Modelling Sustainable Transportation Systems by Applying Supervised Machine Learning Techniques

Public transportation has been reeling under the coronavirus pandemic. To curb the spread of Covid-19 national governments-imposed lockdown regulations at various scales. The transport industry in developing countries bore the initial brunt of lockdowns leading to the grounding of fleets. Ostensibly, very little has been documented on the mechanisms adopted and implemented to develop sustainable mobility solutions in developing countries during the pandemic. Consequently, using the city of Johannesburg as a case study this paper adopted a quantitative research approach to investigate commuters’ perceptions and expectations of the quality of service during the Covid-19 pandemic. Using Supervised Machine Learning techniques, a quality-of-service model was developed to assess the quality of service and inform approaches for sustainable increasing public transport ridership. The results show that there was an increase in retail and recreation-based trips and a decline in work-based trips. This was due to an increase in telework (working from home) during the Covid-19 pandemic. The finding also reveals machine learning techniques can be used to understand commuters’ cognitive decisions or their final outcomes. The trip duration was the most influential feature of the city of Johannesburg also experiments using information gain reveal that increased investment to improve other public transportation features such as reliability and accessibility leads to an increase in public transport ridership. In conclusion, the paper calls for intensified investment in innovative approaches to plan for sustainable public transportation post the Covid-19 pandemic. This can be achieved through upscaling existing uses of technology such as using machine learning in scenario planning.