Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services

TL;DR

This paper introduces ESLE, a new efficient and interpretable location embedding model that combines geospatial and semantic features to identify potential new bike share service ports, demonstrated on Japan's NTT DOCOMO system.

Contribution

The paper presents a novel ESLE model that integrates geospatial and semantic information using CNN-based image analysis, offering a cheaper and more interpretable alternative to existing methods.

Findings

ESLE effectively identifies new service ports for bike sharing.

ESLE is computationally cheaper than existing approaches.

Semantic analysis provides new insights into location features.

Abstract

For short distance traveling in crowded urban areas, bike share services are becoming popular owing to the flexibility and convenience. To expand the service coverage, one of the key tasks is to seek new service ports, which requires to well understand the underlying features of the existing service ports. In this paper, we propose a new model, named for Efficient and Semantic Location Embedding (ESLE), which carries both geospatial and semantic information of the geo-locations. To generate ESLE, we first train a multi-label model with a deep Convolutional Neural Network (CNN) by feeding the static map-tile images and then extract location embedding vectors from the model. Compared to most recent relevant literature, ESLE is not only much cheaper in computation, but also easier to interpret via a systematic semantic analysis. Finally, we apply ESLE to seek new service ports for NTT DOCOMO's bike share services operated in Japan. The initial results demonstrate the effectiveness of ESLE, and provide a few insights that might be difficult to discover by using the conventional approaches.