In the past few days we came to the following insight:
„How might we use live data from the destination to select the best route?“
The service is based on an app that integrates data given by users with live-data on route and destination (like scanning free parking spots) to determine the most efficient means of traveling based on the customer‘s preferences and the enviromental conditions.
For this to work, the customer needs to provide information on which services he is already registered with. Based on the users needs like cost and time efficiency, comfort etc. the system suggests a route and possibly a combination of different services. It will guide the user to a parking spot and inform possible connecting services. For instance the system might suggest the use of a taxi, if no parking spots are available at the destination, combined with car sharing to reduce the overall cost of the trip. This use case is also illustrated in a video:
This service allows the user to save time and money. He can scale his preferences and the app might even suggest services the user was not previously aware of. Most importantly, it will take the hassle out of inner city travel. Especially the search for parking spots can be very stressful.
As mentioned, our system requires live data on traffic and available parking spaces. Means to gather such data are currently being researched by various institutions. If our servcie where to become real we have to collaborate with these entities as well as consolidate input of various data sources and of develop the actual algorithms. Further improvements to the service could also involve parking spot reservation.
The user research lead us to the final question of how we might use information on a destination to recommend the best transportation service. Our service utilizes real time detection of traffic flow and free parking spaces provided by shared data of moving vehicles scanning the urban area. This information is transferred to our database and combined with the information given by the user. Preferences on costs and time limits, as well as specific needs relating to carried luggage or personal constraints let us compute an optimal route and possibly a combination of car sharing and other individual transportation services (taxis etc.). The required infrastructure (e.g. parking spaces) can be reserved and the sequential deployment of different services is organized through the app. An exemplary user story would be a user with luggage traveling to a city center by using a car sharing service to an area with available parking space. The user then switches to a taxi service to accomplish the last part of the trip where parking would be impossible. The journey will be cost efficient, no time will be wasted by a lack of parking possibilities and the user’s specific needs will be addressed.
Our target group for this project includes service providers and users in the field of individual transportation in inner cities. We decided to focus primarily on cars, as the current trend is that fewer city residents want to invest in private cars. In combination with new developments regarding self driving vehicles we expect interesting developments in this field over the next years.
For our research we interviewed a local taxi company “Taxi München” and contrasting that a transportation service provider who uses the “Uber” App to acquire customers.
We also analyzed car sharing companies situated in Munich. For this we talked to “Stattauto” and “DriveNow”.
Furthermore, we had conversations with representatives from “Tesla”, a manufacturer of electric vehicles and the Munich “Diakonie”, a social organisation that relies heavily on car sharing.
In addition, we tested and observed several services ourselves and were able to briefly talk to users of such services on location.