Riverine flooding in Southeast Asian cities increasingly affects their residents, often causing prolonged negative consequences due to their geographic position in lowland areas. The rapid expansion of urban areas into highly vulnerable zones has only exacerbated this issue. To mitigate flood risk, it is crucial to assess and map flood susceptibility and for incorporation into future urban planning. This study evaluates and compares the predictive performance of logistic regression (LR) and the autoencoder multilayer perceptron (AE-MLP), an advanced model, for urban flood risk assessment in Ubon Ratchathani, Thailand. The models were assessed using an area under the receiver operating characteristic curve (AUC) and several indexes (accuracy, F1-score, precision, Recall). This study also mapped vulnerable populations based on estimated flood risk from each model. The AE-MLP model outperformed the LR model, achieving an AUC of 0.950 compared to 0.860. An analysis of flood risk distribution revealed that the LR model estimated 1.79% of the population residing in high-risk areas and 0.83% in very high-risk areas. In comparison, the AE-MLP model estimated 24.00 and 7.97%, respectively, demonstrating its superior sensitivity in identifying vulnerable zones. These findings indicate that the AE-MLP model can significantly improve flood risk prediction and accurately identify high-risk areas. Integrating these models into urban planning and disaster management frameworks can enhance resilience to flooding.

This study focused on pre-flood measures to estimate evacuation times impacted by flood depths and identify alternate routes to reduce loss of life and manage evacuation measures during flood disasters. Evacuation measures, including traffic characteristics, were reviewed according to different flood depths. Several scenarios were constructed for different flooding situations and traffic volumes. Evacuation times in the study area were evaluated and compared for all scenarios with reference to dry conditions. Results of network performance indicators compared to the dry situation showed that average speed dropped to 2 km/h, VHT rose above 200%, and VKT rose above 30%. Cumulative evacuee arrival percentage increased when flood levels were higher than 5 cm. Flood levels of 10–15, 15–20, 20–25, and 25–30 cm represented percentages of remaining evacuees at 9%, 19%, 49%, and 83%, respectively. Time taken to evacuate increased according to flood level. For flood depths of 5–30 cm, travel time increased by 40, 90, 260, and 670 min, respectively, suggesting the need for early evacuation before the flood situation becomes serious.

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In response to escalating climatic threats, this research innovatively integrates flood risk assessment with road network dynamics to formulate optimized evacuation strategies in flood-susceptible regions. Employing a path-based traffic model and flood risk index, our method significantly decreases travel durations by 25% and vehicle hours by 20% compared to traditional frameworks. Complexities arise in intricate evacuation systems. Zone 4 was a critical node due to a 15% rise in vehicular inflow and unique flood risks, emphasizing the necessity for tailored evacuation plans considering variable traffic and flood risks. Our approach enhanced evacuation efficiency and also resulted in a 10% increase in fuel consumption while highlighting areas for further optimization. This study proposes a refined, sustainable evacuation model that harmonizes flood risks and traffic dynamics and offers insights for resilient urban development amid climatic uncertainties.

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes.