IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i16p6331-d395419.html
   My bibliography  Save this article

Hazard Duration Model with Panel Data for Daily Car Travel Distance: A Toyota City Case Study

Author

Listed:
  • Jiahang He

    () (Department of Civil Engineering, Nagoya University, Nagoya 464-8601, Japan)

  • Toshiyuki Yamamoto

    () (Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 4648601, Japan)

  • Tomio Miwa

    () (Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 4648601, Japan)

  • Takayuki Morikawa

    () (Institutes of Innovation for Future Society, Nagoya University, Nagoya 4648601, Japan)

Abstract

The limitation of battery size for electric vehicles has driven researchers to study driving distance. Trip patterns and traveler preferences in terms of distance are affected by multiple variables. This study, using socioeconomics, weather conditions, and vehicle characteristics as covariates, compares lognormal, log-logistic, and Weibull distribution assumptions on daily car travel distances with a parametric hazard model for both pooled and panel regression. The results reveal that the log-logistic distribution performed best for both the pooled and panel models, and the inclusion of heterogeneity by the panel model improves the model. The results suggest that the travel distances achieved by people in Toyota City, Japan, is highly dependent on the weather conditions, specifically the precipitation and wind speed. Socioeconomic indicators, such as age and gender, and vehicle characteristics, such as engine size and vehicle price, also significantly affect the car travel distance.

Suggested Citation

  • 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, Open Access Journal, vol. 12(16), pages 1-13, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6331-:d:395419
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/16/6331/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/16/6331/
    Download Restriction: no
    ---><---

    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. Jason Abrevaya & Shu Shen, 2014. "Estimation Of Censored Panel‐Data Models With Slope Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 523-548, June.
    3. Guille, Christophe & Gross, George, 2009. "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, Elsevier, vol. 37(11), pages 4379-4390, November.
    4. Bhat, Chandra R. & Frusti, Teresa & Zhao, Huimin & Schönfelder, Stefan & Axhausen, Kay W., 2004. "Intershopping duration: an analysis using multiweek data," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 39-60, January.
    5. Bhat, Chandra R., 1996. "A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute," Transportation Research Part B: Methodological, Elsevier, vol. 30(6), pages 465-480, December.
    6. Hidrue, Michael K. & Parsons, George R. & Kempton, Willett & Gardner, Meryl P., 2011. "Willingness to pay for electric vehicles and their attributes," Resource and Energy Economics, Elsevier, vol. 33(3), pages 686-705, September.
    7. Erik Biørn & Kjersti-Gro Lindquist & Terje Skjerpen, 2002. "Heterogeneity in Returns to Scale: A Random Coefficient Analysis with Unbalanced Panel Data," Journal of Productivity Analysis, Springer, vol. 18(1), pages 39-57, July.
    8. Zanni, Alberto M. & Ryley, Tim J., 2015. "The impact of extreme weather conditions on long distance travel behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 305-319.
    9. William Greene, 2003. "A Interpreting Estimated Parameters and Measuring Individual Heterogeneity in Random Coefficient Models," Working Papers 03-19, New York University, Leonard N. Stern School of Business, Department of Economics.
    10. Kharoufeh, Jeffrey P. & Goulias, Konstadinos G., 2002. "Nonparametric identification of daily activity durations using kernel density estimators," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 59-82, January.
    11. Veronique Acker & Frank Witlox, 2011. "Commuting trips within tours: how is commuting related to land use?," Transportation, Springer, vol. 38(3), pages 465-486, May.
    12. 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, Open Access Journal, vol. 12(2), pages 1-12, January.
    13. Bernet Sekasanvu Kato & Herbert Hoijtink, 2004. "Testing homogeneity in a random intercept model using asymptotic, posterior predictive and plug‐in p‐values," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 179-196, May.
    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. Zidan Mao & Dick Ettema & Martin Dijst, 2018. "Analysis of travel time and mode choice shift for non-work stops in commuting: case study of Beijing, China," Transportation, Springer, vol. 45(3), pages 751-766, May.
    2. Keumju Lim & Justine Jihyun Kim & Jongsu Lee, 2020. "Forecasting the future scale of vehicle to grid technology for electric vehicles and its economic value as future electric energy source: The case of South Korea," Energy & Environment, , vol. 31(8), pages 1350-1366, December.
    3. 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, Open Access Journal, vol. 12(2), pages 1-12, January.
    4. Tai-Yu Ma & Iragaël Joly & Charles Raux, 2010. "A shared frailty semi-parametric markov renewal model for travel and activity time-use pattern analysis," Working Papers hal-00477695, HAL.
    5. Parsons, George R. & Hidrue, Michael K. & Kempton, Willett & Gardner, Meryl P., 2014. "Willingness to pay for vehicle-to-grid (V2G) electric vehicles and their contract terms," Energy Economics, Elsevier, vol. 42(C), pages 313-324.
    6. Lee, Backjin & Timmermans, Harry J.P., 2007. "A latent class accelerated hazard model of activity episode durations," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 426-447, May.
    7. Mubbashir Ali & Jussi Ekström & Matti Lehtonen, 2018. "Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems," Energies, MDPI, Open Access Journal, vol. 11(5), pages 1-11, May.
    8. Li, Jingjing & Kim, Changjoo & Sang, Sunhee, 2018. "Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors," Journal of Transport Geography, Elsevier, vol. 70(C), pages 141-147.
    9. Chaouachi, Aymen & Bompard, Ettore & Fulli, Gianluca & Masera, Marcelo & De Gennaro, Michele & Paffumi, Elena, 2016. "Assessment framework for EV and PV synergies in emerging distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 719-728.
    10. Kley, Fabian & Lerch, Christian & Dallinger, David, 2011. "New business models for electric cars--A holistic approach," Energy Policy, Elsevier, vol. 39(6), pages 3392-3403, June.
    11. Ding, Yu & Lu, Huapu, 2016. "Activity participation as a mediating variable to analyze the effect of land use on travel behavior: A structural equation modeling approach," Journal of Transport Geography, Elsevier, vol. 52(C), pages 23-28.
    12. Biorn, Erik & Hagen, Terje P. & Iversen, Tor & Magnussen, Jon, 2006. "Heterogeneity in Hospitals' Responses to a Financial Reform: A Random Coefficient Analysis of The Impact of Activity-Based Financing on Efficiency," MPRA Paper 8169, University Library of Munich, Germany.
    13. Jayawardena, A.V. & Meegahapola, L.G. & Robinson, D.A. & Perera, S., 2015. "Microgrid capability diagram: A tool for optimal grid-tied operation," Renewable Energy, Elsevier, vol. 74(C), pages 497-504.
    14. Petschnig, Martin & Heidenreich, Sven & Spieth, Patrick, 2014. "Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 68-83.
    15. Luo, Lizi & Wu, Zhi & Gu, Wei & Huang, He & Gao, Song & Han, Jun, 2020. "Coordinated allocation of distributed generation resources and electric vehicle charging stations in distribution systems with vehicle-to-grid interaction," Energy, Elsevier, vol. 192(C).
    16. Kim, Junghun & Seung, Hyunchan & Lee, Jongsu & Ahn, Joongha, 2020. "Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions," Energy Economics, Elsevier, vol. 86(C).
    17. Juan Alcácer & Wilbur Chung & Ashton Hawk & Gonçalo Pacheco-de-Almeida, 2018. "Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects," Strategy Science, INFORMS, vol. 3(3), pages 533-553, September.
    18. Saxena, Samveg & Gopal, Anand & Phadke, Amol, 2014. "Electrical consumption of two-, three- and four-wheel light-duty electric vehicles in India," Applied Energy, Elsevier, vol. 115(C), pages 582-590.
    19. Axsen, Jonn & Kurani, Kenneth S., 2013. "Hybrid, plug-in hybrid, or electric—What do car buyers want?," Energy Policy, Elsevier, vol. 61(C), pages 532-543.
    20. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.

    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:12:y:2020:i:16:p:6331-:d:395419. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: https://www.mdpi.com/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.