People want to be mobile, people always have to be mobile. This simple requirement inevitably leads to the concepts of hypermobility, that is, trying to design IT-based solutions to allow people and items to be delivered quickly and securely to any destination. On top of this, all solutions have to be as cheap as possible. To do so, different mobility options not only have to work together across systems – so-called intermodality – but they must also compete with each other.
An easy daily commute – just wishful thinking?
One of the key challenges is that mobility providers' IT systems need to be highly interconnected, and that data such as prices, free capacities, routes, and travel times must be fully transparent in real time in order to match the mobility provider's most appropriate offers to the individual needs of the mobility seekers. So, for example, is the wishful-thinking that an employee arrives at work exactly at the scheduled start at 8:30 am on Monday? To do so, he ideally finds a rental bike in front of his house at 7:40, rides with this to the local train station, takes (without waiting) the train to Downtown where the city bus is ready for the inner-city traffic. Finally at the destination, another rental bicycle is found to cover the last 800m to the place of work.
Last but not least, the commuter pays close attention to the costs and in return weighs up whether it may actually be cheaper and more practical for him to travel individually with his private car.
Planning in a safety buffer for commutes costs mobility seekers valuable time, be it for work or leisure.
But what happens now, when traffic disruption sets in, unexpected weather conditions make cycling impossible, a mobility system in this chain fails? The most practical way out is to calculate a safety buffer so that at least a large portion of potential disruptions can be mitigated. As a result, the mobility seeker loses valuable time, be it for work or leisure. Conversely, one could demand from the IT systems that they calculate and offer an alternative in real time in the event of such disruptions. It is easy to imagine that this is not affordable given today’s limited traffic infrastructures and due to the fact that all systems, such as commuter trains, are economically optimized and in most cases capacity shifting is simply not possible.
How mobile do we really need to be?
The task of IT will be to adapt the mobility request in such a way that a real-time calculation possible for the first time.
To solve this problem, we have developed a completely new idea. This is based on the fact that we detach ourselves from the fantasy of a hypermobile world while asking the following key questions: Which journeys are important or necessary, which ones less so? Which journeys necessarily require the arrival at a precisely predetermined time, which ones can be rescheduled? How can travel be avoided completely by using alternative means of communication or by bundling activities that require on-site presence? The list of these questions could be extended as desired.
However, there is a basic idea underlying all points: The task of IT is no longer to solve a very complex equation that perfectly serves a rigid mobility request. From now on, it will be to adapt the mobility request in such a way that situational parameters are included in the calculation, thus making a real-time calculation possible for the first time. This approach seems to be much more difficult than the one currently being pursued, but if you look closely and consider the latest technologies such as artificial intelligence, the feasibility becomes apparent.
A data-driven paradigm shift in mobility
An algorithm based on AI automatically identifies the user's control movements, including the commuting journeys required by profession, and also recognizes the potential flexibility of the user.
The previously described scenario of the commuter should be used as an example in order to better clarify the idea of the solution. The solution is based on individual movement data, which are collected continuously. This can be done, for example, via a smartphone, where, for privacy reasons, all data remain in the possession of the data owner. An algorithm based on artificial intelligence fully automatically identifies the user's control movements, including the commuting journeys required by profession. However, the algorithm also recognizes the potential flexibility of the user, i.e. the possibilities to postpone the target arrival time. Additional data, such as fixed dates, the type of appointments, the current weather conditions, actual and predicted traffic delays on the route, are included in the algorithm, as well as the current availability of rental bicycles at the transfer points. As a result, with the appropriate flexibility of the traveler, the start times of professional journeys of two successive days might differ from one another. This may seem strange from today's perspective, but it can also lead to a completely new quality of life and a deceleration of everyday life.
The respective data owner has the full sovereignty and control over his or her data at all times.
Essentially, there are two technologies that play a central role in representing this solution: Artificial Intelligence and fully cross-system, legitimate, and secure data exchange. To ensure the latter, we had to develop a completely new technology independently. The resulting concept, which we call “myData”, allows the data exchange of arbitrary formats and granularity under strict adherence to a paradigm: The respective data owner has the full sovereignty and control over his or her data at all times. Translated to our scenario, this means that the personal calendar entries are not made available to any of the mobility providers. They only feed the algorithm to calculate the respective mobility offer and propose it to the end user.
The approach described here is not fiction! We have already created a corresponding prototype with the project name “eCommuter”. It continuously collects personalized user movement data, and the first algorithms for determining rules, routes, and forecasting have also been implemented. The solution is based on our platform myData (developed in parallel) for secure data exchange, which has already been successfully tested in other applications. Intermodal mobility is completely redefined with this solution!
As an industrial manager and university professor, Martin Przewloka has more than 20 years of experience in the successful development, launch and scale-up of technologies. His special technical expertise and scientific interests lie in the fields of digital assistance systems, sensor technologies, smart data and artificial intelligence.
Please note: The opinions expressed in Industry Insights published by dotmagazine are the author’s own and do not reflect the view of the publisher, eco – Association of the Internet Industry.