The MaaS Policy Feedback Loop
Mobility as a Service is as much of a buzz-phrase in the transport world as driverless cars. My colleague Martin Higgitt rightly notes that with the great opportunities of MaaS comes risk. It is not just transport authorities that need to understand how MaaS fits into their vision of the transport future; companies across all transport sectors risk their competitive advantage and customer relationships being disrupted by the power of technology offering customers whole mobility solutions.
In times when budgets are tight, particularly on the revenue side, policy makers may question why valuable resource should be spent enabling new mobility services, particularly for a concept that has only recently come to market. It is easier to build a business case for something with ample evidence, compared to something with little evidence.
A carefully crafted challenge to communities of developers, coders, technologists, and increasingly transport professionals to develop and deliver a MaaS solution can help shape their transport policy making as much as it delivers it. This sounds complicated, but understanding system level interactions and feedback mechanisms, and how these interactions position themselves in the policy lifecycle (Figure 1) helps to frame these challenges.
Figure 1 - Policy Cycle (Source: UNDP, 2016)
Start with your own vision of MaaS. What does this look like to the customer? What are the potential impacts on the transport network, and other policy areas? What is the scale of this impact? Link these policy interactions, and assign probabilities of what will happen based upon existing research such as in ticketing and journey planning. Once you have mapped these, you can then begin to understand how your MaaS intervention can influence your policy making by just doing it.
Take an example of the fuel that drives MaaS: data. A policy decision is made to release data as open data, with the challenge that by doing so techniques can be developed to improve traveller experience. This operational data is then combined with user data, enabling new platforms to map and feedback subsequent behaviour changes, and feedback learnings to help understand this policy’s transport impacts and suggest enhancing data quality to improve insight further. Transport for London have developed this approach iteratively with developers in London, and the net benefit is staggering - 500 apps powered by TfL’s unified API (Application Programming Interface), with 42% of Londoners using these apps.
This mapping of policy system feedback also has another benefit. It allows you to identify where your understanding is currently lacking, and thus craft low cost experiments and challenges to further your understanding, and test new solutions. This can include utilising MaaS platforms to deliver community transport services and new on-demand services in low public transport usage areas, such as the approach used to enhance existing public transport in Atlanta with technology from Uber.
MaaS is not just the latest thing that transport companies and authorities should jump upon. It offers a big opportunity to both deliver against transport policy objectives, and to improve transport policy making using new tools and new ways of thinking. That, surely, is too good an opportunity to miss.