Public transport planning is central to sustainable urban development and impacts accessibility, environmental quality, and equity. Effective decision-making in this domain requires extensive datasets and efficient data interpretation methods. This thesis explores the comparative effectiveness of Excel and map-based GIS interfaces and evaluates how interface design influences efficiency and accuracy in the context of macroscopic public transport planning.

Two models were developed: a map-based WebGIS model and an Excel-based model with tabular data visualization. Both models implement the same data, a relational dataset from Östergötland, Sweden, incorporating origin-destination relationships with generalized travel costs, cost indices, and trip volumes. Each model allows for scenario-based analysis, where users can alter the baseline data with simple decision-making. A total of 11 interviews were conducted where participants tested both models. The interviews included 20 quantitative tasks for each model and 11 qualitative questions regarding both models. The interviews provided insights into the efficiency and accuracy of both interfaces.

The results reveal that the WebGIS model generally solves tasks faster, but with less accuracy than the Excel model. The WebGIS model was 56% more efficient than the Excel model for tasks requiring comprehension of changes resulting from decision-making. It was 26 % faster for tasks requiring comprehension of data and 20 % faster when finding changes in data from decision-making. However, the WebGIS model exhibited slightly higher error rates, underscoring the trade-off between speed and accuracy in map-based interfaces. Conversely, the Excel model was 16 % faster for tasks requiring data inspection with higher accuracy.

Several potential sources of error may have skewed the results. Participants were generally more familiar with Excel, giving that model an advantage. The order of tasks was always the same, meaning this advantage was even more pronounced during the initial tasks. However, the primary limitations of this study are the small sample size and the simplified nature of the tasks, which were chosen to keep the interview duration under two hours.