Self-explainability is seen as an enabler for a broad acceptance of Organic Computing (OC) systems and the overarching field of self-adaptive and self-organising (SASO) systems within industry and society. Organic Traffic Control (OTC) is a system following the OC design principles to establish a self-adaptive and self-learning traffic management of intersection controllers in urban environments. Nevertheless, it is missing the self-explanation property. A step-by-step approach to self-explanation includes detecting the necessity for an explanation, then finding a cause for the observed behaviour, and finally translating the results in a good explanation for the different groups of users. We propose our research plan to utilise incident detection and classification to enable self-explainability in OTC. Additionally, we aim to generalise from our findings to enable self-explainability in multiple different SASO systems.