Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
This work addresses the expansion of bike share services in urban areas, though it appears incremental as it builds on existing location embedding techniques with a focus on efficiency and interpretability.
The paper tackles the problem of identifying new bike share service ports by proposing an Efficient and Semantic Location Embedding (ESLE) model that incorporates geospatial and semantic information from map-tile images using a CNN. The results demonstrate its effectiveness in seeking new ports for NTT DOCOMO's services in Japan, offering insights not easily found with conventional methods.
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.