Data-Driven Transit Scheduling: Statistical Methods to Minimize Passenger Wait Times

Punctuality in bus operations is key to reducing passenger wait times and improving the appeal of public transport. This study presents a data-driven method to optimize bus schedules for Route 16 in Linköping, Sweden. Historical real-time and static bus traffic data (Sept 2021–Dec 2023) was retrieved and labelled using temporal and spatial groupings. Using both total and incremental departure delays, monthly clustering was performed via K-means++. Thereafter, statistical summaries were calculated and used in a grid search over scaled adjustments. New schedules were obtained using unseen data (Jan-June 2024), and optimal configurations were selected based on improvements in schedule adherence, with significance assessed via pooled-variance z-tests. The best strategy, adjustments using incremental delay data at the 50th percentile with s = 0.4, raised on-time performance from ∼ 68% to ∼ 73% (±0.21 pp, p < 0.01). The optimal method for weekends raised the performance to over 80%, reaching ¨Ostg¨otatrafikern’s punctuality goal. Gains varied by stop and time: some saw up to 33% improvement, others none. Morning rush-hour improved by 13% and night services by 2.8%. Percentile-based, stop-specific adjustments proved effective, while the choice of clustering scheme had limited influence on schedule adherence. Future research should incorporate a test dataset to more robustly estimate the performance gains of the best methods. To derive insights that are applicable in real-world settings, the approach must also be extended to include multiple transit routes. Additionally, there is potential to integrate passenger experience metrics into similar analyses to provide a more comprehensive evaluation.