Identification of Accident-Prone Road Sections by Using Relative Frequency Method

  • Ferit YAKAR Gaziosmanpasa University Civil Engineering Department
Keywords: Relative Frequency Method, Accident-Prone Road Sections, Road Safety

Abstract

In this study, assuming that traffic accident occurrence is determined by some road and environment related factors, and future traffic accidents will occur under the same conditions as past traffic accidents, use of Relative Frequency Method (RFM) (also called frequency ratio method) in the determination of accident-prone road sections is investigated. Method was tested on a highway in Trabzon province of Turkey. At the end of the study, sensitivity and specificity values were calculated as 1.00 and 0.83 respectively, which reflects that the method identified all of the 'accident-prone' sections (there is no false negative) and the method has very strong ability to distinguish 'relatively safe' sections. The most useful property of the method is that, if accident data does not exist due to any reason for some part of the road, method can be still used to identify accident-prone sections by using the road properties.

References

Ghaffari A, Kashani AT, and Moghimidarzi S. Identification of Black Spots Based on Reliability Approach. Promet – Traffic&Transportation. 2013; 25, 525-532.

Qin X, Ng M, Reyes PE. Identifying crash-prone locations with quantile regression. Accident Analysis and Prevention. 2010; 42, 1531–1537.

Miranda-Moreno L F, Labbe A, Fu L. Bayesian multiple testing procedures for hotspot identification. Accident Analysis and Prevention. 2007; 39, 1192–1201.

Hauer E. Observational Before-After Studies in Road Safety. Pergamon, Tarrytown, NY.1997.

Miaou SP, Song JJ. Bayesian ranking of sites for engineering safety improvement: decision parameter, treatability concept, statistical criterion and spatial dependence. Accident Analysis and Prevention. 2005; (37), 699–720.

Persaud BN, Hauer E. Comparison of two methods for de-biasing before and after accident studies. Transport. Res. Record. 1984; 975, 43–49.

Persaud BN. Safety migration, the influence of traffic volumes, and other issues in evaluating safety effectiveness. Transport. Res. Record. 1986; 1086, 33–41.

Cheng W, Washington S. Experimental evaluation of hotspot identification methods. Accident Analysis and Prevention. 2005; 37, 870–881.

Chang LY, and Chen WC. Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research. 2005; 36(4),365−375.

Abdel-Aty M, and Pande A. Crash data analysis: Collective vs. individual crash level approach. Journal of Safety Research. 2007; 38, 581–587.

Geurts K, Wets G. Black Spot Analysis Methods: Literature Review.2003. https://doclib.uhasselt.be/dspace/bitstream/1942/5004/1/17.pdf. 22.05.2014.

Elvik R. A survey of operational definitions of hazardous road locations in some European countries. Accident Analysis and Prevention. 2008; 40, 1830–1835.

Kwon OH, Park MJ, Yeo H, Chung K. Evaluating the performance of network screening methods for detecting high collision concentration locations on highways. Accident Analysis and Prevention. 2013; 51, 141– 149

Montella A. A comparative analysis of hotspot identification methods. Accident Analysis and Prevention. 2010; 42, 571–581.

Pirdavani A, Brijs T, Wets G. A Multiple Variables Decision‐Making Approach for Prioritizing Accident Hotspots in the Absence of Crash Data, Transport Reviews: A Transnational Transdisciplinary Journal. 2010; 30(1), 97-113.

Sadeghi A, Ayati E, and Neghab MP. Identification and Prioritization of Hazardous Road Locations by Segmentation and Data Envelopment Analysis Approach. Promet – Traffic&Transportation. 2013; 25, 127-136.

Akgun A. A comparison of landslide susceptibility maps produced by logistic regression, multi-variables decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides. 2012; 9, 93–106.

Yilmaz I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey), Computers & Geosciences. 2009; 35, 1125–1138.

Lee S, Sambath T. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol. 2006; 50(6):847–855.

Demir G, Aytekin M, Akgun A, İkizler SB, Tatar O. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards. 2013; 65:1481–1506.

Pradhan B, Latif ZA, Aman SNA. Application of airborne LiDAR derived parameters and probabilistic-based frequency ratio model in landslide susceptibility mapping. Appl Mech Mater. 2012; 225, 442–447.

Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. 2013; 7,725-742.

Lee S, Talib JA. Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology. 2005; 47, 982–990.

Ayalew L, Yamagishi H, Marui H and Kanno T. Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology. 2005; 81(4) 432-445.

Published
2015-12-21
How to Cite
1.
YAKAR F. Identification of Accident-Prone Road Sections by Using Relative Frequency Method. PROMET [Internet]. 2015Dec.21 [cited 2019Dec.14];27(6):539-47. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1609
Section
Articles