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.

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.

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.

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.