This study presents a comparative analysis of the three train-bus timetable synchronisation approaches in order to improve the performance and attractiveness of integrated public transport networks in rural areas. While the existing literature mainly focuses on synchronisation approaches separately, limited attention has been given to comparing the different synchronisation approaches, with the incorporation of passenger demand. To address this gap, this research develops a Mixed-Integer Linear Programming (MILP) model to compare the three synchronisation approaches: train-first, bus-first, and simultaneous synchronisation. Applied to a rural public transport network in Friesland, the model minimises total passenger travel time weighted by passenger demand. The case study, consisting of one train line and thirteen connecting bus services, provides a manageable evaluation of the model. The results show that simultaneous synchronisation consistently outperforms the sequential approaches, achieving the lowest total passenger waiting time for most operating hours and service lines. Comparing the simultaneous synchronisation approach to the current timetable shows significant improvements in total passenger waiting time across all operating hours and for most of the lines. A mode choice analysis using a Multinomial Logit (MNL) model shows an increase in ridership based on the improved timetable, which facilitates a modal shift from private car to public transport. These findings show that timetable synchronisation alone, without improving infrastructure or the addition of more services, can increase the attractiveness of the public transport network. These findings are especially important in rural areas where car dependency is typically higher. The results suggest that transport authorities should reconsider the current sequential timetable synchronisation practices. Optimisation of multimodal networks simultaneously can improve the performance and attractiveness of integrated public transport networks in rural areas.