By Helen Roberts, National Audit Office, UK
In June 2019, the UK became the first major economy to pass a law that requires net zero greenhouse gas emissions by 2050. How we move around and the modes of transport we use have a vast role to play in reaching this target. Transport is the UK’s highest greenhouse gas-emitting sector, with surface transport, aviation and shipping accounting for 34% of carbon dioxide (CO2) emissions in 2019. Between 1990 and 2019, CO2 emissions from the transport sector reduced by just 4.6%.
The UK Government has identified that a key part of decarbonising will be to accelerate modal shift to public and active transport. For people to be incentivised to make the switch away from personal cars, the public transport system in place needs to accessible, affordable and meet the needs of its various users.
Covid-19 has radically changed how, why, where and whether we travel. From the date of the first lockdown on the 23rd March to the end of 2020, bus use (excluding London) averaged 35% compared to pre-Covid levels, while National Rail use averaged just 22%. On the other hand, car use returned to near normal levels by the end of August. However, given the pressing need to reduce carbon emissions the public transport network will need to play an increasingly significant part in how we travel in the future.
Even prior to Covid-19, public transport provision played a key role in how people accessed services they needed – including healthcare, education, employment, and leisure facilities. In turn, people’s ability to access local services influences health, education and social outcomes for individuals.
However, the nature and availability of public transport services in England are locally determined and highly variable. The National Audit Office (NAO) wanted to explore whether unequal access to public transport may be disproportionally affecting those in disadvantaged communities and whether and how easily they can access services they need. To explore this, the NAO developed an interactive tool based on modelled journey time data and analyses.
A journey time tool
We used modelled data, published by the Department for Transport (DfT), which shows public transport journey times (as at 2017, the most recent data available) to service locations (including primary and secondary schools, FE colleges, hospitals, GP surgeries, centres of employment and town centres) from thousands of output area centroid starting points in England. We also obtained the locations of these services from DfT and used this information to calculate journey times to services for every output area in England. For the education and healthcare service locations, we also obtained data on service quality from Ofsted and the Care Quality Commission. This allowed us to calculate and compare journey times to the nearest service (of any rating) and the nearest good or outstanding service. This was a new piece of analysis, never undertaken before. We used location ratings given as close to 2017 as possible to match the journey time data we used. We recognise that some location ratings may have changed since then.
We also compared these calculated journey times with the Index of Multiple Deprivation (the measure of relative deprivation for areas in England) which allowed us to see how journey times varied by deprivation classification. (Full details of our methodology can be found in our published technical guide.)
Journey times: four findings and their implications
Our analysis demonstrates new insights available to government through innovative analysis and combinations of currently collated datasets.
1. Those who use or are reliant on public transport have longer average journey times to key local services than those with access to a car, although local variation exists.
Whilst fundamentally unsurprising, the increase in journey times can be substantial. Across all service types, the national average journey time by public transport to services was at least double that by car. Understanding the national average journey time by different modes, as well as frequency and reliability of public transport services are important in developing approaches to encourage modal shift from car travel.
2. Journey times cannot tell the whole story on accessibility of services. In deprived urban areas, where journey times are shorter, they need to be viewed alongside the effects of high deprivation.
We found that the most deprived urban areas in England consistently had the shortest journey times to services. But using journey time as the only metric of accessibility discounts other factors that may act as a barrier to services for these communities – such as: cost of travel; gender, age or religious barriers to travel; caring responsibilities; frequency and timing of transport services; capacity at a service; and service opening times.
3. When combined, data on service quality and journey time provide insight on equity of access to health and education services across England.
For example, on average in England, journey times by public transport to the nearest good or outstanding hospital were 70% longer than to the nearest hospital of any rating. Good and outstanding hospitals are not evenly spread out across England and longer public transport journey times (compared to car journey times) may compound this unevenness, putting better services out of reach of some users. In some parts of England, journey times were longer because of the scarcity of good and outstanding hospitals, rather than an inherent issue with transport accessibility to those hospitals. Considering service quality alongside travel time to the service gives decision-makers more information on equity, so they can decide how best to improve outcomes for users and, therefore, overall value for money.
4. Overall, we found that there is more government could be doing to improve the data it collects on journey times and how it uses this information to inform decision making.
Journey time data offer significant potential to enable more informed decision-making across government about the provision of public transport and the location and quality of other key services. Better collection and use of data on public transport journey times and access to key services could be used to develop an integrated local transport system that meets the needs of its users, as well as unlocking multiple benefits and supporting value for money across the provision of public services. Our innovative analysis demonstrated some examples of what could be achieved by combining datasets that government already collect and hold. Full detail on the finding of our analysis can be found in our published insights document.
About the authors: Dr Helen Roberts is a Senior Analyst on the Transport Value for Money team at the National Audit Office. She also leads the NAO’s analytical mapping discipline. Through both these roles, Helen has extensive experience of journey time analysis and examining it’s utility and application in supporting decision making across government.
Suggested further readings
Kotavaara, O., Antikainen, H., Marmion, M. and Rusanen, J. (2012), Scale in the effect of accessibility on population change: GIS and a statistical approach to road, air and rail accessibility in Finland, 1990–2008. The Geographical Journal. https://doi.org/10.1111/j.1475-4959.2012.00460.x
Tomintz, M.N., Clarke, G.P. and Rigby, J.E. (2008), The geography of smoking in Leeds: estimating individual smoking rates and the implications for the location of stop smoking services. Area. https://doi.org/10.1111/j.1475-4762.2008.00837.x
Page, N, Langford, M, Higgs, G. (2019) Exploring spatiotemporal variations in public library provision following a prolonged period of economic austerity: A GIS approach. Area.. https://doi.org/10.1111/area.12575