The data presented in this report indicates that the use of active travel modes varies by gender, age, and income. These differences can be fairly high, depending on the indicator examined. For example, cycling for pleasure or to keep fit is one behaviour that can be over three times more prominent for those with the highest annual net income, while the use of walking for short journeys is the behaviour most often displayed among those earning under £10,000 a year. Thus, cycling for pleasure and access to bike are patterned by income, while walking serves a utilitarian purpose for those earning the lowest. Yet, these individuals also reported the second highest proportion of participants citing health reasons/inability to walk far as their main issue for not walking more and potentially having to rely on a car.

Looking at gender differences, it is evident that men are about twice as likely to have access to bikes than women, and women feel slightly less safe when cycling compared to men. As such, men and those in higher income brackets are more likely to report access to and use of cycling as a mode of travel.

The data on travel to school shows that younger pupils are more likely to be driven, whereas older ones take a bus more often. This likely reflects the lack of decision-making power among younger children on how they are taken to school, and might be driven there on the way to their parents’ workplace. Regardless, walking is still the predominant mode of traveling to school for all age groups and the gap between walking and driving increases with the child’s age. For example, driving is consistently slightly higher for nursery-aged children, even though walking is also reported at a high level, being the predominant mode for over 40% of households surveyed.


This report further highlighted than around 50% of individuals were not able to identify any specific barriers for walking more than they do; however, distance, concerns about cycling in traffic, and weather were cited as reasons for not cycling to work more often. Conversely, Sustrans’ (2020) report highlighted things such as wider pavements, less cars parked on those areas, and more 20-minute neighbourhoods as factors that would encourage a higher propensity among people to walk, wheel and cycle.

Two things are important to keep in mind while interpreting the results discussed here. Firstly, the data for the year 2020 has been affected by the Covid-19 pandemic and the behavioural changes that emerged as a result of the Government mandated restrictions. As such, caution is needed when interpreting these findings, especially if using them to support other strategies. For example, the fact that there was an increase in the proportion of people walking in 2020, does not directly indicate that it would be feasible to maintain such behaviours over a longer period of time, unless wider social, economic, and cultural implications are considered as well (e.g., returning to classrooms, phased return to work/hybrid working, return of public transport services to its pre-pandemic frequency).

Secondly, the data presented here is collected on a national level. It is acknowledged that while gathering national level data is important, to demonstrate high level trends, there is a need to gather data on a local, as well as neighbourhood level, to fully inform the current status of active travel infrastructure and behaviours. While Sustrans published a Walking and Cycling Index Report in May 2022, which provides slightly more nuanced data, that too is on a city level (for Scotland). Having a comprehensive micro level dataset is crucial, especially for a country as socially and geographically diverse as Scotland, wherein life in one area can be completely different to a lifestyle in another. As such, to develop recommendations to improve accessibility, availability, and feasibility of using active travel modes in the daily life of individuals, more granular level data is required. This underscores the relevance of having conversations with the active travel delivery partners to improve data collection which will adequately capture this level of information.