An Algorithm for predicting road accidents based on traffic offence data

Simple item page
dc.contributor.authorJwan, Levice Obongo
dc.date.accessioned2017-11-20T06:48:20Z
dc.date.available2017-11-20T06:48:20Z
dc.date.issued2017
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Computer-Based Information Systems (MSIS) at Strathmore Universityen_US
dc.description.abstractDrivers with multiple records of road traffic violations for instance speeding, driving under influence of alcohol and using mobile phones while driving have been considered as a high risk group for possible involvement in road accidents. Studies have shown that there are links between these reckless behaviors and road accidents. It is therefore critical that such drivers be identified early in advance to eliminate that likelihood. Currently, the road traffic offence data collected by National Transport and Safety Authority for instance speeding and drunk driving data is solely used for reporting and prosecution hence not adequately utilized in ensuring road safety. Effective utilization of these data can positively impact road safety management since authorities can put in place mitigation mechanisms in order to prevent the frequent road accidents. The algorithm-based system developed in this study makes use of traffic offence data to predict the likelihood of a driver causing road accident. Data was gathered using close-ended questionnaires and interviews. The questionnaires and interviews intended to determine causes of road accidents and specific aspects about; booking an offender, relaying of traffic accident data and the need for a system among users within the transport sector in Kenya. Three categories of respondents were used; the National Transport and Safety Authority, the Kenya Police and the motorists. Similar questionnaires were given to the police and the NTSA officials while the motorists had their own set of questions. From the research, it emerged that the major causes of accidents in Kenya were; speeding, dangerous overlapping and drunk driving. Of the 37 respondents; 22 supported the algorithm-based system, indicating a 59.47% approval for the system. The implication of the research is that there will be more people booked for traffic offences and it will be possible for law enforcement to know the risk level of a driver based on the offences committed.en_US
dc.identifier.urihttp://hdl.handle.net/11071/5608
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectRoad Traffic Offenceen_US
dc.subjectRoad Safetyen_US
dc.subjectRoad Accident Predictive Modelsen_US
dc.subjectCrash-prediction Model -- multilane roadsen_US
dc.subjectTracking Road Offendersen_US
dc.titleAn Algorithm for predicting road accidents based on traffic offence dataen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
An Algorithm for predicting road accidents based on traffic offence data.pdf
Size:
955.08 KB
Format:
Adobe Portable Document Format
Description:
Fulltext thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
MSIS Theses and Dissertations (2017)