Abstract
Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.
Highlights
Today, more than 54 percent of the world’s population lives in urban areas (66 percent by 2050)1
Global Navigation Satellite System (GNSS) trajectories used in this study are from anonymized mobile phone users throughout the Greater Tokyo Area from July 1, 2012 to July 31, 2012, which are processed by NTT DOCOMO, INC
“Konzatsu-Tokei (R)” Data refers to data on the flow of people collected by individual location data sent from mobile phones with the users’ consent, through Applications 3 provided by NTT DOCOMO, INC
Summary
More than 54 percent of the world’s population lives in urban areas (66 percent by 2050). The high penetration rate of mobile phones, especially smart phones, enables systematic data collection for longer periods; users are more willing to connect to multiple services and allow service providers to collect user data (Birenboim and Shoval, 2016) These provide an inexpensive means of collecting data on city-scale mobility. Survey-based human mobility data collect certain background information on mobility. They often include individuals’ mobility purpose, home location, household size, and job classification, which complement mobility models and theories. Our main idea is to develop a city-scale human mobility prediction model by integrating GNSS trajectories and social network service (SNS) data, in order for the human mobility prediction model to achieve more accurate predictions and to have additional application capabilities.
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