Navigation guidance to pedestrians’ road accident safety in Kenya using Bayesian analysis
| dc.contributor.author | Kuria, Paul Munene | |
| dc.date.accessioned | 2022-08-02T06:28:38Z | |
| dc.date.available | 2022-08-02T06:28:38Z | |
| dc.date.issued | 2021 | |
| dc.description | A Thesis Submitted to the School of Computing and Engineering Sciences in Partial Fulfilment for the Requirement of the Degree of Master of Science in Information Technology of Strathmore University | en_US |
| dc.description.abstract | In most countries, policy makers find the safety of pedestrians to be a major concern because many pedestrians are involved in road traffic accidents. Roughly, 1.35 million people's lives every year are shuttered because of road traffic accident, among these fatalities pedestrians are most vulnerable road users given that the road infrastructure is designed with less or no consideration to safety of a pedestrian. Therefore, the purpose of this research was to propose a navigation aid system that informs pedestrians about the safety status of a given road route in Kenya. Experimental research was used because it facilitates the manipulation of variables which is essential in this study. In designing the system, the research implemented the Agile development methodology. The data used was from the secondary references which included traffic accident fatalities records from NTSA. The data was used to train a Bayesian Linear Regression model, which predicts the pedestrian’s road accident fatality likelihood in a given road route. The model’s results, posterior distribution likelihood, are presented in terms of color coding on the Google Maps routes via a mobile application, where red indicates high danger, red for high-medium, brown for medium, yellow for low-medium, green for low. The study found that pedestrians constitute 54.14% of total road accidents fatalities and pedestrian’s risk of being involved in a fatal road accident is influenced by following factors, population increase with dependency of region urbanization, the time of the day with 2000hrs-21000hrs posing highest risk, tendency of using same road route and gender in which a male’s risk is 73% higher in comparison to female’s risk. These findings were used in fine tuning the Bayesian model and enriching the system’s web portal reports. The implication of the research is that a pedestrian is able to know the safety status of a given road route in respect to road accidents via a mobile application enabling him/her to choose among the available routes and taking the necessary safety precautions based on the safety status of the chosen route. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/12913 | |
| dc.language.iso | en | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.subject | Pedestrians | en_US |
| dc.subject | Bayesian analysis | en_US |
| dc.subject | Bayesian network | en_US |
| dc.subject | Road accident | en_US |
| dc.title | Navigation guidance to pedestrians’ road accident safety in Kenya using Bayesian analysis | en_US |
| dc.type | Thesis | en_US |
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