Nihonbashi, a commercial hub in Tokyo’s Chuo Ward, has a resident population of 170,000 and a daytime population of 650,000. Alongside other wards in metropolitan Tokyo, it confronts multiple urban risks, including floods, earthquakes, and heatwaves. According to Mainichi News (2019). 38% of evacuation centers across Tokyo’s 23 wards are located in severely flood-prone areas, particularly in the eastern low-lying regions. Chuo Ward’s proximity to Tokyo Bay heightens its susceptibility to these hazards. Tokyo has experienced a rising frequency of heatwaves, with 2022 marking the hottest summer since 1875, characterized by nine consecutive days of temperatures reaching 35°C. In 2024, 123 heat-related deaths were recorded. This vulnerability is intensified by the urban heat island effect, where anthropogenic heat in central Tokyo can exceed 400 W/m², a level classified as ‘extreme danger’ by Harlan et al. (2006). Tokyo’s Climate Adaptation Plan proposes heat countermeasures, including urban enhancements such as cool pavements, urban greening, and the establishment of cool spots; however, it lacks provisions for immediate responses to heatwave events. Official government guidance remains limited to recommendations for hydration and sheltering in place. In contrast, a technical report by the WHO Kobe Centre (2013) delineates three tiers of heat risk and corresponding responses, including the redistribution of vulnerable individuals to air-conditioned environments.

While adaptive and mitigative strategies are vital for managing the long-term impacts of urban risks, the immediate response behaviors of individuals in affected areas warrant equal attention. In Nihonbashi, the efficacy of evacuation plans is undermined by the significant proportion of evacuation centers situated in flood-prone zones, compounded by logistical challenges associated with navigating high-rise buildings and dense street networks. Previous research, such as Yamamoto and Li (2017), has employed the Ant Colony Optimization (ACO) algorithm to evaluate evacuation route safety in central Tokyo, focusing on Shibuya Ward near Nihonbashi. This study accounted for road blockage probabilities due to earthquake-induced building collapses and identified high-congestion routes to avoid, ultimately proposing safer alternatives.

The objective of this study is to assess the adequacy of Nihonbashi’s existing evacuation plans, encompassing both indoor movement within buildings and outdoor navigation to safe areas. With a focus on social vulnerability to urban risks, namely heatwaves, earthquakes, and flooding, the aim is to safeguard residents during emergencies. This study adopts a comprehensive, data-driven approach to evaluate and enhance evacuation strategies in Nihonbashi, integrating diverse data sources, advanced analytics, and predictive modeling tools. The research begins with a robust data collection process to capture the multifaceted nature of Nihonbashi’s urban environment. Spatial data, including building footprints, road networks, and open spaces, will be sourced from OpenStreetMap, Plateau and municipal records. These datasets provide the foundational geometry and topology needed to model the physical landscape accurately. Socioeconomic data, such as age distribution, income levels, and housing characteristics at the neighborhood level, will be gathered from census records and local government databases. This information is essential for understanding social vulnerability, as it reveals populations most at risk during emergencies (e.g., elderly residents or low-income households). Additionally, hazard maps detailing flood risks, earthquake vulnerabilities, and Urban Heat Island (UHI) effects will be incorporated from governmental and research institutions. These maps contextualize the environmental threats specific to Nihonbashi, enabling a risk-informed analysis.

The collected data will be analyzed using a combination of network analysis and spatial statistics to uncover patterns and vulnerabilities. For street network modeling, Python libraries such as NetworkX and OSMnx will be employed. NetworkX facilitates the assessment of connectivity by calculating metrics like node degree and betweenness centrality, identifying critical junctions in the road network. OSMnx complements this by extracting and analyzing street network topologies from OpenStreetMap, highlighting potential bottlenecks and high-traffic zones that could impede evacuation. For socioeconomic hotspot mapping, ArcGIS Pro is used to perform spatial autocorrelation analysis with Global Moran’s I, which measures whether vulnerability indicators (e.g., poverty, age) are clustered, dispersed, or randomly distributed across Nihonbashi. Additionally, Getis-Ord Gi* statistics identifies statistically significant clusters of high vulnerability, producing hotspot maps that pinpoint areas where physical risks (e.g., flooding) and social vulnerabilities (e.g., elderly populations) converge. These analyses will generate actionable insights into high-risk zones requiring prioritized attention.

The core of the methodology is the development of an urban digital twins of Nihonbashi using AnyLogic, a versatile simulation platform as a predictive model to inform decisions. This Nihonbashi digital twins will integrate spatial, socioeconomic, and hazard data into a dynamic, interactive model that replicates both indoor (e.g., building interiors) and outdoor (e.g., street networks) environments. The model will simulate various scenarios, including evacuation dynamics under heatwave, flood, and earthquake conditions, commuter patterns during peak hours, and UHI impacts on pedestrian movement. Key performance metrics, such as Required Safe Egress Time (RSET), will be tracked to assess evacuation plan effectiveness. AnyLogic’s multi-method simulation capabilities (e.g., agent-based and system dynamics modeling) enable the representation of individual behaviors (e.g., panicked movement) and systemic factors (e.g., traffic flow). Beyond simulation, the digital twins serves as an interactive visualization platform, supporting data representation, geospatial analytics, predictive modeling, and scenario-based decision-making. Stakeholders can use it to visualize risk scenarios, test infrastructure interventions (e.g., widened stairwells), and optimize evacuation routes.

The study’s outcomes include risk hotspot maps highlighting convergences of physical and social vulnerabilities, paired with recommendations for alternative evacuation routes. The urban digital twins will serve as a practical tool for stakeholders, enhancing Tokyo’s Climate Adaptation Plan by integrating immediate response strategies with long-term infrastructure solutions. This dual focus is critical for bolstering climate resilience in dense urban settings like Nihonbashi.

References

Harlan, S. L., Brazel, A. J., Prashad, L., Stefanov, W. L., & Larsen, L. (2006). Neighborhood microclimates and vulnerability to heat stress. Social Science & Medicine, 63(11), 2847–2863. https://doi.org/10.1016/j.socscimed.2006.07.030

Mainichi News. (2019). 38% of evacuation centers in central Tokyo in expected flood areas. https://mainichi.jp/english/articles/20190114/p2a/00m/0na/002000c

World Health Organization. (2013). Heat-health action plans: Lessons from the Western Pacific Region (Technical Report). WHO Kobe Centre. https://iris.who.int/handle/10665/208167

Yamamoto, K., & Li, X. (2017). Safety evaluation of evacuation routes in Central Tokyo assuming a large-scale evacuation in case of earthquake disasters. Journal of Risk and Financial Management, 10(3), 14.