Visual Exploration of Cycling Semantics with GPS Trajectory Data

Abstract

Cycling—as a sustainable and convenient exercise and travel mode—has become increasingly popular in modern cities. In recent years, with the proliferation of sport apps and GPS mobile devices in daily life, the accumulated cycling trajectories have opened up valuable opportunities to explore the underlying cycling semantics to enable a better cycling experience. In this paper, based on large-scale GPS trajectories and road network data, we mainly explore cycling semantics from two perspectives. On one hand, from the perspective of the cyclists, trajectories could tell their frequently visited sequences of streets, thus potentially revealing their hidden cycling themes, i.e., cyclist behavior semantics. On the other hand, from the perspective of the road segments, trajectories could show the cyclists’ fine-grained moving features along roads, thus probably uncovering the moving semantics on roads. However, the extraction and understanding of such cycling semantics are nontrivial, since most of the trajectories are raw data and it is also difficult to aggregate the dynamic moving features from trajectories into static road segments. To this end, we establish a new visual analytic system called VizCycSemantics for pervasive computing, in which a topic model (i.e., LDA) is used to extract the topics of cyclist behavior semantics and moving semantics on roads, and a clustering method (i.e., k-means ++) is used to further capture the groups of similar cyclists and road segments within the city; finally, multiple interactive visual interfaces are implemented to facilitate the interpretation for analysts. We conduct extensive case studies in the city of Beijing to demonstrate the effectiveness and practicability of our system and also obtain various insightful findings and pieces of advice.