Is your innovation really necessary?

Some thoughts on innovation, research and continuous improvement in transport planning and transport modelling.

Tim Gent, June 2019

Over recent years it seems as if innovation has become not just ubiquitous but almost obligatory in any discussion of transport planning and transport modelling. This has led me to wonder whether we really understand what we mean by ‘innovation’. Do we appreciate what innovation is, do we consider carefully where to place our effort, and do we balance the risks and rewards of developing new approaches?

I was lucky recently to be able to chair a session at Modelling World on Innovation, which allowed me some scope to consider these points. This article is a summary of my thoughts in preparing that session.

Innovation is a ‘good thing’

I should first say that I am an enthusiast for innovation, improvement and research. We are constantly faced with new challenges which require our approaches to evolve, and opportunities related to new transport modes, new data sources and technology.

Moreover, innovation is a motivating factor for many people working in transport. We like to be doing new things, and being at the cutting edge . It’s creative, interesting and gives a sense of moving forward.

However, this does beg the question of whether all change requires innovation. Certainly there must be room for continuous improvement, and does innovation differ from research and development?

Defining our terms

It’s good to define terms, and some searching on Google throws up some useful definitions:

“Innovation: The process of translating an idea or invention into a good or service that creates value or for which customers will pay.”

“To be called an innovation, an idea must … satisfy a specific need. ” 

https://www.businessdictionary.com/definition/innovation.html

“Creativity is thinking of something new. Innovation is the implementation of something new” @PaulSloane

These definitions emphasise implementing an improvement to meet a specific need. This makes clear that when we innovate, we should first have a specific need in mind, and that we should have a clear idea of what the process of implementation will look like.

We should also keep in mind alternatives. Is it possible to close the gap by continuous improvement of our current approaches? Or is pure research needed to better understand either the problem or potential solutions?

The diagram below attempts to summarise this relationship as follows:

• We require change because our current solution is not fully meeting our needs. There is a gap, which we can clearly define;
• Continuous improvement of our current solution is always an option. Of course, we can innovate to improve, so some innovation builds on our current approach;
• Other innovations replace what we are doing and start again, but the common thread is that we are working towards a defined and clear requirement;
• If the requirement is not clear, or the change required is not yet defined, then we are undertaking research.

Should we believe the hype?

The Gartner Hypecycle should be pretty familiar to anyone working with innovation. It serves as a useful reminder that new technologies will often trigger high expectations. Often these are too high, and lead to a failure to deliver and disillusionment. But with experience comes enlightenment about how innovations can be put to productive use.

This has played out many times in recent years, a notable example for transport modellers being mobile phone data (MPD). A few years ago this seemed to offer a solution to the age-old problem of transport matrix building, and early results were certainly greeted with excitement. However, there were hidden problems with the mobile phone data itself, such as the true extent of the mobile phone ‘cells’ and difficulty in identifying a trip. Ultimately, with the strengths and weaknesses fully recognised, MPD is taking its place alongside existing methods and data sources.

So the hypecycle helps us anticipate how thinking about innovations may develop. However, I believe there are a couple of points here that are often missed.

First, Gartner is a company which exists to help investors, and ultimately their interest is in return on investment. Second, clearly the curve presented is not a strict rule! New approaches may be ‘hyped’ to differing extents, and they may also never climb out of the trough of disillusionment. Occasionally a new idea may even become productive without being over-hyped (examples of this will be gratefully received!).

It’s easy to think of this from an investor’s perspective: you would want to invest early in several technologies close to the ‘trigger’, and preferably move your money close to the ‘peak’!

However, if you are a transport practitioner or other stakeholder, whether in consultancy or the public sector, what is the best way to respond? Arguably we should be really interested in which approaches will get to the Plateau of Productivity. Perhaps we should pay more attention to what is in the Trough of Disillusionment? If the hypecycle shows anything, it is that more certain returns may come from sifting through innovations whose weaknesses are already well understood.

Developing a new idea

Another way to consider progress in developing new ideas is shown in the diagram below. Finding a new idea and mobilising can be a slow process, but can often be followed by rapid progress with prototyping and demonstrations. These stages certainly require care and effort, but those pale into insignificance when we come to the delivery stage, when we must make an idea work in the real world.

"Could we start again please?" or "New approach, same problem"

What this often means in practice is that our original approaches never seem to QUITE deliver what we want. This is all too familiar to transport modellers and transport planners alike. Our solutions often deliver a lot of what we want, but seem to fail at the final hurdle, or require more and more effort to improve, so we wonder whether it's time to start again.

Therefore a new innovation often promises a lot and we’re keen to restart. A fresh start (or a second or third) can deliver rapid progress; not surprisingly, as we know many of the basics, and the main problems we want to address.

However, we have seen that progress will frequently trail off. This begs the question as to whether we are coming up against the same issues as originally encountered, or the new approaches are throwing up their own unique problems?

Returning to the case of mobile phone data, I would argue that both are true. There were specific problems in understanding the nature of MPD, its strengths and weaknesses. But also there are fundamentally hard problems with building reliable transport matrices which no magic wand was going to address. That top part of the curve represents us hitting the same problems, and that's not unique to matrix-building.

Sometimes it can be helpful to have many new entrants working in parallel: perhaps one of them will crack the problem and find the right approach to make a new technology really work. But equally, is there a risk different groups enthusiastically re-tread the same ground?

The tendency for new ideas and approaches to crash into familiar problems was pinpointed in one of my favourite xkcd cartoons. For ‘algorithms’ feel free to insert your own transport-related buzzword!

Source: https://xkcd.com/1831/ (if you’re not familiar with xkcd, happy reading!)

Are you ready to launch?

Another take on new technology I have discovered is the ‘Technology Readiness Level’ used by NASA. Clearly NASA is an organisation which needs to plan carefully over many years to make use of cutting edge technology, without putting critical missions at unnecessary risk! Often they will plan in use of technology when it is not yet fully proven.

The concept of ‘readiness’ seems pretty critical to anyone who has an interest in delivery. Have we understood how much work is needed to put a new technique into production? Do we know how much risk remains?

Putting it together: Are we balancing risk and reward?

Putting this all together, I would suggest thinking about all these issues along the lines of the diagram below. When we work on something new, we should have an idea both of the Impact or Benefit we expect, and of the Effort or Risk which is involved.

Measures where are low effort but high impact can obviously be addressed, whereas anything low benefit and high risk is wasted effort.

Following that, we should consider whether we can best produce benefits by continuous improvement, innovation or research & development.

If R&D is needed, then it is necessary to accept that we cannot have fixed ideas about timescales, effort or indeed the outcomes. As a modeller, I would have to acknowledge that most transport models by their very nature contain an element of uncertainty akin to research!

Key Questions for Innovation

Having thought through this, I propose there are some key questions that should be asked whenever we consider ‘innovating’:

• Need: What is the actual need you are addressing? Are there shortfalls in current approaches, or emerging new requirements?
• Approach: What is the new approach, why is it different? Why do we believe it will help?
• Historic issues / New issues: What has ‘gone wrong’ in the past that prevented a complete solution? What might go wrong with this method, the same things or different things?
• Readiness: Can the method be used now? Does it need improvements, trials or pure research?

If we approach innovation without really understanding these issues, then we risk spending precious time and energy on approaches which are unlikely to deliver what we really need.

However, if we seriously consider the benefits which each innovation can bring, then we stand an excellent chance of addressing real and urgent needs in transport planning.

Acknowledgements

I have used material freely available on the web for the Gartner Hypecycle, NASA TRL, xkcd and definitions of innovation. All other diagrams, definitions and ideas are my own, so I take responsibility if they are unclear or misguided!

Thanks go to Tom Van Vuren and LANDOR for giving me the opportunity to chair at Modelling World, and to the following for humouring me in the development of their presentations on the day: Tim Veitch (Veitch Lister Consulting), Adil Mohammad, Claire Cheriyan (TfL), Elena Golovenko (Jacobs), Siamak Khorgami (AECOM). Also thanks to Stephen Cragg of Systra and many of my Atkins colleagues for many stimulating conversations on these topics!