Evaluation of Composite Performance Indicators for Helmet and Seat-belt Enforcement

Document Type : Research Paper

Authors

1 PhD Candidate, Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

2 Assistant Professor, Highways and Transportation, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

3 Assistant Professor, Construction Management, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

Abstract

Composite indicators are the collection of a group of sub-indicators to measure multidimensional principles that can't be measured by one indicator. Utilizing Composite Indicators (CIS) as like beneficial tools to assess overall performance of transportation safety analysis has extended of recent times. Amongst many models, the Data Envelopment Analysis Model (DEA) is these days an essential tool of constructing CIs amongst different performance methodologies these days. But DEA assumes that all data are specific or quantitative values to be used in basic DEA-based CI models. However, in some problems, the observed values of input and output data are sometimes imprecise or fuzzy statistics due to there are many variables in the road safety that follows natural ambiguity and often there ​​are imprecise values, which can only be represented by qualitative data. In this study, we studied two methods within the DEA framework for modeling quantitative and qualitative data within the context of constructing composite indicators. There are Imprecise DEA (IDEA) and Fuzzy DEA (FDEA) models respectively. Based on their principle, new models of IDEA-CIs and FDEA- CIs are obtained in the assessment of road safety management via developing a composite indicator of road safety for 30 countries round the world. Comparing the results are obtained from the two models with the primary data confirms the efficiency and reliability of the two models. In the end, targets were calculated for inefficient countries then Benchmarks were set and road safety strategies were prioritized for them.

Keywords


Behnood H.R., Ayati E., Hermans E. and Neghab M.A., (2014). Road safety performance evaluation and policy making by data envelopment analysis: A case study of provincial data in Iran. Scientia Iranica, 21(5), pp.1515-1528.
 
Behnood H.R., Ayati E., Brijs T., Neghab M.P. & Shen Y., (2017). A fuzzy decision support system in road safety planning. Proceedings of the Institution of Civil Engineers-Transport, Vol. 170, No. 5, pp. 305-317.
Charnes A., Cooper W., (1984). The non-Archimedean CCR ratio for efficiency analysis: a rejoinder to Boyd and Fare. Eur J Oper Res 15(3):333–334.
 
Charnes A., Cooper W.W., Rhodes E., (1978). Measuring the efficiency of decision-making units. Eur J Oper Res 2:429–444.
 
Chen F., Wu, J., Chen X., Wang J. and Wang D., (2016). Benchmarking road safety performance: Identifying a meaningful reference (best-in-class). Accident Analysis & Prevention, 86, pp.76-89.
 
Cherchye L., Moesen W., Rogge N., van Puyenbroeck T., (2007). An introduction to ‘benefit of the doubt’ composite indicators. Soc Indic Res 82:111–145.
 
Cook W.D., Kress M., Seiford L.M., (1993). On the use of ordinal data in data envelopment analysis. J Oper Res Soc 44:133–140.
 
Cook W.D., Kress M., Seiford L.M., (1996). Data envelopment analysis in the presence of both quantitative and qualitative factors. J Oper Res Soc 47:945–953.
 
Cooper W.W., Park K.S., Yu G., (1999). IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manage Sci 45:597–607.
 
Cooper W.W., Park K.S., Yu G., (2002). An illustrative application of IDEA (Imprecise Data Envelopment Analysis) to a Korean mobile telecommunication company. Oper Res 49(6):807–820.
 
Despotis D.K., Smirlis Y.G., (2002). Data envelopment analysis with imprecise data. Eur. J. Oper. 140, 24–36.
 
Emrouznejad A., Tavana M., (2014). Performance Measurement with Fuzzy Data Envelopment Analysis. In the series of “Studies in Fuzziness and Soft Computing”, Springer-Verlag.
 
Guo P., Tanaka H., (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets Syst 119:149–160.
 
Hermans E., (2009). A methodology for developing a composite road safety performance index for crosscountry comparison (No. D/2009/2451/11).
 
Hillier F.S. and Lieberman G.J., (2001). Introduction to Operations Research, 7th Ed., McGraw-Hill Higher Education.
 
Kao C., (2006). Interval efficiency measures in data envelopment analysis with imprecise data. Eur. Oper. Res. 174, 1087–1099.
 
Léon T., Liern V., Ruiz JL., Sirvent I., (2003). A fuzzy mathematical programming approach to the assessment of efficiency with DEA models. Fuzzy Sets Syst 139:407–419.
 
Shen Y., Ruan D., Hermans E., Brijs T., Wets G., Vanhoof K., (2011). Modeling qualitative data in data envelopment analysis for composite indicators. International Journal Systems Assurance Engineering and Management 2(1), 21–30.
 
Shen Y., Hermans E., Bao Q., Brijs T. and Wets G., (2020). Towards better road safety management: lessons learned from inter-national benchmarking. Accident Analysis & Prevention, 138, p.105484.
 
Siegel J.G. and Shim J.K., (2010). Dictionary of Accounting Terms, Barron's Business Guides, 5th Edition, Barron's snippet.
 
Tešić M., Hermans E., Lipovac K. and Pešić D., (2018). Identifying the most significant indicators of the total road safety performance index. Accident Analysis & Prevention, 113, pp.263-278.
 
Wegman F., (2017). The future of road safety: A worldwide perspective. IATSS research, 40(2), pp.66-71.
 
World Health Organization, (2018). Global status report on road safety 2018 (No. WHO/NMH/ NVI/18.20). World Health Organization
 
Zhou X., Pedrycz W., Kuang Y., & Zhang Z., (2016). Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation. Applied Soft Computing, 46, 424-440.
 
Zhu J. (2003). Imprecise data envelopment analysis (IDEA): a review and improvement with an application. European Journal of Operational Research, 144, 513–529.
 
Zhu J. (2003). Efficiency evaluation with strong ordinal input and output measures. European Journal of Operational Research, 146, 477–485.