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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, vol. 12(16), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6331-:d:395419
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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. Kevin Manaugh & Luis Miranda-Moreno & Ahmed El-Geneidy, 2010. "The effect of neighbourhood characteristics, accessibility, home–work location, and demographics on commuting distances," Transportation, Springer, vol. 37(4), pages 627-646, July.
    12. 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.
    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.
    14. 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.
    15. 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.
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