaptation to Climate Change Actions—The Case of the Wrocław Larger Urban Zone (Poland)," Sustainability, MDPI, vol. 10(4), pages 1-15, April.
    10. Mohamed Azab, 2023. "Optimum Scenarios of EV Charging Infrastructure: A Case Study for the Saudi Arabia Market," Energies, MDPI, vol. 16(13), pages 1-22, July.
    11. Jiahang He & Toshiyuki Yamamoto, 2020. "Characterization of Daily Travel Distance of a University Car Fleet for the Purpose of Replacing Conventional Vehicles with Electric Vehicles," Sustainability, MDPI, vol. 12(2), pages 1-12, January.
    12. Zhaoqi Zang & Xiangdong Xu & Kai Qu & Ruiya Chen & Anthony Chen, 2022. "Travel time reliability in transportation networks: A review of methodological developments," Papers 2206.12696, arXiv.org, revised Jul 2022.
    13. Funke, Simon Árpád & Plötz, Patrick & Wietschel, Martin, 2019. "Invest in fast-charging infrastructure or in longer battery ranges? A cost-efficiency comparison for Germany," Applied Energy, Elsevier, vol. 235(C), pages 888-899.
    14. Ensslen, Axel & Ringler, Philipp & Dörr, Lasse & Jochem, Patrick & Zimmermann, Florian & Fichtner, Wolf, 2018. "Incentivizing smart charging: Modeling charging tariffs for electric vehicles in German and French electricity markets," MPRA Paper 91543, University Library of Munich, Germany, revised 17 Feb 2018.
    15. 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.
    16. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    17. Tchetchik, Anat & Zvi, Liat I. & Kaplan, Sigal & Blass, Vered, 2020. "The joint effects of driving hedonism and trialability on the choice between internal combustion engine, hybrid, and electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    18. Xuefang Li & Chenhui Liu & Jianmin Jia, 2019. "Ownership and Usage Analysis of Alternative Fuel Vehicles in the United States with the 2017 National Household Travel Survey Data," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    19. Willett Kempton & Nathaniel S. Pearre & Randall Guensler & Vetri V. Elango, 2023. "Influence of Battery Energy, Charging Power, and Charging Locations upon EVs’ Ability to Meet Trip Needs," Energies, MDPI, vol. 16(5), pages 1-23, February.
    20. Jiahang He & Toshiyuki Yamamoto & Tomio Miwa & Takayuki Morikawa, 2020. "Hazard Duration Model with Panel Data for Daily Car Travel Distance: A Toyota City Case Study," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    21. Tal, Gil & Karanam, Vaishnavi Chaitanya & Favetti, Matthew P. & Sutton, Katrina May & Ogunmayin, Jade Motayo & Raghavan, Seshadri Srinivasa & Nitta, Christopher & Chakraborty, Debapriya & Davis, Adam , 2021. "Emerging Technology Zero Emission Vehicle Household Travel and Refueling Behavior," Institute of Transportation Studies, Working Paper Series qt2v0853tp, Institute of Transportation Studies, UC Davis.
    22. Zang, Zhaoqi & Xu, Xiangdong & Yang, Chao & Chen, Anthony, 2018. "A closed-form estimation of the travel time percentile function for characterizing travel time reliability," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 228-247.

    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. Jochem, Patrick & Babrowski, Sonja & Fichtner, Wolf, 2015. "Assessing CO2 emissions of electric vehicles in Germany in 2030," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 68-83.
    2. Gnann, Till & Stephens, Thomas S. & Lin, Zhenhong & Plötz, Patrick & Liu, Changzheng & Brokate, Jens, 2018. "What drives the market for plug-in electric vehicles? - A review of international PEV market diffusion models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 158-164.
    3. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    4. Bhat, Furqan A. & Verma, Ashish, 2024. "Electric two-wheeler adoption in India – A discrete choice analysis of motivators and barriers affecting the potential electric two-wheeler buyers," Transport Policy, Elsevier, vol. 152(C), pages 118-131.
    5. Gnann, T. & Speth, D. & Seddig, K. & Stich, M. & Schade, W. & Gómez Vilchez, J.J., 2022. "How to integrate real-world user behavior into models of the market diffusion of alternative fuels in passenger cars - An in-depth comparison of three models for Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    6. Zhang, Cen & Schmöcker, Jan-Dirk & Kuwahara, Masahiro & Nakamura, Toshiyuki & Uno, Nobuhiro, 2020. "A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 135-150.
    7. Viri, Riku & Mäkinen, Johanna & Liimatainen, Heikki, 2021. "Modelling car fleet renewal in Finland: A model and development speed-based scenarios," Transport Policy, Elsevier, vol. 112(C), pages 63-79.
    8. Globisch, Joachim & Plötz, Patrick & Dütschke, Elisabeth & Wietschel, Martin, 2019. "Consumer preferences for public charging infrastructure for electric vehicles," Transport Policy, Elsevier, vol. 81(C), pages 54-63.
    9. Ensslen, Axel & Gnann, Till & Jochem, Patrick & Plötz, Patrick & Dütschke, Elisabeth & Fichtner, Wolf, 2020. "Can product service systems support electric vehicle adoption?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 343-359.
    10. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.
    11. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    12. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    13. Xie, Fei & Lin, Zhenhong, 2017. "Market-driven automotive industry compliance with fuel economy and greenhouse gas standards: Analysis based on consumer choice," Energy Policy, Elsevier, vol. 108(C), pages 299-311.
    14. Schwab, Julia & Sölch, Christian & Zöttl, Gregor, 2022. "Electric Vehicle Cost in 2035: The impact of market penetration and charging strategies," Energy Economics, Elsevier, vol. 114(C).
    15. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    16. Gnann, Till & Speth, Daniel & Plötz, Patrick & Wietschel, Martin & Krail, Michael, 2022. "Markthochlaufszenarien für Elektrofahrzeuge: Rückblick und Ausblick bis 2030," Working Papers "Sustainability and Innovation" S05/2022, Fraunhofer Institute for Systems and Innovation Research (ISI).
    17. Plötz, Patrick & Gnann, Till & Jochem, Patrick & Yilmaz, Hasan Ümitcan & Kaschub, Thomas, 2019. "Impact of electric trucks powered by overhead lines on the European electricity system and CO2 emissions," Energy Policy, Elsevier, vol. 130(C), pages 32-40.
    18. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
    19. Sykes, Maxwell & Axsen, Jonn, 2017. "No free ride to zero-emissions: Simulating a region's need to implement its own zero-emissions vehicle (ZEV) mandate to achieve 2050 GHG targets," Energy Policy, Elsevier, vol. 110(C), pages 447-460.
    20. Romano Alberto Acri & Silvia Barone & Paolo Cambula & Valter Cecchini & Maria Carmen Falvo & Jacopo Lepore & Matteo Manganelli & Federico Santi, 2021. "Forecast of the Demand for Electric Mobility for Rome–Fiumicino International Airport," Energies, MDPI, vol. 14(17), pages 1-19, August.

    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:eee:transb:v:101:y:2017:i:c:p:213-227. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.