Get me there – now!
Qixxit, GoEuro, Moovel, Ally, Touch&Travel, Flinc, … – the list could probably go on for at least another couple of hundred start-ups and apps in the mobility sphere. The market, venture capitalists would argue, is huge and the potential thus immense. However, with so many different avenues of innovation currently being explored, it is hard to get an overview of what is happening. When thinking about innovation in the mobility sector from a user-centric perspective, clustering approaches into the different (and still fragmented) steps in the mobility value chain makes a lot of sense. We have found that mobility innovation broadly falls into three distinct categories: journey planning, real-time multi-modality and integration of services.
Unfortunately, getting from A to B is rarely as easy as it sounds. Especially if it involves the different modes of transport or cross-border travel. No matter how sophisticated individual travel preferences, each journey always consists of three very basic steps: (1) Planning, (2) Execution & (3) Payment. These steps are not always to be aligned in a sequential manner. One certainly buys train tickets before boarding the train. However, if choosing a car sharing travel option, the bill might be issued on a monthly basis and thus only be settled long after the mobility event itself. Despite this, the broad framework helps to pinpoint the hotspots of innovation in the mobility sector, as ‘one holistic’ solution has not yet emerged.
Planning: New interfaces and tailored travel plans
Getting to work is easy, but planning a business trip from Berlin to Munich might not be as straightforward. You might be very well aware of your travel preferences, but still need to struggle through uncustomized suggestions of the travel search engine of your choice. A currently accelerating trend is the use of intelligent assistants, which provide a personalized interface towards clients. Chatbots are already a powerful tool and can handle a vast share of the direct communication with the customer through channels like Facebook Messenger, WhatsApp, E-Mail. Let’s assume you voice the following preferences: “I want to travel to Munich next Tuesday, my meeting is at 11am and I would like to be back in Berlin by 8pm.” Natural Language Processing is already powerful enough to extract the relevant pieces of information, like date, direction of travel, city of departure, time constraints and can adjust suggestions accordingly. In areas like business travel, those innovations will continue to provide more cost-effective, but still personalized planning and booking experience. Human supervision might still be necessary, but the technology will gradually take over.
Execution: True Multi-Modality as a vision
Especially in urban and short-distance travel, multi-modality is a crucial aspect of mobility. Within mobility apps like Qixxit, Moovel and Ally, customers can compare different travel options quite handily. Nevertheless, planning in a multi-modal world is only one challenge. During the journey, proactive guidance needs to be provided to the traveller in order to be of real value. What happens if the train is delayed by five minutes? Should an alternative route be chosen? Providing real-time assistance for multi-modal travel requires mobility applications to make use of all available data sources, while reliably mapping them regarding their effect on transport operators and routes.
While this is challenge enough, it requires sophisticated machine learning approaches to make robust predictions about delays within the respective transportation network. While various transport operators are already trying to query all data sources for the benefit of their operations, research projects like SD4M (“Smart Data for Mobility”) are moving into a similar direction, processing unstructured data from Twitter and Facebook in a real-time fashion. Not ambitious enough? Well, innovation on the data-side of multi-modality is flanked by even more ambitious approaches: Optimizing for traffic flows, while simultaneously optimizing routing for car-sharing services. This is real data science, with an exciting value proposition.
Payment: Seamless integration, enabling new business models
Most of the transportation services are not synchronized. One might use DriveNow in the initial phase of the journey, but switch to Deutsche Bahn for the rest of the journey. Payment for DriveNow is handled separately, while the ticket purchase for train travel needs to be settled in advance and could be handled in cash (at the ticket terminal) or by credit card for in-app purchases. Generally though, integration across services is very likely to increase over time. Apps like “Touch&Travel” already indicate how the future might look like. One app, which allows for the usage of various transportation services, providing for a ‘universal ticket’ and handling payment in the background. These new approaches are interestingly leading into a direction of “streamlined-pay-per-use-model”, which is operated from one standardized interface. To underline and proof the feasibility of such approaches, data science is essential as it empowers to predict mobility patterns and usage and allows operators to experiment with alternative business models.
The roadmap of innovation for a user-centric mobility experience is still stacked and a lot of potential still remains untapped. But ideas are still floating and current efforts and continuous advancements within data science allow for optimism regarding the future of mobility.