Large-scale human mobility data (e.g. GPS data) encodes valuable information interested by various fields. Extracting stay and movement behaviors from noisy positioning record sequences is a critical preliminary step to utilize human mobility data. For the past two decades, this processing has been founded on a simple intuition proposed by Hariharan and Zheng et al.[18, 48], which uses manually selected parameters to make recursive, rule-based classification as to whether data points in a positioning record sequence constitute noise, move, or stay. This de facto processing approach, despite its simplicity, is inherently sensitive to parameter choice and suffers from the low efficiency of sequential processing. These inherent limitations make it practically infeasible, when confronted with the large-scale, fine-grained human mobility datasets in industry. To address this fundamental problem in human mobility data utilization, we innotatively rethink the distinction in representation patterns of noise/stay/move within the positioning record sequence from the lens of topological persistence, culminating in a novel pipeline for robust and efficient human mobility data processing. This is grounded in our empirical observation that topological persistence features of stay/move/noise exhibit robust and generalizable discriminability across variations in parameter choice, individuals, and geographical regions. By introducing the Laplacian to simplify the computation of topological persistence features, our processing pipeline is capable to exploit GPUs' parallel capacity for efficient processing. Experiments on real-world GPS datasets totaling up to thousand billion data points demonstrate that our method produces processing results comparable to those of human annotators, while requiring only 10% of the time consumed by previous approaches. We further show that our method is scalable for cumulative data volume and remains effective in identifying stay/move behaviors that traditional techniques consistently fail to handle, even under conditions of severe positioning errors and diverse behavioral patterns.

Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they are often affected by nonperiodic public events, such as disasters and occasional celebrations. Since regular human mobility patterns are affected by these events, estimating their causal effects is critical to accurate mobility predictions. News articles provide unique perspectives on these events, though processing them is a challenge. In this study, we propose a causality based prediction model, CausalMob, to analyze the causal effects of public events. We first utilize large language models (LLMs) to extract human intentions from news and transform them into features that act as causal treatments. Next, the model learns representations of spatio-temporal regional covariates from multiple data sources to serve as confounders for causal inference. Finally, we present a causal effect estimation framework to ensure that event features remain independent of confounders during prediction. Based on large-scale real-world data, the experimental results show that the proposed model excels in human mobility prediction, outperforming state-of-the-art models.

Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external features, alongside the complexities of spatial relationships and temporal uncertainties, significantly increases the complexity of forecasting models. Additionally, traditional approaches have handled data preprocessing separately from the learning model, leading to inefficiencies caused by repeated trials of preprocessing and training. In this study, we propose a federated architecture capable of learning directly from raw data with varying features and time granularities or lengths. The model adopts a unified design that accommodates different feature types, time scales, and temporal periods. Our experiments focus on federating route search records and begin by processing raw data within the model framework. Unlike traditional models, this approach integrates the data federation phase into the learning process, enabling compatibility with various time frequencies and input/output configurations. The accuracy of the proposed model is demonstrated through evaluations using diverse learning patterns and parameter settings. The results show that online search log data is useful for forecasting long-term traffic, highlighting the model’s adaptability and efficiency.

As governments increasingly rely on digital data to enhance public services, it is crucial to rethink how this data is utilized to better meet the needs of citizens. This paper emphasizes the transformative potential of structured data semantics in improving service delivery at both local and national levels. Focusing on the case of Noto Peninsula, Japan, which was affected by a recent earthquake, we explore how the implementation of the Universal Menu facilitated efficient access to vital services for affected residents. By examining this case, the paper underscores the need for a standardized approach to data organization that allows municipalities to tailor services effectively to the unique needs of their communities. Drawing on examples from other countries, we argue that embracing structured data semantics not only enhances the responsiveness of government services but also empowers citizens to easily identify and access the support they require. The conclusion reinforces the necessity of this paradigm shift for fostering resilient and citizen-centric governance in an increasingly digital world.

In recent years, data spaces have emerged as a framework for secure data collaboration. Data spaces are distributed data collaboration infrastructure systems that enable data collaboration among stakeholders. One of the requirements for data space systems is the technical enforcement of governance policies for the shared data. The objective of this study is to propose a new policy definition language, DSPOL, as a stepping stone toward realizing the enforcement of data governance for the practical use of data spaces. DSPOL can describe the constraints that must be followed in data space systems when accessing and using data, and when distributing deliverables. It has verification and validation functions to ensure that the policy description is as the writer intended and that there are no inconsistencies in the policy. We defined the state transitions of data usage in data user environments, developed a model of the execution infrastructure in data space systems, and formulated the contents of the describable policies. We implemented DSPOL and its verification and validation functions. Examples of policy descriptions based on supposed scenarios were shown, and examples of verification and validation were presented.

Recently, smart meters have been widely used as social infrastructure, making it easy to obtain electricity power consumption data. Electricity power consumption data has high potential in healthcare since it reflects various activities in daily life. We have been focusing on the potential of smart meters and studying frailty detection model using electricity power consumption data; frailty is a condition of reduced function and health in the elderly, also said to be reversible, which means the elderly can return to robust conditions by implementing appropriate interventions. Therefore, detecting frailty at an early stage and implementing intervention makes it possible to extend healthy life expectancy. Our previous research showed that frailty detection is possible with acceptable accuracy only using electricity power consumption data from smart meters. However, for real-world implementation, it is crucial to guarantee the robustness of the model performance. Moreover, implementing interventions after detecting frailty and ongoing monitoring is essential to extending healthy life expectancy. Given these points, we aim to guarantee the robustness of frailty detection model and verify the effectiveness of frailty detection model as a post-intervention monitoring tool. As a result of case study conducted in three regions, we demonstrated that the proposed frailty detection model has seasonal and regional robustness, showing acceptable performance in various seasons and regions. We also confirmed that it is possible to observe the elderly’s condition by tracking the outputs of the proposed model. Through our research, we showed the feasibility of frailty detection using smart meters and the possibility of a healthcare platform using smart meters.

Data utilization for efficiency and optimization is increasing in many fields, and it is expected to create new value by sharing data among multiple stakeholders. In addition to domain-specific data platforms, which are already prevalent in Europe and Japan, cross-domain data platforms are being developed to enable interoperable and sovereign data exchange across different domains. However, through an extensive survey, we found that as the requirements for data exchange vary with regulations and/or applications, data platform initiatives and the software tools they offer are scattered. Thus, we need an experimental environment where various data exchange platform technologies can be learned, developed, and tested in one place. The objective of this paper is to propose the establishment of the International Testbed for Dataspace Technology (ITDT), an academic and industrial testbed for data platform technology. The ITDT intends to provide an environment that facilitates the development and testing of interoperability, portability, and customizability for data platform technologies. We expect the ITDT to promote the advancement of data platforms, international collaboration, and the formation of a neutral technical community. This paper presents the requirements, architectural design, and future plans for the ITDT.

Many applications utilizing big data are being used every day, and the importance of data utilization is increasing day by day. Data spaces, a federated data management system, is gaining increasing attention in Europe and Japan. To promote data spaces into real applications and increase data exchange between companies and individuals, the data exchanged over data spaces must be well protected. To achieve that goal, we propose to apply homomorphic encryption scheme to data spaces, propose its architecture, and analyze its advantages. Furthermore, we also investigate the feasibility of the proposed architecture based on results of calculation of GHG emission of a self-driving electric vehicle in the real-world experiment.

Data is the most important property for bringing innovation and digital transformation. In Japan, data-driven society is one of the most important concept for the future society referred in Society 5.0, DFFT (Data Free Flow with Trust), and National Data Strategy of Japan. Several public and/or industrial domains have already established data ecosystem including data sharing platforms. However, there are many scenarios and applications which cannot be supported by the domain-specific data platforms. For example, disaster response, tourism, GHG (Greenhouse Gas) management such as the Scope 3 protocol, supply chain management support, and smart city are among them. So, we need a cross-domain data-exchange platform to support these applications. The objective of this paper is to propose "DATA-EX (data exchange)", which is a platform which is under development and will be operated by Data Society Alliance (DSA), and works as an infrastructure for cross-domain data exchange consisting of open standard specifications and guidelines for data exchange and platform operation, software systems based on the standards, and governance rules for the operation of DATA-EX. This paper presents definition, objectives, basic strategies, and architecture of DATA-EX, and its current status and future plan.

Predicting future people’s behavior can significantly impact various industries. Intelligent transportation system (ITS) advancement, in particular, depends on the ability to predict traffic congestion. If we can do so, we can encourage people to alter their behavior, which reduces traffic congestion, traffic accidents, travel times, and CO2emissions while also promoting the development of applications like dynamic pricing. However, predicting traffic congestion a few days ahead is challenging owing to its spatial and temporal dependence and its nature of being susceptible to external factors, such as weather, local events, and the pandemic of infectious diseases. For these reasons, previous studies have been limited to predicting the next few minutes to a few hours. To address this limitation, we propose using search log data of the toll route search service owned by East Nippon Expressway Co., Ltd. (NEXCO East), which operates expressway services in Japan, as these data are available several days before the prediction and comprehensively explain multiple external factors. We show that search log data can contribute to predicting people’s behavior by verifying the improvement in the accuracy of traffic congestion prediction.

With the increase of the number of the elderly, healthcare systems to support the daily life and wellbeing of the elderly attracted attention. Especially, frailty syndrome is one of the most significant challenges faced by many countries because of its high association with mortality and hospitalization. Recently, with the progress of ICT (Information and Communication Technology), many frailty detection models which use sensors were proposed. However, many of them require very high costs caused by the installation and management of sensors. Therefore, the objective of this study is to propose a machine learning-based frailty detection model using only electric power consumption data from smart meter, which uses no other devices such as sensors. Also, we examined the feasibility of our model through a case study, in which we have conducted on 24 elderly people. As a result of a cast study, we could detect frailty with 82% accuracy, 77% precision, 84% recall, and 80% f-score for a 2-class classification problem (frailty or non-frailty). The results of our study show that more elderly people can receive frailty diagnoses through smart meters. Moreover, since frailty is a reversible condition that could be restored to a healthy status with early and appropriate intervention, our model has potential to extend the healthy expectancy of the elderly.