Towards a Digital Twin of Seychelles' Road Transport System

Abstract

Seychelles faces a fast growth in car ownership. Infrastructure improvements cannot keep pace with the grown mobility demand, resulting in an increasing congestion problem during peak hours. This thesis aims to build a reliable microscopic traffic model for Seychelles’ main island Mahé. It focuses on motorized private transport. I used openly available map data to build a model in the open-source traffic simulation software SUMO. The traffic demand is based on existing reports, but I refined it following the current change in mobility behavior. I performed traffic counts using drones to calibrate the model to the actual traffic situation. The validation showed a good fit for the current situation. However, the SARS-CoV-2 pandemic may have impacted the traffic counts. Then, I demonstrated the utility of the traffic model in a case study. I assessed the pedestrianization of major roads in the capital city in four scenarios. All scenarios showed negative effects on the road transport network. Nevertheless, pedestrianization also has many positive aspects that a traffic model can not assess. During the entire thesis, I worked in close collaboration with Seychelles’ Department of Transport, following a transdisciplinary approach. The authorities should be able to use the developed traffic model to assess infrastructure improvements and prioritize projects based on their benefits.