Hotspots identification and ranking for road safety improvement: an alternative approach.

During the last decade, the concept of composite performance index, brought from economic and business statistics, has become a popular practice in the field of road safety, namely for the identification and classification of worst performing areas or time slots also known as hotspots. The overall quality of a composite index depends upon the complexity of phenomena of interest as well as the relevance of the methodological approach used to aggregate the various indicators into a single composite index. However, current aggregation methods used to estimate the composite road safety performance index suffer from various deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, the weighting of the indicators as well as their high "degree of freedom" which enables one to readily manipulate them to produce desired outcomes (Munda and Nardo, 2003, 2005, 2009). The objective of this study is to contribute to the ongoing research effort on the estimation of road safety composite index for hotspots' identification and ranking. The aggregation method for constructing the composite road safety performance index introduced in this paper, strives to minimize the aforementioned deficiencies of the current approaches. Furthermore, this new method can be viewed as an intelligent decision support system for road safety performance evaluation, in order to prioritize interventions for road safety improvement.

[1]  Fred Wegman,et al.  SUNflowerNext: Towards A Composite Road Safety Performance Index , 2008 .

[2]  Geert Wets,et al.  Combining road safety information in a performance index. , 2008, Accident; analysis and prevention.

[3]  W. Härdle,et al.  Applied Multivariate Statistical Analysis , 2003 .

[4]  Geert Wets,et al.  A hybrid system of neural networks and rough sets for road safety performance indicators , 2010, Soft Comput..

[5]  Giuseppe Munda,et al.  Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting , 2009 .

[6]  Geert Wets,et al.  Benchmarking road safety: lessons to learn from a data envelopment analysis. , 2009, Accident; analysis and prevention.

[7]  Victoria Gitelman,et al.  Designing a composite indicator for road safety. , 2010 .

[8]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[9]  Thomas L. Saaty,et al.  Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation , 1990 .

[10]  Margaret M. Peden,et al.  World Report on Road Traffic Injury Prevention , 2004 .

[11]  Ronan A Lyons,et al.  Using multiple datasets to understand trends in serious road traffic casualties. , 2008, Accident; analysis and prevention.

[12]  Stefano Tarantola,et al.  Handbook on Constructing Composite Indicators: Methodology and User Guide , 2005 .

[13]  Wang Peng,et al.  Analysis of Time Distribution in Traffic Accident Based on Fuzzy Assessment Method , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[14]  Davy Janssens,et al.  Improved hierarchical fuzzy TOPSIS for road safety performance evaluation , 2012, Knowl. Based Syst..

[15]  Tom Brijs,et al.  Road safety risk evaluation and target setting using data envelopment analysis and its extensions. , 2012, Accident; analysis and prevention.

[16]  Ghazwan Al Haji,et al.  Towards a road safety development index (RSDI). Development of an international index to measure road safety performance , 2005 .