title: Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.
abstract: Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research.
authors.affiliation: Dynatrend Construction Ltd. Bowie, MD 20720, United States. Electronic address: tcmbakwe@yahoo.com.
authors.forename: Anthony C
authors.initials: AC
authors.lastname: Mbakwe
authors.affiliation: Department of Transportation and Urban Infrastructure Studies, School of Engineering, Morgan State University, 1700 E. Coldspring Lane, Baltimore, MD 21251, United States. Electronic address: Anthony.Saka@morgan.edu.
authors.forename: Anthony A
authors.initials: AA
authors.lastname: Saka
authors.affiliation: Department of Transportation Systems Engineering, Ajou University Suwon, Gyeonggi 446-701, Republic of Korea. Electronic address: keechoo@ajou.ac.kr.
authors.forename: Keechoo
authors.initials: K
authors.lastname: Choi
authors.affiliation: Department of Transportation and Urban Infrastructure Studies, School of Engineering, Morgan State University, 1700 E. Coldspring Lane, Baltimore, MD 21251, United States. Electronic address: YoungJae.Lee@morgan.edu.
authors.forename: Young-Jae
authors.initials: YJ
authors.lastname: Lee
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D001334:Automobile Driving / Q000592:standards*; D001499:Bayes Theorem; D003697:Delphi Technique; D003906:Developing Countries; D004779:Environment Design / Q000592:standards*; D006801:Humans; D056910:Republic of Korea; D012307:Risk Factors; D012449:Safety
keywords: Bayesian network; Delphi technique; Developing countries; Forecasting accident rate; Korea; Traffic safety