Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

Abstract

With the deployment of modern infrastructure for public transportation, several studies have analyzed movement patterns of people using smart card data and have characterized different areas. In this paper, we propose the "movement purpose hypothesis" that each movement occurs from two causes: where the person is and what the person wants to do at a given moment. We formulate this hypothesis to a synthesis model in which two network graphs generate a movement network graph. Then we develop two novel-embedding models to assess the hypothesis, and demonstrate that the models obtain a vector representation of a geospatial area using movement patterns of people from large-scale smart card data. We conducted an experiment using smart card data for a large network of railroads in the Kansai region of Japan. We obtained a vector representation of each railroad station and each purpose using the developed embedding models. Results show that network embedding methods are suitable for a large-scale movement of data, and the developed models perform better than existing embedding methods in the task of multi-label classification for train stations on the purpose of use data set. Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data.

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

“…A ik is the outdegree (or degree in the case of undirected graph) of node min (word, label) APP [3] O (2) max (node, node)) GraphEmbed [83] O (2) min (word, word) + O [41], [42] O (2) min (station, company), O…”

“…Node classification is conducted by applying a classifier on the set of labelled node embedding for training. The example classifiers include SVM ( [1], [20], [33], [34], [41], [42], [45], [56], [57], [60], [73], [75], [81], [87]), logistic regression ( [1], [17], [19], [20], [21], [25], [27], [28], [45], [59], [124]) and k-nearest neighbour classification ( [58], [151]). Then given the embedding of an unlabelled node, the trained classifier can predict its class label.…”

“…4.3.3. Other heterogeneous graphs also exist. For instance, [41] and [42] work on the mobility data graph, in which the station (s), role (r) and company (c) nodes are connected by three types of links (s-s, s-r, s-c). [43] embeds a Wikipedia graph with three types of nodes (entity (e), category (c) and word (w)) and three types of edges (e-e, e-c, w-w).…”