Road traffic conditions in Kenya: Exploring the policies and traffic cultures from unstructured user-generated data using NLP
Road traffic accidents (RTA) are a prevalent cause of fatality with African countries having the highest fatality index (25–34 per quota). The World Health Organization estimates Kenya's fatality rate due to RTA at 28 per quota. From literature, the country's fatality and injuries have increased by 26% and 46.5%, respectively, since the year 2015. The country is faced with incomplete RTA data capturing, hindering effective planning and policy adjustments to curb the menace. In this paper, the authors scrapped user-generated data (Twitter) and national transport and safety authority's (NTSA) reports to shed light on traffic safety, practices, and cultures in the country. To this end, the authors gathered 1,000,000 tweets and 8000 speeding entries between 2015 and 2021 and performed natural language processing (NLP) and quantitative study of the data. The authors applied NLP and n-gram search of keywords to categorize data into 8 topics: traffic, public service vehicle (PSVs), policing, accident, infrastructure, recklessness, robbery, and corruption. From the data, policing, which touches on all police and law-enforcement-related activity was found to be highly correlated with PSVs, recklessness, accidents, traffic congestion, robbery, infrastructure, and corruption with indices of r(76) = 0.92, 0. 91, 0.87, 0.82, 0.81, 0.76, and 0.70, respectively with p < 0.001. The topic modeling confirmed the identified topics to be the latent discussion issues affecting the public. From the study, PSVs, policing and traffic flow were isolated as key issues that ought to be addressed immediately. The research recommended the integration of driver monitoring systems to strengthen policing. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03861112
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Muguro, Joseph
- Njeri, Waweru
- Matsushita, Kojiro
- Sasaki, Minoru
- Publication Date: 2022-10
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 329-344
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Serial:
- IATSS Research
- Volume: 46
- Issue Number: 3
- Publisher: Elsevier
- ISSN: 0386-1112
- Serial URL: http://www.sciencedirect.com/science/journal/03861112
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Crashes; Data mining; Law enforcement; Social media; Traffic safety
- Identifier Terms: Twitter
- Geographic Terms: Kenya
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01843661
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 10:07AM