Service reliability has an important impact on the satisfaction stated by public transport users with the service they receive. The main source of unreliability is found in headway variance, which also affects waiting times and distributes passengers unevenly across vehicles. However, it is still unclear how headway irregularity, with its impact in waiting, crowdedness and reliability, affect travellers' service satisfaction. Different stated preference studies have identified nonlinear impacts produced by overcrowding. However, none of these studies is directly related to users' satisfaction evaluation. In this study, we investigate the existence of this non-linearity in users' satisfaction caused by both the crowding level and the number of denied boardings through a post-service satisfaction survey of bus and metro users. An Ordered Logit Model was estimated, accounting for sample heteroscedasticity and preference heterogeneity. Overall, there is a significant and negative perception of the bus mode, keeping all other attributes equal. For users under 35 years old, comfort experienced almost always plays an important role in service satisfaction, while for those over 35 years old women are significantly more sensitive to this attribute. Most important, crowding has a negative and non-linear impact on how passengers evaluate their travel satisfaction. Using a Likert-type scale, this curve is convex. This relationship between crowding and satisfaction might bias service planning and delivery if performance indicators associated to service are not properly weighted by the number of passengers served. Improving level of service indicators in this direction might provide public transport agencies a clearer and more accurate perception of the actual users' experience.

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The most relevant issues related to headway variability in public transport planning, operations and quality of service are reviewed in this paper. We discuss the causes and consequences of headway variability, the alternative metrics that have been proposed to measure it, the preventive and reactive strategies to control headway variability in both research and practice, including the role of drivers and of present and future technology, and how service provision contracts might deal with headway variability through metrics and financial incentives. The most influential elements that explain headway variability along a route are the irregularity at which vehicles are dispatched, the scheduled frequency, the distance travelled or route length, the passenger demand and associated dwell times, and the number of stops. We conclude that there is a large gap between the state-of-the-art and the state-of-practice in terms of identification of headway variability issues, as well as in the development of mitigation and control measures. It is therefore paramount that future research will contribute to closing this gap by addressing organisational, contractual and technological barriers in the implementation of measures aimed at mitigating headway variability in public transport services.

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