Networks, Digital Twins, and AI

Permanent URI for this collectionhttps://hdl.handle.net/10125/112437

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    Bridging Flood Sensing and Traffic State Estimation for Roadside Flood Sensor Placement: A Bayesian Network-based Approach
    (2026-01-06) Pan, Xiyu; Mohammadi, Neda; Taylor, John
    Urban flooding's impact on road networks and traffic is becoming a more serious problem in the context of climate change. Estimating traffic states under flood impacts requires information about road inundation. Roadside flood sensors are becoming an important source of this information. This study proposes a Bayesian Network-based approach to strategically place roadside flood sensors to enhance the information input for traffic state estimation. By integrating historical traffic speed data with flood-induced road closures, the method quantifies the uncertainty reduction in traffic estimation through entropy analysis and applies a greedy algorithm to optimize sensor placement. In the results, flood information from optimally placed sensors reduced the uncertainty of traffic states in the Bayesian Network, thereby helping traffic state estimation and prediction achieve more certain estimation results. Beyond proposing a sensor placement method, this study also enhances the information value of flood sensing and is expected to promote larger-scale application of roadside flood sensors.
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    Toward Smart City Digital Twins: Bridging Preparation and Response Phases in Urban Flood Evacuation through Agent-Based Modeling
    (2026-01-06) Nakai, Fuko; Wise, Sarah; Nakanishi, Hitomi; Han, Wendi; Nakamura, Shinichiro; Otsuyama, Kensuke; Yoshimura, Kei; Sekimoto, Yoshihide
    This study presents an agent-based digital twin model for urban flood evacuation, using Okazaki City, Japan, as a case study. The model bridges the preparation and response phases of flood management by combining static infrastructure data with dynamic flood hazard and evacuation instruction data, enabling simulation of individual-level evacuation behaviors under changing conditions. By quantifying journey durations, the model revealed disparities in accessibility to safety. Results show that evacuation success depends on infrastructure and individual factors, with limited mobility increasing the risk of delay or failure. It also supports scenario-based evaluation of policy options, such as evacuation strategies and road closures, enhancing applicability across different disaster types and urban contexts. While the current version relies on secondary data and simplified assumptions, it demonstrates the feasibility of using digital twins for evacuation planning. Incorporating behavioral and traffic data and engaging stakeholders more deeply would enhance the model’s realism and strengthen its role as a reliable mirror of real-world evacuation processes for policy evaluation.
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    Introduction to the Minitrack on Networks, Digital Twins, and AI
    (2026-01-06) Mohammadi, Neda; Taylor, John