Public Transportation in Kenya (A Phenomenological Study of Transport Issues)

Data Collection Methods

Author(s): Joseph Kamau Muguro*, Paul Waweru Njeri* and Minoru Sasaki *

Pp: 21-57 (37)

DOI: 10.2174/9789815238518124010004

* (Excluding Mailing and Handling)

Abstract

The section highlights significant shortcomings in the Kenyan data collection landscape, particularly as reported by the National Police Service (NPS) and the National Transport and Safety Authority (NTSA). Official sources of data are found to be lacking in quality, accuracy, and comprehensiveness, thus impeding a systematic approach to addressing transportation issues, particularly regarding accidents and fatalities. To overcome these limitations, the research proposes exploring alternative data sources such as social media platforms and surveys, combined with advanced analytical techniques like Natural Language Processing (NLP) and machine learning. This approach aims to gain deeper insights into the causes of accidents and factors contributing to high fatality rates. Recommendations include standardizing data collection schemes, implementing digital reporting and data integration, fostering interagency collaboration and mandatory reporting, establishing public awareness campaigns, adopting international best practices, and reviewing legislation and policies. These measures are essential for enhancing the quality and breadth of transportation data and improving road safety in Kenya.

“ One Important Role for the City is to Conduct Studies to Document Areas of Greatest Need, and to Facilitate Coordination between our Public and Private Transportation Options to Weave it into a Dense Tapestry of Accessible and Reliable Transportation.” — Mark Noble.


Keywords: Accident statistics, Kenyan transportation data, NLP, NTSA data, Twitter data, Topic modeling, User-generated data.

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