Fuel economy gaps within and across garages: A bivariate random parameters seemingly unrelated regression approach

International Journal of Sustainable Transportation

Volumn 13, Issue 5, 2019, Pages 324-339

  Wali, Behram ^a   Khattak, Asad J ^a   Greene, David L ^a   Liu, Jun ^b  

^a University of Tennessee   (United States)

^b VIRGINIA DEPARTMENT OF TRANSPORTATION   (United States)

Author keywords

Fuel economy gap; garage; My MPG; on road and test cycle estimates; random parameters; seemingly unrelated regression estimation; two vehicles

Indexed keywords

AUTOMOBILE MANUFACTURE; FUELS; GARAGES (PARKING); ROADS AND STREETS;

DIFFERENTIAL EFFECT; EMPIRICAL ANALYSIS; MY MPG; RANDOM PARAMETER MODEL; RANDOM PARAMETERS; SEEMINGLY UNRELATED REGRESSION; TEST CYCLES; UNOBSERVED HETEROGENEITY;

FUEL ECONOMY;

CORRELATION; ESTIMATION METHOD; FUEL CONSUMPTION; HETEROGENEITY; PARAMETERIZATION; REGRESSION ANALYSIS; TRAFFIC CONGESTION; TRANSPORTATION ECONOMICS;

UNITED STATES;

EID: 85047613463     PISSN: 15568318     EISSN: 15568334     Source Type: Journal    
DOI: 10.1080/15568318.2018.1466222     Document Type: Article

References (67)

1. An, F., & Sauer, A. (2004). Comparison of passenger vehicle fuel economy and greenhouse gas emission standards around the world. Pew Center on Global Climate Change, 25.
2. Anastasopoulos, P., Karlaftis, M., Haddock, J., & Mannering, F. (2012). Household automobile and motorcycle ownership analyzed with random parameters bivariate ordered probit model. Transportation Research Record: Journal of the Transportation Research Board, 2279, 12–20. DOI: 10.3141/2279-02
3. Anastasopoulos, P. C. (2016). Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis. Analytic Methods in Accident Research, 11, 17–32.
4. Anastasopoulos, P. C., Shankar, V. N., Haddock, J. E., & Mannering, F. L. (2012). A multivariate tobit analysis of highway accident-injury-severity rates. Accident Analysis & Prevention, 45, 110–119.
5. Anderson, S. T., Parry, I. W., Sallee, J. M., & Fischer, C. (2011). Automobile fuel economy standards: Impacts, efficiency, and alternatives. Review of Environmental Economics and Policy, 5, 89–108.
6. Andrews, R. L., & Currim, I. S. (2003). Retention of latent segments in regression-based marketing models. International Journal of Research in Marketing, 20, 315–321.
7. Anwaar, A., Anastasopoulos, P., Ong, G. P., Labi, S., & Islam, M. B. (2012). Factors affecting highway safety, health care services, and motorization—an exploratory empirical analysis using aggregate data. Journal of Transportation Safety & Security, 4, 94–115.
8. Behnood, A., & Mannering, F. (2017). The effect of passengers on driver-injury severities in single-vehicle crashes: A random parameters heterogeneity-in-means approach. Analytic Methods in Accident Research, 14, 41–53.
9. Berry, I. M. (2010). The effects of driving style and vehicle performance on the real-world fuel consumption of US light-duty vehicles, Massachusetts Institute of Technology Libraries Archives.
10. Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44, 62–91.
11. ConsumerReports. (2013). The mpg Gap. Retrieved from: http://www.consumerreports.org/cro/magazine/2013/08/the-mpg-gap/index.htm (Accessed 06.05.17).
12. Dings, J., (2013). Mind the Gap! Why official car fuel economy figures don’t match up to reality. Transp. Environ.
13. EIA. (2016). Annual Energy Outlook 2016: with projections to 2040. US Energy Information Administration, US Department of Energy. Retrieved from: https://www.eia.gov/outlooks/aeo/pdf/0383(2016).pdf.
14. EIA. (2017). “How much gasoline does the United States consume? U.S. Energy Information Administration. Retrieved from: https://www.eia.gov/tools/faqs/faq.php?id =23&t=10.” Retrieved 13th June, 2017.
15. EPA. (2015). What’s in a Label? Exploring Fuel Economy and the Environment, US Department of Energy; Retrieved from: https://www.epa.gov/sites/production/files/documents/fueleconomyhighschool.pdf.https://www.epa. gov/sites/production/files/documents/fueleconomyhighschool.pdf.
16. G., D. K. (2008). Maximizing Benefits from Current Technology. S. Presentation at CARB symposium titled Reducing Greenhouse Gas Emissions from Passenger Vehicle, CA. 21 April 2008. Retrieved from: http://www.arb.ca.gov/cc/ccms/meetings/042108/4_21_current_techn_2_duleep.pdf.
17. Greene, D., Goeltz, R., Hopson, J., & Tworek, E. (2006). Analysis of in-use fuel economy shortfall by means of voluntarily reported fuel economy estimates. Transportation Research Record: Journal of the Transportation Research Board, 1983, 99–105.
18. Greene, D., Khattak, A. J., Liu, J., Hopson, J., Wang, X., & Goeltz, R. (2015). How Do Motorists' Own Fuel Economy Estimates Compare with Official Government Ratings? A Statistical Analysis. Retrieved from: http://bakercenter.utk.edu/wp-content/uploads/2015/09/BR_Greene_4-15.pdf. Howard H. Baker Jr Center for Public Policy, University of Tennessee. Knoxville.
19. Greene, D. L., Khattak, A. J., Liu, J., Wang, X., Hopson, J. L., & Goeltz, R. (2017). What is the evidence concerning the gap between on-road and Environmental Protection Agency fuel economy ratings?. Transport Policy, 53, 146–160.
20. Greene, D. L., Liu, J., Khattak, A. J., Wali, B., Hopson, J. L., and Goeltz, R., (2017). How does On-Road Fuel Economy Vary with Vehicle Cumulative Mileage and Daily Use?. Transportation Research Part D: Transport and Environment. DOI: 10.1016/j.trd.2017.06.004 (forthcoming).
21. Greene, W. H. (2011). Econometric Analysis (Seventh Edition, Pearson Publishers, U.K.).
22. Greene, W. H., & Hensher, D. A. (2010). Modeling ordered choices: A primer Cambridge University Press, U.K.
23. Greene, W. H., Hensher, D. A., & Rose, J. (2006). Accounting for heterogeneity in the variance of unobserved effects in mixed logit models. Transportation Research Part B: Methodological, 40, 75–92.
24. Greene, W. H., N. Harris, M., Hollingworth, B., & Maitra, P., (2008). A bivariate latent class correlated generalized ordered probit model with an application to modeling observed obesity levels.
25. Heckman, J. J. (1977). Sample selection bias as a specification error (with an application to the estimation of labor supply functions), National Bureau of Economic Research Cambridge, Mass., USA.
26. Heckman, J. J. (2010). Selection bias and self-selection. Microeconometrics, Springer: 201–224, Palgrave Macmillan, London, U.K.
27. Heiss, F., & Winschel, V. (2008). Likelihood approximation by numerical integration on sparse grids. Journal of Econometrics, 144, 62–80.
28. Helfand, G., & Wolverton, A. (2011). Evaluating the consumer response to fuel economy: A review of the literature. International Review of Environmental and Resource Economics, 5(2), 103–146.
29. Huo, H., He, K., Wang, M., & Yao, Z. (2012). Vehicle technologies, fuel-economy policies, and fuel-consumption rates of Chinese vehicles. Energy Policy, 43, 30–36.
30. Jacobsen, M. R. (2013). Evaluating US fuel economy standards in a model with producer and household heterogeneity. American Economic Journal: Economic Policy, 5, 148–187.
31. Khattak, A. J., & Rodriguez, D. (2005). Travel behavior in neo-traditional neighborhood developments: A case study in USA. Transportation Research Part A: Policy and Practice, 39, 481–500.
32. Khattak, A. J., Schofer, J. L., & Koppelman, F. S. (1995). Effect of traffic information on commuters' propensity to change route and departure time. Journal of Advanced Transportation, 29, 193–212.
33. Kurani, K. S., Sanguinetti, A., & Park, H. (2015). Actual results may vary: a behavioral review of eco-driving for policy makers. A white paper for the National Center for Sustainable Transportation.
34. Kurani, K. S., Stillwater, T., Jones, M., and Caperello, N., (2013). Ecodrive I-80: A Large Sample Fuel Economy Feedback Field Test Final Report. Report: ITS-RR-13-15, Davis, CA. Retrieved from https://fueleconomy. gov/feg/pdfs/EcoDrive% 20I-80.pdf.
35. Li, X., Khattak, A. J., & Wali, B. (2017). Large-scale traffic incident duration analysis: the role of multi-agency response and on-scene times. Transportation Research Record: Journal of the Transportation Research Board, 2616, 39. DOI: 10.3141/2616-05.
36. Li, X., Khattak, A. J., & Wali, B. (2017). Role of multiagency response and on-scene times in large-scale traffic incidents. Transportation Research Record: Journal of the Transportation Research Board, 2616, 39–48.
37. Lin, Z., & Greene, D. (2011). Predicting Individual Fuel Economy. SAE International Journal of Fuels and Lubricants, 4, 84–95. (2011-01-0618).
38. Liu, J., Khattak, A., & Wang, X. (2017). A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: implications for sustainable cities. International Journal of Sustainable Transportation, 11, 170–185.
39. Liu, J., & Khattak, A. J. (2016). Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles. Transportation Research Part C: emerging Technologies, 68, 83–100.
40. Liu, J., Wang, X., & Khattak, A. (2016). Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users. Transportation Research Part C: Emerging Technologies, 67, 280–298.
41. Ma, H., Xie, H., Huang, D., & Xiong, S. (2015). Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses. Transportation Research Part D: Transport and Environment, 41, 205–216.
42. Mannering, F. L., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic methods in accident research. Analytic Methods in Accident Research, 1, 1–22.
43. Mannering, F. L., Shankar, V., & Bhat, C. R. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 11, 1–16.
44. McNutt, B., Pirkey, D., Dulla, R., & Miller, C. (1978). A comparison of fuel economy results from EPA tests and actual in-use experience, 1974-1977 model year cars. SAE Technical Paper, 780037, DOI: https://doi.org/10.4271/780037.
45. McNutt, B. D., Dulla, R., Crawford, R., McAdams, H., & Morse, N. (1982). Comparison of EPA and on-road fuel economy–analysis approaches, trends, and impacts. SAE Technical Paper. 820788, DOI: https://doi.org/10.4271/820788.
46. Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: an exploratory empirical analysis. Accident Analysis & Prevention, 40, 260–266.
47. Mintz, M. M., Vyas, A. R., & Conley, L. A. (1993). Differences between EPA-test and in-use fuel economy: Are the correction factors correct? Argonne National Lab., IL (United States).
48. Mock, P., German, J., Bandivadekar, A., Riemersma, I., Ligterink, N., & Lambrecht, U. (2013). From laboratory to road: A comparison of official and real-world fuel consumption and CO2 values for cars in Europe and the United States, ICCT.
49. Russo, B. J., Savolainen, P. T., Schneider, I. V., W. H., & Anastasopoulos, P. C. (2014). Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model. Analytic Methods in Accident Research, 2, 21–29.
50. Rykowski, R. A., Nam, E. K., & Hoffman, G. (2005). On-road testing and characterization of fuel economy of light-duty vehicles. SAE Technical Paper. 2005-01-0677, DOI: https://doi.org/10.4271/2005-01-0677.
51. Sarwar, M. T., & Anastasopoulos, P. C. (2017). The effect of long term non-invasive pavement deterioration on accident injury-severity rates: A seemingly unrelated and multivariate equations approach. Analytic Methods in Accident Research, 13, 1–15.
52. Schneider, R., Freas, W., & McMahon, T. (1982). In-use fuel economy of 1981 passenger cars. SAE Technical Paper. 820790, DOI: https://doi.org/10.4271/820790.
53. Sharer, P., Leydier, R., & Rousseau, A. (2007). Impact of drive cycle aggressiveness and speed on HEVs fuel consumption sensitivity. SAE Technical Paper 2007-01-0281, DOI: https://doi.org/10.4271/2007-01-0281.
54. Tang, T. Q., Huang, H. J., & Shang, H. Y. (2015). Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions. Transportation Research Part D: Transport and Environment, 41, 423–432.
55. Train, K., (2003). Discrete choice methods with simulation. Cambridge University Press.
56. Venkataraman, N., Ulfarsson, G. F., & Shankar, V. N. (2013). Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type. Accident Analysis & Prevention, 59, 309–318.
57. Wali, B., Ahmed, A., & Ahmad, N. (2017). An ordered-probit analysis of enforcement of road speed limits. Proceedings of the Institution of Civil Engineers-Transport, Thomas Telford Ltd, 171(4), 1–10.
58. Wali, B., Ahmed, A., Iqbal, S., & Hussain, A. (2017b). Effectiveness of enforcement levels of speed limit and drink driving laws and associated factors–Exploratory empirical analysis using a bivariate ordered probit model. Journal of Traffic and Transportation Engineering (English Edition), 4(3), 272–279.
59. Wali, B., Greene, D. L., Khattak, A. J., & Liu, J. (2018b). Analyzing within garage fuel economy gaps to support vehicle purchasing decisions–A copula-based modeling & forecasting approach. Transportation Research Part D: Transport and Environment, 63, 186–208.
60. Wali, B., Khattak, A., & Xu, J. (2017c). Contributory fault and level of personal injury to drivers involved in head-on collisions: Application of copula-based bivariate ordinal models. Accident Analysis & Prevention, 110, 101–114. doi:10.1016/j.aap.2017.10.018
61. Wali, B., Khattak, A., Waters, J., Chimba, D., & Li, X. (2018). Development of safety performance functions for tennessee: unobserved heterogeneity & functional form analysis. Transportation Research Record: Journal of the Transportation Research Board (Accepted). DOI: 10.1177/0361198118767409.
62. Wang, X., Khattak, A. J., Liu, J., Masghati-Amoli, G., & Son, S. (2015). What is the level of volatility in instantaneous driving decisions?. Transportation Research Part C: Emerging Technologies, 58, 413–427.
63. Washington, S. P., Karlaftis, M. G., & Mannering, F. (2010). Statistical and econometric methods for transportation data analysis CRC press, Boca Raton, FL, USA.
64. Weiss, A. A. (1993). A bivariate ordered probit model with truncation: Helmet use and motorcycle injuries. Applied Statistics, 42, 487–499.
65. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press, Cambridge, Massachusetts, London, England.
66. Yamamoto, T., & Shankar, V. N. (2004). Bivariate ordered-response probit model of driver’s and passenger’s injury severities in collisions with fixed objects. Accident Analysis and Prevention, 36, 869–876.
67. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57, 348–368.