City-Scale Human Mobility Prediction Model by Integrating GNSS Trajectories and SNS Data Using Long Short-Term Memory

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.

Security that Matters: General Knowledge and Correlations in the Context of Applied Satellite Navigation, Specific Interference-Events and the Use of the GNSS-Technology

“…All studies which can be classified mainly in the field of safety research indicate that human navigation using different systems is a very complex interaction, requiring detailed yet comprehensive individual data to capture the basic psychological mechanisms (see Irmischer & Clarke, 2018;Maciuk & Rudyk, 2020;Miyazawa et al, 2020). The present study represents one of the few existing attempts to investigate the interrelationships and interactions related to satellite positioning systems for human navigation in a social science-psychological context.…”

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

“…This can mitigate the risks derived by significant changes in the environment (for example, constructing new buildings along a railway) or by the activation of new RF sources (such as new 5G RAN nodes). ML models have been applied to other positioning and navigation applications such as regional mapping of the geoid [73]; human mobility analysis on large-scale mobility data which has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health [74]. Other applications include location prediction using GPS trackers to, for example, locate missing people with dementia [75]; improving GNSS Positioning from smartphones [76][77][78][79]; improving GPS code phase positioning accuracy in urban environments [80]; improving accuracy of differential GPS (DGPS) correction prediction in position domain [81]; and improving kinematic GNSS positioning accuracy with lowcost GNSS receiver in urban environments [19].…”

An Analysis of Synthetic Timeseries as an Enabler to Improve Region-based Human Mobility Forecasting

“…Regarding non-parametric methods, Deep Learning (DL) algorithms have been widely used for such a forecasting task. In that sense, LSTM [24], Conv-LSTM [25] and GRU [26] networks have been some of the most relevant Recurrent Neural Networks used in this setting. More recently, some works have profited from the graphbased structure of most human-mobility settings (e.g.…”