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.

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.

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.