Analysis Of Road Traffic Violations In Addis Ababa City (the Cause Of Arada Sub-city)

Road traffic injury death rates are highest in the African region. Violation ofrntraffic rules, which is a human factor, is an important factor behind traffic crashes and manyrnlives could be saved if all drivers complied with the rules and regulations of road. Other thanrnpiece of reports from traffic police, very few is known about magnitude, trends andrncharacteristics of traffic offences in Addis Ababa City. In addition, almost no literatures existsrnon differences in traffic offences by driver and vehicle characteristics in Addis Ababa city. Thernobjective of this research is to characterize and identify most common traffic offences, trendsrnand magnitudes of traffic offences in Arada sub city over 3 years. Further, this study alsorninvestigates differences in traffic offences by drivers and vehicle characteristics. Finally, thisrnstudy finds attitudes of traffic offenders towards unsafe driving behaviors. rnMethodology-To achieve the objectives of this research two methods were followed. The firstrnmethod was secondary traffic offence data collection from Arada sub city over (2007E.C2010E.C)rnrnand analyzed. The second method was through questionnaires results from 385rntraffic offenders at sub city traffic police department who come to take back their drivingrnlicense and vehicle plate after paying traffic offence fines. Questions containing traffic offenderrnand vehicle characteristics, history on traffic crashes and frequency of traffic offences forrnselected traffic offence types and attitudes towards unsafe driving behaviors were prepared.rnThese collected data were analyzed by Excel and SPSS; Independent t-test, one-way ANOVArntest, Chi-square, Descriptive and frequencies were used. rnResults-Over all, 154436 traffic offences registered, 95.6% occurred by male. It showed anrnincrease of 8.3% and 13.04% in 2008 E.C and 2009 E.C respectively. But, the rate is decreasingrnfor female averagely by 2.4% and increasing for male averagely by 13.7%. The most commonrntraffic offences were traffic flow obstruction (12.9%), disrespecting prohibiting signs (11.3%),rnparking of motor vehicles on prohibited areas (8.5%), overloading (8.2%) and using mobilernphone while driving (7.8%). Youth groups (18-30 years) are the most traffic offenders than anyrnother age category and drivers older than 50 were the second traffic offenders as compared tornlicensed driver population in the city. rnDrivers with license level 4, 5 and license level 6 (with old licensing system) were morerninvolved in traffic offences than others as compared to their corresponding license populationrnin the city. Code 1, 5 and others (T, CD, UN, ET and police) vehicles showed higher involvement in risk factors of road traffic crashes. Further, vehicles coded 1 showed higherrninvolvement in total traffic offences. rnResults of Chi-square showed that there is an association of driver and vehicle characteristicsrnand crash involvements. Traffic offenders with degree and above education level, 11-15 yearsrnof driving experience, low income, code 3, new vehicles with (0-5 service year) and privatelyrnowned vehicles showed higher involvement in traffic crashes than others . Results showed thatrnmale (p-value=0.002), in-friendship or cohabited (p-value=0.0022), 3-5 years of drivingrnexperience (p-value=0.036), taxis (p-value=0.036), medium income (p-value=0.001) trafficrnoffenders showed higher significant score in speeding at 5%. In case of red-light running,rntraffic offenders of 24-29 years old, (p-value=0.012), private workers, (p-value=0.003), vehiclernservice year, 6-10 years (p-value=0.001) showed significant higher score. Finally, respondentsrnhave negative attitudes towards unsafe driving behaviors but they still don’t completelyrntranslate into positive behaviors. rnConclusions- It can be concluded that male, 18-30 years, vehicles coded-1 (taxis), coded-3rn(commercial vehicles) and coded-5, and drivers with license level-4, license level-5 and licensernlevel-6 were highly connected to traffic offences. In addition, young age (24-29years), newrnvehicles (0-5years), single and private workers, higher education levels (degree and above),rnexperienced drivers, Buses and private cars also showed higher connection with number ofrntraffic offences and crash involvements. More stringent effort and proper interventions shouldrnbe done towards these groups and the most common traffic

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