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Automotive IUI


Multimodal Transportation Systems

Multimodality and sharing resources is seen to be the future of transportation in condensed, urban areas. Multimodal transportation systems help people to find their optimal way of traveling by offering them shared transportation resources (vehicles, bikes, public transport etc.) as a complement to their own vehicles. In this way, not only do they assist individuals in optimizing their trips (time, money and effort wise), but also help solving some of the biggest problems of urban living: traffic congestion, noise and pollution.

To be able to leave cars at home or at parking places outside a city center and change to public transportation systems, people have to be aware of

  • Alternative connections and mobility services (e.g., (e)-car, (e)-bike sharing)
  • Parking places where they can leave their cars
  • Ticketing information
  • Real value, in terms of time, cost, carbon footprint, of the alternative choices computed on the basis of real time data (e.g., traffic jams, parking unavailability)
  • Reasons or stimuli to change the common mobility behavior
 

Integrating different data sources, considering a variety of user requirements, preferences and expectations and providing personal support makes creating a multimodal transportation system a challenging problem.

Data correlation is in the focus of our research. We develop techniques for gathering and pre-processing the real-time data that can affect the personalized intermodal route recommendations. The captured traffic data is analyzed regarding which route is affected, giving the input to the intermodal mobility data representation.

 

Vulnerable Road Users

In-vehicle systems constantly support drivers with traffic information, maps, navigation systems, feedback and other similar services. Public transport users are supported with accurate plans, time table and connection opportunities.

Bicycles require different engagements than vehicles and public transport. Only very few cities have a well-planned and realized bike-lane network. In the rest of the cases, the cyclists need to find their optimal combination of bike lanes, sidewalks, motorways and pedestrian zones to get from one place to another. As a consequence, cycling in urban areas can be stressful and discouraging experience both for people that know the city very well, but rarely use bicycles as well as for people that regularly use their bicycles for recreation or sport.

Cyclists as a vulnerable group of people are influenced by many external factors like weather, topography, personal shape etc. The problem of route definition for cyclists very often extends to the problem of: Which routes are suitable for specific type of users and current set of conditions? Considering external data sources relevant to cyclists, our goal is to define a cycling route network beyond what existing maps and routing services can offer.

In our group we focus on urban cycling support for fleet users and individuals and we aim to develop system for automatic community based cycling route generation with real time personalized navigation support for cyclists.

In addition to the services we also research the presentation space and devices as additional factors that influences the cycling experience. Different types of sensors and wearables like glasses, watches, integrated touch sensors, leap devices etc. are being evaluated and used in our projects.