Public transportation plays a crucial role in people’s lives. High-quality public transportation is essential for individual well-being and promotes economic growth and productivity. Importantly, a reliable and efficient bus service can encourage people to opt for public transport over private vehicles, aiding transport authorities in their efforts to reduce road congestion. Bus service reliability is a pivotal factor in determining service quality for passengers. Given bus services' inherent complexity and stochastic behaviour, understanding the factors contributing to service unreliability is of utmost importance for transport authorities.

This study adopts a before-and-after approach, focusing on a specific corridor in Christchurch known as Riccarton Road. It reviews the literature on several types of bus performance indicators and employs the coefficient of Variation (CoV) to assess the level of improvement in bus service components along this corridor before and after the implementation of bus priority changes. Additionally, the study investigates the presence of a correlation between dwell times at successive bus stops, link times at successive links, and the correlation between dwell times and link times. The significance of modelling for transport authorities in this context is invaluable. Accurate models can immensely benefit transport authorities in the planning and monitoring bus services, allowing them to identify bottlenecks, assess the impact of various interventions like bus priority measures, and allocate resources more efficiently. By employing both macro-simulation and micro-simulation models, this study aims to equip transport authorities with the tools necessary for maintaining and improving service reliability.

Various sources of variation have been identified as contributory factors in bus service performance. Historically, macro-simulation models in existing studies have often ignored link time and dwell time distributions. Furthermore, no study has considered the correlation between link times and dwell times when assessing bus service reliability. This study leverages data from before and after the implementation of bus priority measures to investigate these overlooked aspects. It explores the correlation between various components of bus travel time and how accounting for these correlations can enhance the accuracy of macro-simulation models.

The micro-simulation model developed in this study goes further by considering the interaction between buses and other vehicles at bus stops. This helps in comparing the reliability of bus services with and without bus priority measures at stops.

The study concludes by offering a comprehensive comparison between micro-simulation and macro- simulation models. This comparison covers various aspects, from the accuracy of the models to the level of effort required to develop them, the skills needed, the level of detail each model offers, and the amount of data each requires. It also delves into their respective applications in the planning and monitoring of bus services, providing a holistic view for transport authorities to make informed decisions.