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Road Safety Assessment of U.S. States: A Joint Frontier and Neural Network Modeling Approach

Egilmez, Gokhan
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1374854708

Abstract Details

2013, Master of Science (MS), Ohio University, Civil Engineering (Engineering and Technology).
In this thesis, road safety assessment and prediction modeling for U.S. states fatal crashes are addressed. In the first part, a DEA-based Malmquist Index model was developed to assess the relative efficiency and productivity of U.S. states in decreasing the number of road fatalities. Even though the national trend in fatal crashes has reached to the lowest level since 1949 (Traffic Safety Annual Assessment Highlights, 2010), a state-by-state analysis and comparison has not been studied considering other characteristics of the holistic national road safety assessment problem in any work in the literature or organizational reports. The single output, fatal crashes, and five inputs were aggregated into single road safety score and utilized in the DEA-based Malmquist Index mathematical model. The period of 2002-2008 was considered due to data availability for the inputs and the output considered. According to the results, there is a slight negative productivity (an average of -0.2 percent productivity) observed in the U.S. on minimizing the number of fatal crashes along with an average of 2.1 percent efficiency decline and 1.8 percent technological improvement. The productivity in reducing the fatal crashes can only be attributed to the technological growth since there is a negative efficiency growth is occurred. It can be concluded that even though there is a declining trend observed in the fatality rates, the efficiency of states in utilizing societal and economical resources towards the goal of zero fatality is not still efficient. In the second part, a nonparametric prediction model, Artificial Neural Network, was developed to assist policy makers in minimizing fatal crashes across the United States. Seven input variables from four safety performance input domains while fatal crashes was utilized as the single output variable for the scope of the research. Artificial Neural Networks (ANN) was utilized and the best neural network model was developed out of 1000 networks. The proposed neural network model predicted data with 84 percent coefficient of determination. In addition, developed ANN model was benchmarked with a multiple linear regression model and outperformed in all performance metrics including r, R2 and the standard error of estimate. A sensitivity analysis was also conducted and the results indicated that road length, vehicle miles traveled, and safety expenditures were the top three input variables on fatal crashes. In conclusion, more effective policy making towards increasing safety belt usage and better utilization of safety expenditures to improve road condition are derived as the key areas to focus on for state highway safety agencies from the scope of current research. This research also reveals the significance of the relationship between the four input domains and fatal crashes for the United States from a holistic perspective and offers a robust nonparametric model to policy makers for the prediction of fatal crashes.
Deborah McAvoy, Ph.D. (Advisor)
Byung-Cheol Kim, Ph.D. (Committee Member)
Ken Walsh, Ph.D. (Committee Member)
M. Khurrum S. Bhutta, Ph.D. (Committee Member)
78 p.
Civil Engineering; Industrial Engineering; Transportation
Road Safety Assessment; Benchmarking; Data Envelopment Analysis; Malmquist Productivity Index; Nonparametric Predictive Modeling; Artificial Neural Networks; Machine Learning; US States

Recommended Citations

Citations

  • Egilmez, G. (2013). Road Safety Assessment of U.S. States: A Joint Frontier and Neural Network Modeling Approach [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1374854708

    APA Style (7th edition)

  • Egilmez, Gokhan. Road Safety Assessment of U.S. States: A Joint Frontier and Neural Network Modeling Approach. 2013. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1374854708.

    MLA Style (8th edition)

  • Egilmez, Gokhan. "Road Safety Assessment of U.S. States: A Joint Frontier and Neural Network Modeling Approach." Master's thesis, Ohio University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1374854708

    Chicago Manual of Style (17th edition)

ohiou1374854708
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© 2013, some rights reserved.Creative Commons License Road Safety Assessment of U.S. States: A Joint Frontier and Neural Network Modeling Approach by Gokhan Egilmez is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Ohio University and OhioLINK.