Walkability is an important indicator of sustainable urban development, especially in fragmented historical blocks affected by modern development efforts. Xi’an, China, and Kyoto, Japan, which are among the oldest cities in Asia, have similar historical backgrounds and a grid-based street network that differs from European historical cities. They also have their own distinctive development characteristics. This study uses a novel quantitative approach to identify areas for improvement in the historical built environment of the two cities that, as part of an overall effort to create a pedestrian-friendly urban center, will promote and facilitate walking activities. The proposed method combines macro- and micro-aspects to identify factors that can either improve or hinder the walking environment. It was found that to ensure a walkable environment in its historical blocks, Xi’an will need to consider the potential negative impact on walkability as it pursues increased modernization. The built environment and the infrastructure of its streets need to be improved, and its historical streetscape and vegetation characteristics need to be maintained. As for Kyoto, to ensure a favorable walking environment, the focus should be on improving the vitality of its streets.

The COVID-19 pandemic has greatly affected the mobility of individuals everywhere. This has been especially true in China, where many restrictions, including lockdowns, have been widely applied. This paper discusses the impact of the pandemic on walkability, an important factor in promoting urban neighborhoods, in the main urban area of Xi’an, China, one of China’s four great ancient capitals. Based on the street view data obtained before and after the pandemic, the paper quantitatively compares changes in specific components of selected streetscapes through a deep learning (DL) street view analysis. The aim is to identify the impact of the pandemic on walkability and determine the elements that influence increased walkability in Xi’an’s historical area, using a walkability evaluation model based on a regression analysis involving three factors (streetscape components, walkability check scores, and street connectivity of space syntax for every image). Although Xi’an’s urban structure did not change significantly, the pandemic has clearly impacted street vitality, especially in terms of reducing pedestrian flow and commercial value. Based on study results, the street environment has great room for improvement, especially in the city’s historical blocks, by reconsidering safety measures to pedestrians and the important role of atmospheric aspects on the streets.

Tsunamis resulting from earthquakes can cause extensive damage, including infrastructure destruction, injuries, and fatalities. To address these risks, numerous tsunami evacuation simulations have been conducted to assess evacuation strategies and identify issues, particularly in Japan, where the Nankai Trough earthquake is anticipated, affecting underground malls in urban areas. Our previous research primarily focused on evacuating from underground to the surface, neglecting surface evacuation. Therefore, this study extended the previous study to include horizontal evacuation following the exit from the underground mall to ground level. It also considered the impact of pedestrian road crossings as interactions with vehicular traffic within the simulation to enhance realism and verify the evacuation risk. The study also explores scenarios where evacuation guide stairs are unavailable, comparing the effectiveness of alternative staircases in the evacuation plan. Results indicate that while using alternative staircases temporarily increases the number of evacuees reaching the surface, road crossings on evacuation routes become significant bottlenecks, leading to surface congestion and delayed evacuations. This raises doubts about the effectiveness of alternative staircases and highlights the need to consider alternative evacuation routes as a secondary plan. Additionally, the study reveals that reduced pedestrian flow at road crossings can significantly impact on evacuation times. Ultimately, this study demonstrated that achieving a short evacuation time from underground malls to the ground level might not always yield the best evacuation plan. It underscored the importance of considering aboveground factors in evacuation planning to ensure safety in the event of a tsunami caused by an earthquake.

Crowd management at large-scale events and specific facilities is a critical issue from the perspectives of safety and service quality improvement. Traditional methods for crowd management often rely on empirical knowledge, which has limitations in quickly grasping the on-site situation and making decisions on the spot. In this study, we developed a real-time crowd flow prediction and visualization platform incorporating an agent-based crowd simulation and an advanced crowd management system called crowd management platform as a service. In a case study focused on the area around the Tokyo Dome, we demonstrated that capturing pedestrian flow allows for accurate predictions of congestion at the nearest train station up to 10 min in advance. Moreover, the time required to predict the situation 20 min ahead for 3,000 agents was 1 min and 35 s, confirming the feasibility of real-time processing. To enhance the accuracy and reliability of the simulation results, a sensitivity analysis considering errors in pedestrian flow measurement revealed that simple linear models cannot capture the complexity of crowd dynamics adequately. Notably, the agent-based simulation replicated stop-and-go wave patterns observed in actual measurements under specific crowd conditions, confirming the advantage of using agent-based simulations. Finally, we proposed a method that enables facility managers and security personnel to conduct a more comprehensive evaluation. This method integrates their existing experience with the aggregated display of multiple simulation results, which includes consideration of errors in pedestrian flow measurement through a visualization platform.