Peak-hour congestion remains a persistent challenge in many countries, including the Netherlands. A key factor behind congestion is the timing of daily commuting trips. Although many studies have examined why commuters choose specific departure times, most analyses focus on average behaviour. In reality, commuters often vary their departure times from day to day, and these variations differ strongly between individuals. Understanding this variability is important for designing policies that aim to shift travel away from peak hours.

This thesis investigates departure-time variability among Dutch car commuters using high-resolution travel data from the Nederlands Verplaatsingspanel (NVP). The dataset contains GPS-based travel records that make it possible to observe actual commuting behaviour across multiple days. To capture both daily patterns and differences between individuals, a multi-level Latent Class Cluster Model (ML-LCCM) is applied. This modelling approach groups commuting days into clusters with similar timing patterns, while simultaneously classifying individuals into broader behavioural profiles based on the variability in their daily schedules.

The results reveal that departure-time behaviour is highly heterogeneous. Several distinct day-level patterns emerge, ranging from highly regular commuting days to days with large timing variability. At the person level, different types of commuters can be identified, including individuals with very stable commuting schedules as well as those with much more flexible and irregular travel patterns. These behavioural profiles suggest that commuters differ substantially in their ability or willingness to adjust departure times.

The findings highlight that policies aimed at reducing peak-hour congestion should account for these differences in flexibility between commuters. Measures that assume all travellers can easily shift their travel times may therefore have limited effectiveness. By identifying distinct behavioural profiles, this research contributes to a more nuanced understanding of commuter behaviour and provides insights that can support the design of more targeted mobility policies.