Bayesian Approach for Analysis of Road Traffic Accident (The Case of Addis Ababa
| dc.contributor.advisor | Bekele, Rahel (PhD) | |
| dc.contributor.author | Tabor, Alemayehu | |
| dc.date.accessioned | 2018-11-23T14:51:00Z | |
| dc.date.accessioned | 2023-11-29T04:57:22Z | |
| dc.date.available | 2018-11-23T14:51:00Z | |
| dc.date.available | 2023-11-29T04:57:22Z | |
| dc.date.issued | 2009-01 | |
| dc.description.abstract | Road traffic accidents are among the top leading causes of deaths and various levels of injuries in the world. Ethiopian is one of the developing countries where the situation is becoming worse and worse form time to time. The country is experiencing highest rate of such accident result in fatalities and a high economic loss. Addis Ababa, the capital city of Ethiopia, accounts for approximately more than 21% of the fatal accidents, 42% of the injury accident and 65% of the total accidents reported in the whole country. This thesis reports the study carried out to develop accident predictive models based on the data collected on road accident in Addis Ababa. The BN power predictor and constructor were used for prediction and model construction purposes respectively. As a result relating or finding interrelatedness between the road and traffic flow explanatory variables and building a significant accident predictive models was possible. In doing so the potential applicability of Bayesian network to help traffic accident data analysis in decision-making process was explored. In the thesis, the process of building a model using Bayesian network tools and techniques from historical road accident record data is explained. Different tools and techniques are also used for the purpose of data analysis. The methodology adopted consisted of basic steps of data collection in which all the records are selected and extracted from Addis Ababa traffic office; data preparation which includes tasks such as data transformation, deriving of new attributes, and handling of missing values, and finally model building and validation using the selected tools and techniques. In the first experiment, a best learned model that can classify accidents well with a better accuracy as serious, crash or property damage was selected and evaluated. The second experiment was also conducted after the necessary input of the domain experts is added. Experiment results reveal that the model built with mentioned techniques and tools are very much helpful in identifying the potential contributors or causes of this ever-growing challenge of the road transport in Addis Ababa and their interrelatedness. The whole research process can be a good input for further research works. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/14474 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Road traffic accidents | en_US |
| dc.subject | are among the top leading causes | en_US |
| dc.title | Bayesian Approach for Analysis of Road Traffic Accident (The Case of Addis Ababa | en_US |
| dc.type | Thesis | en_US |