"Closing the gaps?"
Many women will be familiar with sharing their location with a friend before walking home at night, walking with keys in their hand, faking a phone conversation when feeling threatened, moving train carriages to find safety in numbers, planning a route three times as long to avoid a dodgy alleyway, and other ways of reducing the risks they face. It’s not all women, and it’s not just women, but chances are most women will know what I’m talking about. The strategies are many, and we just accept them as normal practice (even?though they are so very far from best practice).?
This observation is not new. Others have quite rightly pointed out that the problem with these practices - effective or not - is that they place responsibility on the victim. It would be preferable to change society's expectations about what is acceptable, rather than change what women have to do in response.?
We talk a lot about perceived risk in our daily work at TRL (for example this blog ). Actual risk may be the most important element in safety outcomes, but perceived risk is key when we consider behaviour. In the same way that many more people are afraid of flying than are afraid of car travel - despite the statistics telling us the reverse would be more logical - perceived risk dominates when we make our choices. This means that - regardless of the options as we put in place to enable modal shift, regardless of the effort poured into encouraging active travel - many will still choose the car, or worse will not feel able to travel at all, if we cannot change the risks (and the perceptions of these) they face. Without change, as ever, the most disadvantaged will be disproportionately affected.
What can we do? Changing society or even the whole of transport, is not easy, but change is beginning. More women in the industry is a start, and the great work being done in STEM in schools will hopefully drive us in the right direction over a longer timescale. But the key point is data, or more accurately, lack of data.?
Many readers will be aware of the enlightening book ‘Invisible Women’ by Caroline Criado Perez which starkly outlines the data bias towards men.?Things that we know ‘work’ – like seat belts – turn out to be working differently for different groups of people; woman are more likely to be injured in a collision than men. We cannot uncover these issues without collecting the data, and we cannot address these issues without evaluating the data to understand what is going on.
Other articles will highlight some of the great work TRL and others have been doing to try and increase representation of women, both in datasets and in the industry. Data gaps and biases are not unique to women of course, men are have been underrepresented in most of the young driver research TRL has carried out over the last 30 years (see PPR828- Transforming the Practical Driving Test for an example) and many minority or 'difficult to reach' groups are not studied due to practical constraints; this project is a great example of how this can be tackled (in this case with young male drivers from poor backgrounds).
One of my favourite concepts has always been ‘intelligent disobedience’ – introduced to me through learning about the training of guide dogs; broadly, it is the concept of going against instructions in order to achieve a better, and safer, outcome. For the guide dogs, it is disobeying an order when they know it would put their owner in danger; for us, it is not accepting the status quo when it puts some people at greater risk. As well as making sure that new transport solutions have everybody’s needs built in from the start, we need to intelligently question and re-evaluate our existing systems from a new perspective. And before we can close the gaps, we need to know where they are and what causes them.
We need evaluation to know what works, but it’s not enough to only know what works; we need to know who it works for too.
And the answer must be everybody.?
This article was written by Jill Weekley - Principal Consultant at TRL for Monitoring and Evaluation