Asian cities are unique, where people may need more than just walking; however, the walkability research in a local context has received less attention. This paper analyses the walking needs and the influential factors of walking behavior in Asian cities. A comparative analysis in Bangkok and Nagoya is presented, taking into account the characteristics of local street design and development. Street evaluation experiments of various streets are conducted through 360-degree videos. The factor analyses indicate that the walking needs in both cities can be considered in two levels, but the components of the needs are different. Being smooth and having no obstacles are common basic needs. Safety and security are regarded as basic needs in Nagoya, but as higher-level needs in Bangkok. Attractiveness of activity and walk pleasure are the common upper-level needs. Street elements such as width, green, traffic, parking, pollution exposure, and activity are found to be influential on the walking needs. The structural equation models indicate that higher-level needs have more of an influence than the lower-level needs for walking behavior in Bangkok and for shop walking in Nagoya, while the basic need has more influence on walking willingness for transit access. These findings call for practical attention for street design and development where local context and street functions must be carefully considered.

Recently, deep learning techniques, specifically semantic segmentation, have been employed to extract visual features from street images, a dimension that has received limited attention in the investigation of the connection between subjective and objective road environment perception. This study is dedicated to exploring and comprehending the factors influencing commuters’ perceptions of the road environment, with the aim of bridging the gap in interpreting environmental quality in Thailand. Semantic segmentation was applied to identify visual objects, expressed as a percentage of pixels represented in 14,812 street images from the Bangkok Metropolitan Region. Subjective road environment perception was assessed through a questionnaire, with a total of 3600 samples collected. Both sets of data were converted to average values per grid, with a grid size of 500 × 500 square meters, resulting in a total of 631 grids with data points. Finally, a multiple linear regression model was employed to analyze the relationship between the ratios of objects obtained from street images via semantic segmentation and human sensory perception of the road environment. The findings from this analysis indicate that the attributes of distinct object classes have a notable impact on individuals’ perceptions of the road environment. Visual elements such as infrastructure, construction, nature, and vehicles were identified as influential factors in shaping the perception of the road environment. However, human and object features did not exhibit statistical significance in this regard. Furthermore, when examining different road environments, which can be categorized into urban, community, and rural contexts, it becomes evident that these contexts distinctly affect the perceptions of various road environments. Consequently, gaining a comprehensive understanding of how street environments are perceived is crucial for the design and planning of neighborhoods and urban communities, facilitating the creation of safer and more enjoyable living environments.

The well-being of residents is a top priority for megacities, which is why urban design and sustainable development are crucial topics. Quality of Life (QoL) is used as an effective key performance index (KPI) to measure the efficiency of a city plan’s quantity and quality factors. For city dwellers, QoL for pedestrians is also significant. The walkability concept evaluates and analyzes the QoL in a walking scene. However, the traditional questionnaire survey approach is costly, time-consuming, and limited in its evaluation area. To overcome these limitations, the paper proposes using artificial intelligence (AI) technology to evaluate walkability data collected through a questionnaire survey using virtual reality (VR) tools. The proposed method involves knowledge extraction using deep convolutional neural networks (DCNNs) for information extraction and deep learning (DL) models to infer QoL scores. Knowledge distillation (KD) is also applied to reduce the model size and improve real-time performance. The experiment results demonstrate that the proposed approach is practical and can be considered an alternative method for acquiring QoL.

近年，歩行空間のニーズが多様化する中，客観的な空間要素の量が主観的な評価に与える影響を明らかにすることで，ニーズによって有効な空間整備の要素を把握できると考える．本研究では国内外の歩行空間の画像認識を用いて客観的な空間指標を計測し，主観評価との関係性を明らかにすることを目的とする．まず，画像認識ツールを用いて空間指標を計測し，クラスター分析で空間シーンを分類する．そして，空間シーンを含めた空間指標と主観評価の関係について分析を行う．この結果，空間シーンは大きく人と車の量で分類され，シーン指標を含めた空間指標は量的や質的な主観評価をある程度説明できることが示された．

近年，街路空間整備では，移動を目的とした交通機能と活動を目的とした滞留機能の両面が求められており，それらの空間の有効性は，車と歩行者の道路の利用状況によっても影響を受けると考えられる．そこで本研究では，交通密度を変化させた街路 CG を作成し，車と歩行者の交通密度の違いが街路デザイン評価に与える影響を分析することを目的とする．まず，道路の交通密度指標について整理し，街路 CG を作成する．そして，交通密度を変化させた街路デザインの CG についてアンケートで評価を行う．この結果，車と歩行者の交通密度によって街路デザインの評価が大きく異なることが分かった．