Abstract
Globally, transportation systems are under pressure to adopt and ensure more sustainable, inclusive, and efficient systems as cities become more complex and connected. Consequently, in the quest to develop a functional, sustainable, and inclusive transportation system, it is mandatory to first determine the key characteristics that set apart various public transportation options. Distinguishing between modes of transportation enables a detailed examination beyond superficial characteristics, resulting in better system optimization, evidence-based, and better-informed decision-making. Moreover, accurate classification of public transportation data is essential for comprehending mobility trends, achieving more guided urban planning. In this paper, an SVM model was trained and evaluated for the classification of minibus taxis, buses, and ride-hailing using data collected from the survey, to find the main attributes that distinguish the two modes of transport from each other based on users' evaluation of their performance. The overarching objective is to enable policymakers to identify best practices across modes. By understanding what works well in one transport mode, lessons can be drawn and adapted to improve the performance and integration of others. The SVM model achieved a high accuracy of 90% in validation, demonstrating the effectiveness of the adopted approach. The findings reveal underlying factors that influence mobility trends, patterns, and behaviours. Overall, the SVM informs a user-centered approach to transport policy, enabling the design of interventions that respond directly to the factors that matter most to commuters. Ultimately, the insights derived from the SVM model provide evidence-based recommendations for the development of future sustainable public transport in African cities. They also inform better practices that empower planners to design a customer-centric transportation system that is better suited to fulfil the desires and requirements of the community.