Ride Vibrations: Towards Comfort-Based Bicycle Navigation

Ride Vibrations: Towards Comfort-Based Bicycle Navigation

Leitung:  Wage, Feuerhake, Golze
Jahr:  2019

Due to the wide availability on smartphones, navigation applications are no longer used only for driving in unknown surroundings, but also for everyday travel by bicycle and other modes. In contrast to the car, travel times by bicycle are barely influenced by the respective traffic situation but considerably by the preferred speed (or physical condition) and also to a large extent by the individual route choice made. Especially to avoid situations with low comfort like gradients, many or long stops at traffic lights and badly maintained roads, cyclists vary from the shortest or fastest route. This fact is only indirectly considered in common navigation applications.

With the aim to explicitly integrate the underground surface quality into the route calculation for cyclists, methods for automated logging and analysis are developed at ikg under the slogan "Ride Vibrations". The basis of the data collection is the prototype smartphone app "RideVibes" to record positions and accelerations. By this it is possible to log the accelerations on everyday routes by means of a smartphone mounted on the handlebars. Initial works shows sufficient sensitivity with regard to surface roughness and suitability for weighting future route calculations.

However, so far collected data also includes illuminance measurements where the night-time ones could be used to optimize routes in the dark with the best possible illumination. Another task in the domain of routing is the generation and improvement of sufficiently accurate path and road graphs from users’ trajectories. Furthermore, map matching methods can be enriched by further aspects (e.g. surface roughness) to improve their results in complex situations. Data-driven experiments on route choice behavior, e.g. how much detour is fine to prevent a bad quality path, are another option.


Possible Topics

► “Ride Into the Light”: Use illuminance data for route optimization at night

► Routing graph update: Trajectory based change, anomaly and missing segment detection

► Trajectory map matching: Introduce additional features like roughness or orientation

► Route choice preferences: Data driven inference of route choice factor weights



► General interest and knowledge in spatial data analysis and GIS-lectures

► Skills in Python, Java or comparative

► Thesis can be done in German or English