Container transport is widely used in logistics, and understanding the movement patterns of container trailers can improve logistics efficiency. Despite the availability of vehicle GPS data, studies focusing on container trailers in port areas are still limited. This study uses ETC2.0 data to extract trailer trajectories that visited Sendai Port in northern Japan. This port is of moderate size, allowing for the collection of a substantial amount of data, yet not so extensive that it precludes detailed analysis of individual cases. Analysis of the stop points of the trailers entering the container terminal shows similar characteristics to those observed in bigger Japanese ports. Since detecting stops alone may not capture visits to locations relevant to understanding trailer mobility, this study proposes a method to detect even short visits to such places. This method enabled the characterization between trailers transporting empty containers and those carrying loaded containers to the container terminal by the locations visited before and after accessing the container terminal.

In this paper, we propose a method for predicting the impression evaluation of buildings’ exterior appearance using street image big data, and we demonstrate its applicability to architectural design. First, we conduct a large-scale impression evaluation web questionnaire using building exterior images extracted mechanically from street image big data in Ota Ward, Tokyo. Next, by training a deep learning model using the results, we can quantify the impression evaluation of building exterior images taken from different building uses and angles. Furthermore, we analyze the impression evaluation scores predicted by the model and demonstrate this method in architectural design through several case studies.

Remote sensing image semantic segmentation of land use, benefitted from the development of deep learning and consequently made considerable progress in terms of inferencing accuracy and speed. However, the effective training of semantic segmentation models for remote sensing imagery necessitates extensively detailed pixel-level annotations, and gathering such data is both time-intensive and laborious. Thus, this study implemented low-rank adaptation on a stable diffusion algorithm to learn the distribution of the pixel-level annotations in case of the LoveDA dataset. Consequently, the annotation-image pairs were used to train the remote sensing image generator based on stable diffusion guided by ControlNet. We proposed a stable diffusion based approach, which can generate image-annotation pairs from scratch. The generated annotation and image pairs achieved a high accuracy of 0.520 mean intersection-over-union on LoveDA dataset, which is close to the original data training result of 0.539 mIoU. Furthermore, the mixed training using generated and original data achieved 0.542 mIoU, thereby demonstrating the data augmentation function of our approach. This study provided a solution for the high-cost pixel-level annotation issue, and thus, exhibited the potential of artificial intelligence generated content.

Western Japan experienced heavy, record-breaking rain from June 28 to July 8, 2018, causing approximately 600 road sections to be closed in Hiroshima and Okayama Prefectures. The government performed recovery activities pursuant to their own road-recovery plan to help damaged citizens' lives return to their original condition as soon as possible. However, it took a week after the disaster to begin to restoration of neighborhood roads. Therefore, optimal decision-making systems are necessary to improve resiliency related to real life. We adopt an off-policy algorithm of a single-agent deep Q-learning approach, because disaster situations differ based on the occurrence area, and a generalized solution for optimal decision making does not exist. To develop a decision-making system that accounts for human mobility, we utilized origin-destination pairs extracted from the location data of smartphones and, digital road maps reproduced from real topologies. Agent in our model is the entrance point of the O-D pairs passing through a damaged road section on normal days. Each agent is assigned tendency to decide actions according to the road type, and a weight factor depending on the extent of damage. In addition, a restoration rate, which refers to the score of the action, is calculated based on the delayed travel time according to the changed demand of the trip. Restoration rate is affected by the agent's action, and gives the environment information related to the answer signals. This helped us to ascertain that it takes a longer time for origin-destination pairs using a road section with heavy traffic to recover to their original state. We estimated the number of required steps of each O-D's mobility until they get their own restoration rate over than 0.85. We ensure that government administrations could utilize our results as a reference data to determine the priority of restoration work and estimate the amount of input per route.

When conducting agent economic simulation for supply chains, inter-company transaction data are essential. However, the current inter-firm transaction data are network data in which branch office information is aggregated into headquarters transaction data. This study proposes a method to estimate branch office transactions from inter-company transaction data aggregated among headquarters by using a gravity model. We also confirm the method's reliability by comparing the estimated transaction data with the inter-regional input-output tables. We analytically considered the transition for all network configurations, demonstrating that the transaction quantity depends on the amount of labor and distance. We also demonstrated that our model fits well with data from business transactions, implying that the whole network structure can be used to model money flow in the real world.

Many previous studies showed that house rent is affected by residential property characteristics, house surrounding environment, facilities, and so on. However, there are few researches on finding the relationship between house rent and people's activities. Thus, we used hourly location-based big data collected by mobile phone GPS data to monitor people's activities all over the city. Multiple residential property characteristics and environments helped to verify if there is a relationship between house rent and people congestion in Tokyo. We find that people congestion has relationship with house rent and make more accurate prediction. We also employed linear and regularization regression and artificial neural network as algorithm and find artificial neural network might be the best calculation method.

This study examines the economic impact on the supply chain of a large-scale flood in the Arakawa river area in Tokyo by using the geographic information system (GIS) data on inter-firm transactions. First, we identify the firms that were unable to continue doing business due to damage by using flood simulation analysis data on the Arakawa area. Second, we identify the firms that have business relationships with affected firms by using inter-firm transaction network data. Then, we discuss and analyze the industrial structure of the firms that have relationships with affected firms. Finally, we estimate the amount of transactions affected. Our results are as follows. First, 15-28% of the firms in Japan have business relationships with the affected firms. The figure increases to 44-48% when second-order firms whose transaction partners have business relationships with affected firms are included, and to nearly 52% when fifth-order firms are included. The impact on major cities, such as Tokyo and Osaka, is significant. By industry, the economic impact on the financial and transportation industries is large, while that on the agriculture, forestry, and fisheries industries is small. These results indicate the importance of business continuity planning for large-scale floods.