Mobility as a Service (MaaS) — what is it and how do we make it a reality?

Peter Stephan
Sep 29, 2018 · 8 min read

What is MaaS?

MaaS is Mobility as a Service. So what would mobility be if it wasn’t a service? Well today, mobility is mainly based on discrete products e.g. owning a car. A key premise of MaaS is that it will allow a shift away from privately owned forms of transport and products; as other options such car-sharing, ride-sharing, bike-sharing and on-demand public transport (PT) could replace private vehicle travel (to some degree) and complement the PT network. This could significantly reduce the need to even own a car! (Hence mobility would transition from being about products, to being provided “as a Service”). There are potentially great benefits to this for cities such as increasing the usage of non-car modes and reduced road network congestion.

MaaS puts the customer as its core focus

Looking at MaaS from the customer’s perspective; at the centre of MaaS is an all-encompassing smartphone app.

Whim, an early MaaS example in operation in Finland

The app may allow travellers to:

  • Enter their travel need from A to B at a desired time (now or in the future).
  • Search for available travel options, across a wide range of existing, emerging and future modes including: public transport, with the addition of new “on-demand” or “demand responsive” transport services; taxi’s*; ride sharing* (e.g. Uber); car sharing* (where a car can be hired for a period such as GoGet); bike / e-bike sharing (e.g. Brisbane CityCycles or Gold Coast Mobike scheme); and even scooter sharing (yes, this becoming a thing, see here).
  • They could be able to seamlessly select and book their desired transport option, potentially involving a multi-modal journey.
  • Then pay for that transport option in the app, regardless of how many transport modes and operators are involved in the background.
  • The app could also integrate real-time data feeds from many sources, and may use predictive analytics, to inform the traveller of information such as: when and where their service is arriving; if it’s on time; where they are in their journey; when and where they need to alight their service, park or be dropped off; how to transfer to the next leg; and network conditions such as congestion and incidents on the road network, where there are available car parks, or crowding and seating availability on public transport etc.
  • MaaS could also enable more customer feedback, like Uber have with their rating systems at the end of the trip.

*Note that these modes could involve autonomous and/or electric vehicles in the future as these vehicle technologies are adopted and I’ve wrote about some of my thoughts on the impact of these technologies here.

In terms of the customer cost, with MaaS we are possibly going to see the introduction of new business models such as monthly subscriptions to transport like Whim in Finland offer (see below), and like we already have today for our mobile phones, internet, entertainment such as Netflix and Spotify, and other “things” as a Service like Software as a Service. Whilst this is often touted, we cannot know for sure if it will be accepted and preferred over a pay-as-you-go system (like we currently have). Some user surveys however suggest that the monthly subscription models could be more accepted if it includes a policy for ‘rolling over’ unused credits into the next month.

Whim payment plan options and inclusions, which includes a pay-as-you-go option (Whim To Go) and monthly subscriptions (Whim Urban and Whim Unlimited)

MaaS could make mobility highly personalised

In a MaaS world, travel is expected to become highly personalised through the app, and it may also be able to use techniques such as machine learning to learn and tailor the travel options to the traveller — for example which modes, routes and operators the user prefers; when and where they typically commute / travel; and with the knowledge of the users travel patterns it may able to predictively inform users of relevant information such as incidents affecting their regular pattern of travel.

Predictive and informational messages could be pushed to users through the app, or in ‘more personalised’ forms that they already use such as via text message, email, Facebook Messenger or even Snapchat!

The pricing packages offered by MaaS operators could also be highly personalised for the user, informed by the depth of data that will be accumulated about their travel patterns — this could inform the inclusions and exclusions in a monthly subscription package, for example how many taxi rides are included, how many days of car-sharing are included, whether bike-sharing is desired.

Is MaaS just about smartphone apps?

In short, I believe it is not — there are quite a lot more parts involved in the MaaS ecosystem! This diagram below provides a good overview of the MaaS ecosystem.

Source: Finland LVM Ministry of Transport and Communications / QIC MaaS Red Paper

My summary of it is:

  • At the customer level, it involves the smartphone app, as well as potentially wearable devices (e.g. smart watches) which are increasingly becoming popular and have app capabilities as well.
  • The payment system is also key, and may enable pay-as-you-go payments, (recurring) monthly subscription plans, as well as a range of payment mechanisms such as debit and credit cards through the app, and broader use of smart travel cards across the transport network.
  • There’s also a wide range of transport operators and modes involved, as mentioned above, that need to be integrated into any MaaS platform from a systems, data, operational and contractual perspective.
  • Data will be integral to this ecosystem, from user accounts, to capturing real-time network conditions and vehicle positions, through to payment processing and customer feedback. Application Programming Interfaces (APIs) will be the software technique allowing data to be exchanged between systems in this ecosystem.
  • Infrastructure such as our roads, railways, stops and stations will still also be required and will likely evolve to become more digitally connected and enable real-time data gathering, as well as increasingly providing up-to-date travel information. As an example, video footage from existing CCTV cameras could be streamed into an artificial intelligence (AI) engine to count traffic or pedestrian volumes and speeds, and identify incidents, as NZ Transport Agency is already developing.

More broadly, there will also be a lot of changes in the transport industry related to planning, policies and the workforce skillsets required to deliver and operate MaaS.

How do we make MaaS a reality?

Whilst MaaS promises a range of potentially great benefits, it will have its challenges. In my address, I posed a few of the big questions and industry challenges that I see, for example;

  • What should be the role of government vs the private sector in MaaS? For example, who will be the developer or developers of the MaaS apps? In a particular region, should there be multiple MaaS apps available or just the one?
  • What data will be required to be made available and shared through the ecosystem?
  • How do the economics of technology-based transport solutions like MaaS compare to investing in physical infrastructure? And to assess this, how will we need to change the way we model our transport networks to provide an evidence base?
  • With smarter payment methods and apps, will it be possible to introduce smarter incentives for people to use MaaS and PT, such as discounted fares if a service is late?
  • How soon are we likely to see MaaS solutions more widely on the market around the world?

Whilst there are probably few easy answers to these questions, some of my key takeaways from the event have been:

  • A wide range of open data source types will be required (e.g. real-time multi-modal network information, booking systems, customer feedback, etc.) to enable the development of MaaS applications and to help transit operators optimise their service delivery. This data will have a wide range of other uses as well such as for planning, modelling and analysis.
  • Collaboration between governments and the private sector (including transit operators and start-ups) is likely to be required, with government especially having a role of being an enabler by providing the open data required.
  • The industry should embrace a start-up mindset of experimentation and “fail fast and learn fast” i.e. a failure is not really a failure if it is learnt from (with a few caveats such as it not costing too much or sacrificing safety etc.). There are many opportunities to run real-world experiments within the MaaS ecosystem such as by making changes to the apps, trialling different stop locations and routes, and even trialling pop-up services in new areas to test the market for acceptance. This will likely involve a significant shift in culture and skills; something that partnering and working with start-ups could assist with.
  • Aside from real-world experimentation, a few ways that government and industry can embrace an experimental culture is through modelling and user surveys. Modelling and surveys can allow for experimentation and analysis of scenarios in a desktop environment without the cost and risk of trying many options in the real-world. However the large majority of transport models in the industry today do not consider MaaS and its associated mobility changes including autonomous vehicles, ridesharing and on-demand public transport. Further work could therefore be undertaken to improve our current models, or develop new ones, so that they can test and experiment with different MaaS scenarios before they are progressed to the next stage (i.e. real-world testing, which is likely to be much more expensive). In addition to modelling, asking real people (customers) about their views, preferences and choices towards MaaS solutions could also be highly valuable as there is limited real-world data about these new concepts. In fact undertaking surveys (when undertaken carefully to minimise bias) could be complementary to the modelling activities by providing input data to the models. Surveys could also be used to identify the specific suburbs/locations and demographics that are most willing to adopt MaaS, and therefore where real-world trials would be most warranted.

I look forward to seeing how this landscape evolves and hope to be a part of shaping it!

Peter Stephan

Written by

Transport Data Analyst & Urban Futurist. Keen on big data, GIS and the future of cities & transport. Web:

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