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Complex systems modeling for evaluating potential impact of traffic safety policies: a case on drug-involved fatal crashes

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Abstract

Driving under the influence of illicit drugs is a critical road safety and public health concern. The U.S. National Drug Control Strategy has set a goal in 2010 to lower drugged driving significantly. In this study we presented a complex systems approach and developed a system dynamics (SD) model of drugged driving for assessing the impact of drugged driving per se law on the crash fatalities over time. The experimental analyses presented the behavioral change on the trend of number of annual drug-related fatally injured drivers when per se law is implemented with certain effect and investigated on the impact of drugged driving per se law on the number drug-related fatally injured drivers. By considering multiple interrelated factors that may influence drugged driving behaviors, the SD model was helpful in analyzing the potential “real world” impact of policy interventions on improving roadway safety and the behavior of drivers given the road infrastructure. Analyses showed that per se law would have negative exponential effect on the drugged driving fatalities over time and the policy effect would require time to be visible. In addition, combining policies of drugged driving and investing on public transportation would cause a higher change over time on reversing the trend of number of drugged driving-related crashes, however, cost effectiveness of policies still need further investigation.

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Authors and Affiliations

  1. Department of Supply Chain Management and Analytics, College of Business Administration, University of Nebraska-Lincoln, P.O.Box 880491, Lincoln, NE, 68588-0491, USA

    Ozgur M. Araz

  2. Department of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, Omaha, USA

    Fernando A. Wilson

  3. Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA

    Jim P. Stimpson

Authors
  1. Ozgur M. Araz
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  2. Fernando A. Wilson
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  3. Jim P. Stimpson
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Corresponding author

Correspondence to Ozgur M. Araz.

Appendix

Appendix

1.1 List of abbreviation in the model

DD:

Drugged drivers

Dec:

Decrease

DFID:

Drug-related fatally injured drivers

DU:

Drug users

Inc:

Increase

PS:

Per se law

PTMT:

Public transit miles traveled

TS:

Traffic safety

Veh:

Vehicle

VMT:

Vehicle miles traveled

See Tables 2, 3 and 4.

Table 2 Comparison between published data and SD estimation of the number of fatal crashes involving drugged drivers: 2005–2009
Full size table
Table 3 Model Validation Statistics
Full size table
Table 4 Values of combined intervention effects on average annual decreasing % of DFIDs from 2010 to 2015
Full size table

1.2 Model Equations

  1. 1.

    Annual DFIDs = Drug-related crash rate * Drugged Drivers (DD)

  2. 2.

    Congestion (Travel Time Index) = 0.4236 + 0.0023 * (Vehicle Miles Traveled (VMT)/Highway Capacity (Lane-miles)

  3. 3.

    DD Dec = Drugged Drivers (DD) * MAX(Effect of drugged driving law (PS DD Dec rate), (Effect of drugged driving law (PS DD Dec rate) + Normal effect of traffic safety policies (TS DD Dec rate)”))

  4. 4.

    DD Inc = Normal DD rate * DU Dec

  5. 5.

    Nondriving Drug Users (DU) = INTEG (DU Inc-DU Dec, 3.5132e + 007)

  6. 6.

    Drug-crash risk = 20.52 * (1 − Public transit adequacy)

  7. 7.

    Drug-related crash rate = Drug-crash risk * Normal crash rate

  8. 8.

    Drug-related Fatal Injured Drivers (DFIDs) = INTEG (SMOOTH(Annual DFIDs, 1),0)

  9. 9.

    Drugged Drivers (DD) = INTEG (DD Inc-DD Dec, 1.1e + 007)

  10. 10.

    DU Dec = Nondriving Drug Users (DU) * (DU treatment # + Other Dec rate)

  11. 11.

    DU Inc = Non User Population * DU initiation rate * Non User Population/(Non User Population + Total Drug Users)

  12. 12.

    Effect of congestion on PTMT = Congestion (Travel Time Index)/1.255

  13. 13.

    Effect of DFIDs on PTMT = Tolerable DFIDs * (DELAY1(Drug-related Fatal Injured Drivers (DFIDs), Time))

  14. 14.

    Effect of drugged driving law (PS DD Dec rate) = 0 + 0 * (STEP(0.05, 15) + STEP(0.05, 2016) + STEP(0.05, 2017) + STEP(0.05, 2018) + STEP(0.05, 2019))

  15. 15.

    Gasoline price = WITH LOOKUP (Initial price + 0.27 * (Time-2002), ([(0,0)-(43,10)], (1, 1.65), (2,1.88), (3,2.16), (4,2.55), (5,2.78), (6,2.93), (7,3.3), (8,2.41)))

  16. 16.

    Highway Capacity (Lane-miles) = INTEG (Lane-mile Inc, 8.29817e + 006)

  17. 17.

    Lane-mile Inc = Highway Capacity (Lane-miles) * Lane-mile Inc rate

  18. 18.

    Normal crash rate = (Normal fatal crash #/(Vehicle Miles Traveled (VMT))) * Congestion (Travel Time Index)

  19. 19.

    PTMT Inc per capita = Public transit adequacy * (1 + Effect of congestion on PTMT) * (1 + Effect of DFIDs on PTMT) + (Public transit adequacy * (1 + Effect of congestion on PTMT + Effect of DFIDs on PTMT))

  20. 20.

    Public transit adequacy = 0 + (STEP(0.005, 2015) + ((STEP(0.005, 2016) + STEP(0.005, 2017) + STEP(0.005, 2018) + STEP(0.005, 2019) + STEP(0.005, 2020) + STEP(0.005, 2021) + STEP(0.005, 2022) + STEP(0.005, 2023) + STEP(0.005, 2024) + STEP(0.005, 2025))))

  21. 21.

    Total Drug Users = Nondriving Drug Users (DU) + Drugged Drivers (DD)

  22. 22.

    Vehicle Miles Traveled (VMT) = INTEG (VMT Inc and Dec, 2.87361e + 009)

  23. 23.

    VMT Inc and Dec = VMT per vehicle-(Public Transit Miles Traveled (PTMT) Inc)

  24. 24.

    VMT per vehicle = (Vehicle Miles Traveled (VMT)/Vehicles)/Gasoline price * 0.654

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Araz, O.M., Wilson, F.A. & Stimpson, J.P. Complex systems modeling for evaluating potential impact of traffic safety policies: a case on drug-involved fatal crashes. Ann Oper Res 291, 37–58 (2020). https://doi.org/10.1007/s10479-018-2961-5

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