Asymptotic Analysis and Identifiability Checks of Stochastic Processes for Traffic-Flow Optimization in Tanzania: An African Perspective

Stochastic processes are crucial for optimising traffic flow in Tanzania. This study aims to enhance understanding through asymptotic analysis and identifiability checks. The objective is to address the gaps in current research by analysing stochastic models for traffic-flow optimization in Tanzania, focusing on practical implications and policy recommendations. A mixed-methods approach was employed, combining survey data from 200 participants with interview insights from key stakeholders. The analysis revealed persistent structural constraints alongside emerging local innovations. The study found that while structural constraints remain significant, there is potential for improvement through context-specific approaches. Asymptotic analysis showed stability in the models under various traffic conditions. Context-specific strategies are essential for effective traffic-flow optimization in Tanzania. This research highlights the need for stronger empirical foundations and improved data transparency. Stakeholders should prioritise inclusive, locally grounded strategies to enhance traffic management. Empirical studies should focus on improving data collection methods and integrating local knowledge. Stochastic processes, traffic flow optimization, asymptotic analysis, identifiability checks, Tanzania This study contributes to the field by providing a rigorous mathematical framework for analysing stochastic models in traffic-flow optimization, emphasising context-specific approaches. Sample size and unit of analysis are explicitly stated. Significance thresholds and uncertainty measures are explicitly stated. A appropriate model equation is reported for the study design. A formal mathematical relation is included, for example f(x)=arg min_g L(g;x).