Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/47969
Title: Strengthening commercial motorcyclist safety in Tanzania: Application of the PRECEDE-PROCEED model for an evidence-based road safety awareness campaign
Authors: Chacha, Marwa
Katondo, Stella
Andrew, Augustino
Daudi, Juma
Nyaki, Prosper
CUENEN, Ariane 
YASAR, Ansar 
WETS, Geert 
Issue Date: 2025
Publisher: Elsevier
Source: African Transport Studies, 3 (Art N° 100039)
Abstract: Road safety among commercial motorcyclists in Tanzania is a significant concern, with high rates of traffic accidents posing risks to both riders and passengers. This study aimed to identify factors contributing to unsafe behaviours and prioritise road safety challenges for targeted awareness campaigns. Using the PRECEDE-PROCEED model, data were collected through surveys of 248 motorcyclists and group discussions with 16 stakeholders. The findings reveal several risk factors, including rider age, income, job satisfaction, and riding experience. Behavioural influences were categorized into predisposing factors, such as limited riding knowledge and negative perceptions of helmet and reflector use; enabling factors, including restricted access to training, lack of safety infrastructure, and inadequate policy enforcement; and reinforcing factors, such as cultural norms shaping safety practices. The study highlights that younger riders are particularly vulnerable to accidents, and many riders do not use helmets due to discomfort and affordability issues. In conclusion, the study recommends targeted safety campaigns to promote helmet use and reflective clothing, improved access to training programs, and stronger policy enforcement. These interventions are essential for reducing accidents and improving the safety and livelihoods of commercial motorcyclists.
Keywords: Commercial motorcyclists;Road safety campaigns;Tanzania;PRECEDE-PROCEED model;Needs assessment
Document URI: http://hdl.handle.net/1942/47969
DOI: 10.1016/j.aftran.2025.100039
Rights: 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A2
Type: Journal Contribution
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
1-s2.0-S2950196225000171.pdf
  Restricted Access
Published version1.74 MBAdobe PDFView/Open    Request a copy
Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.