Public transportation systems operate in dynamic and unpredictable environments, necessitating close monitoring of real-world operations. Integrating data into route design, scheduling, and policy formulation is essential for creating an efficient and sustainable transit network. This thesis utilises historical bus travel time data to predict future bus travel times, enhancing the analysis of network reachability.

A literature review identified four prediction models to be investigated: Historical Average, Vector AutoRegression, Random Forest, and Long Short-Term Memory deep neural network. A case study involving six bus lines in Groningen was formulated, providing the necessary KoppelVlak 6 and GTFS schedule datasets to be used as inputs for the prediction models. During model development, patterns in historical travel time data were identified, and prediction accuracy was evaluated. The output from the most accurate prediction model was then utilised as input for the reachability analysis.

The analysis demonstrated that complex machine learning models, such as Random Forest and Long Short-Term Memory deep neural networks, yielded the most accurate predictions. The time of day when the journey occurs is particularly predictive of travel and dwell times. Integrating these predictions into a reachability analysis revealed instances of increased reachability, decreased reachability, and missed transfers compared to the original schedule.

The findings demonstrate that travel time prediction models can significantly enhance reachability analyses. This more accurate representation of reachability can be used to identify systemic issues in the design of the public transportation network, allowing for interventions to improve performance.