MODE - the software Solution for capturing different modes of transport
MODE is based on many years of R&D work. Besides GPS and other localizing functions, smartphones provide sensor-based acceleration data for determining the means of transport - captured reliably via frequency analysis. Combined with GIS data, time schedule and real-time information of the public transport system, MODE thus enables an autonomous ticketing solution without additional infrastructure or user interaction.
MODE distinguishes between eight different means of transport: bicycle, motorcycle, car, bus, tram, subway, railway and walking. The smartphone-based data is transmitted to a server where the MODE algorithm creates a reliable and detailed trip information.
MODE can easily be integrated into various existing ticketing applications and platforms, considering specific customer requirements and interfaces.
MODE thus forms the ideal basis for autonomous ticketing solutions for public transport operators and mobility providers.
- No user-interaction necessary
- No additional infrastructure required (e.g. Bluetooth beacons)
- Automatic differentiation between eight means of transport
- Detailed, reliable trip information
- Simple integration into existing ticketing applications
- Battery-saving operation (no continuous GPS required)
Carinthia and Styria, Austria:
Project “Smart Journey”, carried out in collaboration with the Austrian Federal Railways (ÖBB), the Austrian Federal Ministry of Transport, Innovation and Technology (bmvit) and the transport associations Carinthia and Styria, with the aim of evaluating smartphone-based travel surveys for an autonomous ticketing and a respective revenue sharing system.
Capturing the mobility behaviour of users of the Wien-Mobil-Routing app in collaboration with the public transport operator Wiener Linien.
Survey of the multimodal mobility behaviour in the city of Tbilisi/Georgia, organized within the framework of the Future Cities Programme of the Asian Development Bank (ADB).
Great Success at SHL Challenge
At the Sussex-Huawei Locomotion Transportation Recognition Challenge 2018 in Singapore, AIT’s solution was second to none in detecting changes between means of transport and achieved the excellent fourth place overall. In addition, the AIT model requires the shortest training time and by far the least storage space. This allows mobile implementations and provides an optimal basis for autonomous ticketing solutions.