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The effects of transportation energy policy on fuel consumption and transportation safety

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Abstract

This paper examined the impact of transportation energy policies on traffic safety through policy simulations. Considering the changes in the vehicle miles traveled (VMT) and in vehicle stock composition as a result of policy changes, the impacts of these changes on traffic accidents were examined in terms of the number of traffic accidents, traffic fatalities, and total accident costs. The main focus was on the following policy alternatives: Fuel tax, mileage based a VMT tax, Pay-as-you-drive (PAYD) and Pay-at-the-pump (PATP) insurance premium policy, and the Corporate Average Fuel Economy (CAFE) standards regulations. By integrating three interrelated economic demand decisions fully (size of the vehicle stock, use of the vehicle stock, and energy efficiency), the short-run, long-run and dynamic effects of a policy change can be predicted. The results showed that the share of light trucks will keep increasing in the future in all policy alternatives and that fuel consumption will decrease compared to the baseline in every scenario except for the VMT tax policy. The results also show that the fatality rates per vehicle miles traveled will decrease, but the CAFE policy will result in more fatalities and higher fatality rates compared to the baseline scenario. The results may provide guidance as to what would reduce the energy dependency while reducing the undesirable side effects related to traffic safety. The outcomes of this research will provide a set of specific results comparing policy scenarios in a consistent manner. The results will provide guidance as to whether the policy option would improve energy dependency while reducing the undesirable side effects, such as the problems related to the environment and the safety of motor vehicle travel.

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Notes

  1. Transportation Energy Data Book (26), Table 2.7, Table 4.17, and 4.18.

  2. For the purposes of compiling the DOT safety statistics, a fatality is defined as any injury that results in death within 30 days of a transportation crash, accident, or incident.

  3. Mass and weight are assumed to be interchangeable despite, conceptually, the two terms being distinct.

  4. Appendix 1 presents the full structural model based on system equations (1) and the estimation results.

  5. By further defining a κ ij as the accident involvement rate of crash severity κ (κ = F (fatality), H (injury), P (property damage only)), the number of vehicles involved can also be calculated according to the crash severity.

  6. The probability of a car-car crash, for example, can be calculated from the equation, \( p\left(C,C\right)=\left(\begin{array}{c}\hfill {V}_{C2}\hfill \\ {}\hfill 2\hfill \end{array}\right)/\left(\begin{array}{c}\hfill {V}_2\hfill \\ {}\hfill 2\hfill \end{array}\right) \), where \( \left(\begin{array}{c}\hfill {V}_{C2}\hfill \\ {}\hfill 2\hfill \end{array}\right) \) denotes the combination function of choosing two vehicles out of the number of VC2 and, by definition, \( \left(\begin{array}{c}\hfill {V}_{C2}\hfill \\ {}\hfill 1\hfill \end{array}\right)={V}_{C2} \).

  7. Busse et al. [3] examined the effects of fuel prices on car prices and market shares. They estimate the effect of fuel prices on new light duty vehicle shares with more segments of the LDV types (i.e. Compact, Midsize, Luxury, Sports, SUV, Pickup, and Minivan) using a linear probability model.

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Author information

Authors and Affiliations

  1. Korea Institute for Industrial Economics & Trade (KIET), 66 Heogiro, Dongdaemun-gu, Seoul, 130-742, Korea

    Chun-Kon Kim

  2. School of Computer Information Engineering, Sangji University, Usan-dong, Wonju-si, Gangwon-do, 220-702, Korea

    Kyungyong Chung

  3. Department of Packaging, Yonsei University, 1 Yonseidae-gil, Heungeop-myeon, Wonju-si, Gangwon-do, Korea

    Younjung Kim & Kang-Dae Lee

Authors
  1. Chun-Kon Kim
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  2. Kyungyong Chung
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  3. Younjung Kim
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  4. Kang-Dae Lee
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Corresponding author

Correspondence to Kang-Dae Lee.

Appendices

Appendices

1.1 Appendix 1: estimates of system equations

We estimate the full structural model based on system equations (1) and Table 3 shows the estimation results. Formally, then, the system is the following:

Table 3 Estimation results of system equations (1) using 3SLS
Full size table
$$ {(vma)}_t={\alpha}^m{(vma)}_{t-1}+{\alpha}^{mv}{(vehstock)}_t+{\beta}_1^m{(pm)}_t+{\beta}_3^m{X}_t^m+{u}_t^m,{(vehstock)}_t={\alpha}^v{(vehstock)}_{t-1}+{\alpha}^{vm}{(vma)}_t+{\beta}_1^v{(pv)}_t+{\beta}_2^v{(pm)}_t+{\beta}_3^v{X}_t^v+{u}_t^v,{\left(f\operatorname{int}\right)}_t={\alpha}^f{\left(f\operatorname{int}\right)}_{t-1}+{\alpha}^{fm}{(vma)}_t+{\beta}_1^f{(pf)}_t+{\beta}_2^f{(cafe)}_t+{\beta}_3^f{X}_t^f+{u}_t^f, $$
(A.1)

with error terms following the rule

$$ {u}_t^k={\rho}_t^k{u}_{t-1}^k+{\varepsilon}_t^k,\kern1em k=m,v,f. $$
(A.2)

See Small and Van Dender [16] for data sources and detailed description of how the variables were generated and estimated.

Appendix 2

Table 4 Data sources
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Kim, CK., Chung, K., Kim, Y. et al. The effects of transportation energy policy on fuel consumption and transportation safety. Multimed Tools Appl 74, 2535–2557 (2015). https://doi.org/10.1007/s11042-014-1974-6

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  • DOI: https://doi.org/10.1007/s11042-014-1974-6

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