Approach to Identifying Black Spots Based on Potential Saving in Accident Costs

  • Huy Huu Nguyen Prince of Songkla University
  • Pichai Taneerananon Prince of Songkla University
  • Paramet Luathep Prince of Songkla University
Keywords: SAPO, safety potential, hot spots, black spots, hazardous locations.

Abstract

In improving road safety, the identification of black spots based on potential saving in accident costs is an attempt to make the selection of black spots to treat out of the identified ones. This selection is based on a new approach in which safety potential is employed as a key parameter which has a dual function of identification and prioritization. With this approach, it is possible to find black spots where safety improvement measures are expected to have the greatest economical effectiveness. Therefore, the approach may be a practically suitable tool for developing countries in road traffic accident reduction effort. This paper intends to introduce the new approach to identify road accident black spots in detail. First, the evolution of criteria for black spot identification is reviewed. What follows is an analytical framework for identifying black spots based on potential saving in accident costs. Finally, a particular case of practical implementation is enclosed in order to illustrate the approach.

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Author Biographies

Huy Huu Nguyen

EU-Asia Road Safety Centre of Excellence (RoSCoE), Department of Civil Engineering, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand

Pichai Taneerananon

Centre for Road Safety Research, Department of Civil Engineering, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand

Paramet Luathep

Centre for Road Safety Research, Department of Civil Engineering, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand

Published In
Vol 20 No 2, May 18, 2016
How to Cite
[1]
H. H. Nguyen, P. Taneerananon, and P. Luathep, “Approach to Identifying Black Spots Based on Potential Saving in Accident Costs”, Eng. J., vol. 20, no. 2, pp. 109-122, May 2016.