Satisfactory public transportation (PT) should enable people to reach attractive destinations and desired activities fast, comfortably, safely, and affordably. When PT fails to do so it will have negative effects on the overall accessibility in a society. Evaluating a PT system essentially means measuring to what extent the demand from the users is met, and for such an analysis understanding the concept of accessibility is paramount. Whether an individual will experience a high or a low level of accessibility will likely depend on their personal capabilities, as well as on the surrounding environment. Barriers obstructing an individual from using PT could for example be of physical of phycological nature or come in the shape of public space management disproportionally favoring certain groups of society. Low accessibility can thus be linked to social exclusion, since when a person cannot reach important destinations, their chances to participate in society will be subdued. To measure the accessibility of a PT system, and how a PT system affects the overall accessibility of a destination, it is common practice to use indicators that can represent different categories of social exclusion. This approach was the basis for constructing the performance measurement tool called Bus Stop Ranking Algorithm (BSRA) which was created in the QGIS application Graphical Modeler. BSRA calculates the usefulness of bus stops by counting the number of vulnerable groups, the number of workplaces, and the total population within comfortable walking distance from bus stops, as well as comparing travel times by car and bicycle from residential areas to important locations. The tool was ordered by a private PT company which will use it to make decisions regarding e.g., creating new bus stops, or for relocating, removing, or redesigning existing bus stops or bus routes. The Swedish municipality Lidingö was used as the study area to demonstrate how to use BSRA and how to interpret its output. Using equal weights for all indicators, it was discovered that 9 bus stops in the southern part of Lidingö could be regarded as particularly useful compared to the other 207 bus stops in the municipality. Variables such as the space-temporal component, i.e., changes during the day were not used. Socio economic factors such as segregation were also not highlighted, since all indicators had the same effect on the total scores. Adjusting the weights for some indicators could expose underlying dynamics affecting the total scores for the bus stops and help the PT company make design changes where they will be needed the most.