Green Wave for Cyclists via Self-Learning Routing Algorithms and a Cycling Assistant for Smartphones
Advanced routing and navigation incorporating real time traffic information is state of the art - at least for car drivers. The number of cyclists using digital navigation even to reach familiar destinations is increasing with the availability of waterproof smartphones and smartphone mounts. However, for realistic routing and real-time cycling assistance, profound knowledge about expected waiting times at traffic signals and cycling behavior between the stops is essential. GPS tracks of cyclists can be used to obtain both even when (real time) information of the traffic control is not available.
In BikeWave methods for analyzing waiting times at intersections were developed. Cleaned and map matched trajectories are used to extract signal control cycle lengths and green-light durations for all possible relations at junctions. The trajectories are also used to derive cyclists’ riding profiles.
Both signal patterns and riding profiles were then used as input for realistic calculation of routes optimized for shortest travel time. Based on this travel time calculation bi-objective routing with road popularity by our CycleTripMap was implemented. Additionally the signal patterns were also used in the “GreenWaveBuddy” feature of our project partner’s app “BikeCityGuide”, which allowed for anticipatory driving by visualizing the green/red phase of the next traffic light on the route.