Enhancing Road Traffic Safety in- Kenya Using Artificial Neural Networks

Billington Muchiri; Dr. Solomon Mwanjele; Ms Grace Mwaura

Enhancing Road Traffic Safety in- Kenya Using Artificial Neural Networks

Billington Muchiri *

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Dr. Solomon Mwanjele

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Ms Grace Mwaura

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Enhancing Road Traffic Safety in- Kenya Using Artificial Neural Networks

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Abstract

The world loses a human live in every 24 second due to Road Traffic Accidents (RTAs). In Kenya approximately 3000 lives are lost annually due to RTAs. The interventions to improve road traffic safety (RTS) failed because they were not informed by any scientific research. In this paper we employed the multi-layer feed forward perceptron neural network model to classify the road traffic safety status (RTSS) as:-excellent, fair, poor or danger states which model’s output are. We considered the vehicle internal factors that contribute to RTAs as model’s inputs which included:-inside-vehicle-condition, entertainment, safety-awareness, passager’s (attention, criminal-history, health-history, movement inside vehicle, body posture, frequency of journey, drunkenness’, drug-influence, use-of-mobile-phone and load), luggagetype and the safetybelt. The model was trained, tested and validated with classical data collected from a sample of 1000 respondents from road traffic safety authority (RTSA) experts in Kenya.

References

7 Cites in Article

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Consolata Wangari Ndung'u,Ratemo,Matayo Bonface,Lydia Mwai (2015). Analysis of Causes & Response Strategies of Road Traffic Accidents in Kenya.
Gene Lesinski,Steven Corns,Cihan Dagli (2016). Application of an Artificial neural Network to predict Graduation Success at the United States military Academy.
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Antonio Celesti,Antonino Galletta,Lorenzo Carnevale,Maria Fazio,Aime Lay-Ekuakille,Massimo Villari (2018). An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Billington Muchiri, Dr. Solomon Mwanjele, Ms Grace Mwaura. 2019. "Enhancing Road Traffic Safety in- Kenya Using Artificial Neural Networks". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D4).

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GJCST Volume 19 Issue D4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification F.1.1

Submission ReceivedDecember 16, 2018
Peer Review Double Blind
Handling Editor
Accepted January 3, 2019
Published January 15, 2019

Version of record

v1.2

Issue date

November 14, 2019

Language

English

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Article Matrices

Total Score: 148

Country: Kenya

Subject: Global Journal of Computer Science and Technology

Authors: Billington Muchiri, Dr. Solomon Mwanjele, Ms Grace Mwaura (PhD/Dr. count: 1)

View Count (all-time): 476

Total Views (Real + Logic): 2009

Total Downloads (simulated): 100

Publish Date: 2019 01, Tue

Monthly Totals (Real + Logic):

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Total Views: 2K

Total Downloads: 100

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