All Title Author
Keywords Abstract


Identifying and Ranking Dangerous Road Segments a Case of Hawassa-Shashemene-Bulbula Two-Lane Two-Way Rural Highway, Ethiopia

DOI: 10.4236/jtts.2018.83009, PP. 151-174

Keywords: Dangerous Road Segment, Empirical Bayes Estimate, Safety Performance Function, Potential for Safety Improvement, Countermeasures

Full-Text   Cite this paper   Add to My Lib

Abstract:

According to the study made by United Nation Economic Commission for Africa, Ethiopia stands as one of the worst countries with respect to road safety performance in terms of traffic accident fatalities per 10,000 vehicles (i.e. 95 in 2007/8). Road safety generally depends on humans, vehicles, and highway conditions. These factors influence road safety separately or in combination. One of the basic means to improve road safety is to reduce hazardous conditions of roads. The main objective of this study is to identify and rank hazardous locations and propose appropriate simple and inexpensive countermeasures along Hawassa-Shashemene-Bulbula main two-lane rural road. Accordingly, the road and traffic data were collected from field investigation and Ethiopian Road Authority and accident data were gathered from police stations. Then, the study road equally divided into short sections of 1.5 km and traffic volume and accident frequencies assigned for each road site to predict theoretical frequencies of accident. Empirical Bayes method and Safety Performance Function have been used to estimate an index known as Potential for Safety Improvement (PSI) for each site of the study area to identify and rank road sites. The result showed that out of 43 road segments 22 of them were identified as dangerous road segments. Moreover, based on further criterion established for screening the ranked road sections 8 road segments were found the most dangerous road segments as they have contributed 76% of total PSI values. The degree of haphazardness of a given road segment in the study area has directly associated with the availability of risk indicating road and traffic factors. Finally, it recommends that regulatory body of road safety in the study area should give high priority and immediate response for the improvement of most dangerous road segments.

References

[1]  United Nation Economic Commission for Africa (2009) Case Study Report on Road Safety in Ethiopia.
[2]  Ethiopian Road Authority (ERA) (2004) Road Safety Audit Manual. Federal Democratic Republic of Ethiopia, Addis Ababa.
[3]  World Health Organization (2009) Global Status Report on Road Safety Time for Action Geneva.
http://www.who.int/violence_injury_prevention/road_safety_status/2009/en/
[4]  World Health Organization (2015) Global Status Report on Road Safety.
http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/
[5]  TRL (2001) Ross Silcock Partnership, Study Report for a Sectoral Road Safety Program in Ethiopia. Volume 1, Transport Research Laboratory, Addis Ababa.
[6]  Berhanu, G. (2000) Effects of Road and Traffic Factors on Road Safety in Ethiopia. Norwegian University of Science and Technology, Trondheim.
[7]  Segni, G. (2007) Causes of Road Traffic Accidents and Possible Counter Measures on Addis Ababa—Shashemene Road. Master’s Thesis, Addis Ababa University, Addis Ababa.
[8]  Elvik, R. (2008) The Predictive Validity of Empirical Bayes Estimates of Road Safety. Accident Analysis & Prevention.
https://doi.org/10.1016/j.aap.2008.07.007
[9]  Hauer, E. (1986) On the Estimation of the Expected Number of Accidents. Accident Analysis & Prevention 18, 1-12.
[10]  Stokes, R.W. and Mutabazi, M.I. (1996) Rate-Quality Controls Method of Identifying Hazardous Road Locations. Transport Research Record, 1542, 44-48.
[11]  McGuigan D.R.D. ,et al. (1981)The Use of Relationship between Road Accident and Traffic Flow in Black-Spot Identification Traffic Engineering and Control 22, 448-453.
[12]  Maher, M.J. and Mountain, L.J. (1988) The Identification of Accident Black Spots: A Comparison of Current Methods. Accident Analysis and Prevention, 20, 143-151.
[13]  Hauer, E. (1997) Observational Before—After Studies in Road Safety. Pergamon Publications, London.
[14]  American Association of State Highway and Transportation Officials (AASHTO) (2009) Highway Safety Manual.
[15]  PIARC (2003) Road Safety Manual. World Roads Association.
[16]  Persaud, B., Lyon, G. and Nguyen, T. (1999) Empirical Bayes Procedure for Ranking Sites for Safety Investigation by Potential for Safety Improvement. Transportation Research Record, 1665, 7-12.
https://doi.org/10.3141/1665-02
[17]  Saccomanno, F.F., Grossi, R., Greco, D. and Mehmood, A. (2001) Identifying Black Spots along Highway SS107 in Southern Italy using Two Models. Journal of Transportation Engineering, 6, 551-521.
[18]  Carlin, B.P. and Louis, T.A. (1997) Bayes and Empirical Bayes Methods for Data Analysis. Statistics and Computing, 7, 153-154.
[19]  Cheng, W. and Washington, S.P. (2005) Experimental Evaluation of Hotspot Identification Methods. Accident Analysis & Prevention, 37, 870-881.
[20]  Directive 2008/96/Ecof, The European Parliament and of the Council of 19 November 2008 on Road Infrastructure Safety Management.
[21]  Hauer E. ,et al. (1992)Empirical Bayes Approach to the Estimation of Unsafely—The Multivariate Regression Method Accident Analysis & Prevention 24, 457-477.
[22]  Hauer, E., Harwood, D.W., Council, F.M. and Griffith, M.S. (2002) Estimating Safety by the Empirical Bayes Method: A Tutorial. Transportation Research Record: Journal of the Research Board, No. 1784, 126-131.
[23]  Hauer, E., Persaud, B.N., Smiley, A. and Duncan, D. (1991) Estimating the Accident Potential of an Ontario Driver. Accident Analysis & Prevention, 23, 133-152.
[24]  Tunaru R. ,et al. (2002)Hierarchical Bayesian Models for Multiple Count Data Austrian Journal of Statistics 31, 221-229.
[25]  Ethiopian Road Authority (ERA) (2001) Geometric Design Manual. Federal Democratic Republic of Ethiopia, Addis Ababa.
[26]  Dissanayeke, S. and Ratnayake, I. (2006) Statistical Modeling of Crash Frequency on Rural Freeways and Two-Lane Highways using Negative Binomial Distribution. Advance in Transportation Studies an International Journal, 8 Section B 9.
[27]  Miaou, S.-P. (1996) Measuring the Goodness-of-Fit of Accident Prediction Models. FHWA-RD-96-040, Federal Highway Administration, Washington DC.
[28]  Wood, G.R. (2002) Generalized Linear Accident Models and Goodness-of-Fit Testing. Accident Analysis & Prevention, 34, 417.
https://doi.org/10.1016/S0001-4575(01)00037-9

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal