Automatic vehicle location (AVL) and automatic passenger counting (APC) systems can generate a huge quantity and variety of operational, spatial, and temporal data. This potentially allows the discovery of new ways to enhance service quality and transport efficiency by utilizing AVL-APC inputs. There is currently no framework for implementing full service quality improvement cycles from automated data (Boyle 2008) and this motivates our case study. The objective of this research is to apply a digital ecosystem metaphor that extends the use of AVL and APC data for the benefit of transit agencies. It is intended that this framework will capture relevant data for stakeholders and enable the discovery of otherwise hidden trends that help explain irregularities in operations and suggest new avenues for service improvement. The framework is divided into two components: proactive adaptation and reactive adaptation.