The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used a Wi-Fi scanner device to track tourists’ traveling behavior in Hokkaido’s tourism area, which occupies a large region that features a unique natural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important for maintaining the tourism atmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARM method integrated with a Wi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies.

As the population is shrinking in many municipalities in Japan, one of its effects is the vacant house crisis. The rise of empty houses profoundly affects the city’s society and economy, e.g., property value reduction, increased crime rate, poor sanitation, and housing market stagnation. To better understand the mechanism of the vacant house crisis, the present study proposes to examine the determinants of housing vacancy spatial distribution with the case study of the city of Sapporo. The results highlight the severe vacant cluster in the central city, which would seem to link to the disequilibrium housing market rather than the urban decline. Regarding vacancy determinants, demographic features were the most influential factors, followed by housing and neighborhood characteristics. Specifically, the vacancy correlated strongly with a high density of single households, children, the elderly (in the center), and a high share of offices. The surplus in housing supply and the inelasticity in housing structures also affected the vacancy significantly. On the contrary, a high percentage of private property, household ownership, and the elderly (in suburban) would reduce the vacancy. For other facilities, clinics, parking, public transportation, and educational institutions had a medium effect on the vacancy. Finally, the influence factors varied, across city areas, in magnitude and direction. These outcomes would be helpful for decision-making to alleviate the rise of vacant houses and their effect on the urban area.

Urban and residential roads play an integral role in the infrastructure system of a city. Although they take up a large proportion of the national road network, maintenance plans for urban roads are beset by many problems. These include difficulty in collecting enormous volumes of data, implementing analyses, and interpreting results because of complicated frameworks. Thus, this study aims to introduce an effective and reliable method of formulating a maintenance plan using integrated criteria of spatial autocorrelation analysis and roadside conditions. The results demonstrate that defective pavements are clustered in certain areas, for example, mountainous and forested areas, which indicate environmental effects. Using a mixed index as a criterion for prioritization, approximately 55% of roadside residents (represented by the total residential housing floor area) and 90% of commercial and medical facilities surrounding critical sections gained benefit from maintenance activities in the second year. Importantly, the proposed method presents the advantages of simplifying implications and quantitative outcomes that could support local agents in not only implementing but also making decisions and interpreting such decisions for the community.

我が国では，舗装点検の効率化が積極的に進められており，IRIの測定については，簡易な加速度センサーとAIの導入が進んでいる．本研究では，市販のアクションカメラから得られる路面画像，上下振動加速度，走行速度を入力とするマルチモーダルAIを用いた低コストで簡易なIRI測定技術を開発した．まず，Elastic Netを用いたIRI推定モデルを構築し，画像から得られるひび割れ率が推定精度の向上に寄与することを明らかにした．本技術の検証試験では，検出率（区分II以上）と的中率（区分II以上）は最高ランクのA，的中率（区分III）はランクBが得られた．検出率（区分III）はランクCとなったものの，精度向上策として，正解データの追加に加え，SMOGNを用いたデータ拡張が有効であることを示した．

北海道では，救急搬送に60分以上を要する地域が多く，さらに，車両振動に注意が必要となる路面損傷箇所が広く分布する．本研究では，道内の三次救急搬送路線を対象に，救急車プローブ調査と路面性状調査を実施し，路面損傷が救急車の走行速度に与える影響を統計モデルにより分析し，複数の舗装修繕シナリオにおける搬送時間を推計した．まず，速度低下には，信号交差点などの道路構造に加えて，ひび割れ率，IRI，低温ひび割れ本数などの路面性状も影響することを明らかにした．搬送時間の推計では，低温ひび割れが1本以上ある100m区間を簡易補修すると現状から約2分短縮でき，また，高水準な路面管理を行う場合に比べ，損傷度が1.5倍増加する場合には，約4分以上の到着遅れが生じる可能性が示唆された．