Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories
This work addresses the need for personalized route planning for bicyclists, which is crucial for urban infrastructure development, though it is incremental as it combines existing algorithms in a new way.
The paper tackles the problem of inferring bicyclists' routing preferences from sparse trajectory data by classifying trajectories into groups and identifying favored road types for each, enabling the computation of adequate routes using weighted graphs and shortest-path algorithms.
Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimum-weight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.