Metro Station Research Articles

Usage Sphere and Lexico-Stylistic Features of Anthroponomastics

The article elaborates usage sphere and lexico-stylistic features of anthroponomastics. When the person names combine with toponyms, there appears a new term called “anthroponomastics”. The article revealed that anthroponomastics mainly cover the names of villages, settlements, cities, streets or avenues, restaurants or cafes, markets or shops public buildings such as schools, universities, colleges, stations, museums, metro stations, hospitals, mosques, churches, etc. Public buildings are usually named after historical figures, poets, writers, musicians, academicians or scholars, presidents while shops or markets, cafes or restaurants are named after ordinary people depending on the willing and desire of citizens. The study underlines that there is a minute distinction between anthroponomastics and personification. In personification, human character and quality are transferred onto an inanimate object while only name is transferred onto place names in anthroponomastics. Besides, novel terms such as zooponomastics, vegeponomastics, aviaponomastics have also been compared with anthroponomastics in the article.

Simulation-based adaptive optimization for passenger flow control measures at metro stations

Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.

Transit-Oriented Development in Middle Eastern Cities: The Urban Renewal Framework for the Souq Waqif in Doha, Qatar

Cities around the world are evolving dramatically due to urbanization. Over the last few decades, rapid urbanization coupled with rapid economic growth in Qatar has resulted in transport-related issues such as urban sprawl, traffic congestion, poor walkability, and an increasingly inefficient transit network lacking pedestrian accessibility and connectivity. The related sprawl (due to a lack of compactness), a lack of intricacy, and connectivity have challenged the built environment’s sustainability. The Souq Waqif neighborhood, renowned for its rich cultural heritage and historical significance, serves as the focal point of this study. Its central positioning and status as a prominent social nexus within Doha render it an ideal case study. Specifically, this research investigates the Souq Waqif metro station, evaluating it through the lens of five distinct design aspects: (1) walkability, (2) ground use balance, (3) mixed-use spaces and public spaces, (4) multi-modality and transportation hubs, and (5) public transportation network. To reach this aim, the study methodology uses a qualitative approach informed by the Integrated Modification Methodology (IMM). The IMM framework provides a means to transform underperforming neighborhoods like Souq Waqif, into more cohesive and sustainable communities by retrofitting, renovating, and reactivating the existing urban fabric. Site observations, interviews, and surveys were used to collect data to complement the analysis. Findings highlight the deficiencies in connectivity and multimodal transportation adjacent to the Souq Waqif metro station. In addition, the ability of metro stations to conserve crucial cultural legacy and social identity in rapidly urbanizing cities like Doha is explored. To this end, recommendations are given to increase the efficiency and sustainability of Souq Waqif and the like.

Understanding the interplay among urban public transport modes: Spatial variation and built environment effects

Research on public transport systems is continually driven by the societal benefits of transport services. However, the nuanced effects of the built environment on public transport mode shares across different modes have received little attention. Using multi-source travel data from Shenzhen, this study presents an analytical framework, which integrates the light gradient boosting machine and Shapley additive explanations, to delve into the complex interplay among public transport modes. The results reveal that most origin-destination pairs with available metro services exhibit a metro share exceeding 50%, underscoring the high attractiveness of the metro. Travel distance emerges as the primary determinant of mode share, with transport-related characteristics proving more influential than land use modifications. This study identifies nonlinear effects of the built environment on mode share, with specific thresholds for built environment characteristics suggesting fine-tuned planning strategies. Metro station densities of 2 counts/km2 correlates with increased metro share, while bus stop densities of 15 counts/km2 are associated with higher bus share. Areas within 2 km of a metro station or with bus stop densities below 15 counts/km2 tend to have higher bike shares. Specific land use ratios and high levels of land use mix are linked to increased shares of certain modes. The findings suggest optimizing bus services in relation to metro availability and offer guidance for balanced planning in promoting metro and shared mobility to achieve a sustainable transport system.

Analysis of the spatial characteristics and driving forces of underground consumer service space in Chinese megacities based on multi-source data

Underground consumer service spaces (UCSS) offer new solutions for urban residents’ daily needs, but existing studies on their distribution and driving forces are often fragmented and overshadowed by research on other underground spaces, lacking targeted analysis. This study examines UCSS in the central urban areas of seven representative Chinese megacities. Using spatial analysis methods like kernel density estimation, multi-distance spatial clustering, and geographical detectors, the spatial characteristics and driving forces of UCSS are analyzed alongside aboveground consumer service spaces (ACSS). Results show that both ACSS and UCSS exhibit multi-centered, concentric spatial patterns, though UCSS demonstrates higher spatial aggregation. Unlike other underground public spaces (UPS), UCSS relies more on service industry agglomeration and market factors, while other UPS are more influenced by surrounding development intensity. UCSS follows the core principles of central place theory but deviates from the market-driven patterns typical of ACSS. Socioeconomic conditions and transportation infrastructure form the foundational basis for UCSS distribution, while service industry agglomeration, market dependence, and land development intensity exert more direct influence. The commercial atmosphere and existing underground space development play critical roles in UCSS distribution. Two key spatial scales for understanding UCSS distribution are the strong influence zones of shopping malls and metro stations, and high-density urban areas.

Active travel in the university setting: Assessing the effects of social behavior, socioeconomics, and spatial location

University campuses are pooling efforts to promote active mobility to reduce their negative impacts on the urban environment, which is significantly influenced by the overreliance on motorized modes of transport. Providing sufficient and safe accessibility conditions for active travel has been highlighted as a crucial strategy for transforming campuses into more livable and sustainable areas in cities. To further explore the likelihood of active mobility uptake at university campuses, this study explored university students’ mobility patterns over time, examining the role of social behavior, socioeconomics, and spatial location factors. The Faculty of Engineering at the University of Porto, Greater Oporto, Portugal, provided the empirical focus for this research. The data analyzed were acquired through surveys of representative samples and spatial analysis over the academic years of 2012, 2017, and 2023. The statistical analysis explained the tendencies and multifactorial influences on travel behavior among university students. Results indicated that travel distance is associated with housing options and travel costs, whereas access to a metro station was associated with walking or cycling. Hence, this study contributed to a deeper understanding of active travel behavior. It provided insights to guide planning practitioners and decision makers in creating integrated transport policy packages that address the barriers and needs of the university community and the surrounding neighborhoods.

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models.

Retrospective Analysis of the Anti Spitting Laws in Various Geographical Locations and Implementing Sectors in India

Objective: Public spitting attributable to Smokeless Tobacco (SLT) use not only causes a threat to public cleanliness but also to the people exposed around them. An electronic search on various anti-spitting laws and ban in India was done using Google search engine for duration of three months (November 2018 to January 2019). Material and Methods: The original state gazettes were reviewed to extract the data on spit ban and penalty. Renewals of Acts were also thoroughly checked for recent updates of penalty or severity of the act. The law implementing bodies were broadly classified as National, State, Municipal, Village councils (Gram Panchayat), Airports, Metro Railway, and Company Law Tribunal. Results: A total of 42 Anti-spitting laws were identified in this search and 16 states had anti spitting law under different sectors. Discussion: Under these laws, sign boards and warnings are placed commonly at Airports, Railway stations, and metro stations. States with high rates of SLT users like Tripura, Arunachal Pradesh and Mizoram did not have any policy on spitting. Conclusion: Strong enforcement of anti-spitting laws in India will not only reduce spread of communicable diseases but also indirectly will reduce the act of using SLT also.

Exploring the complex relationship between metro and shared bikes in the built environment: Competition, connection, and complementation

Merging the flexibility of bike-sharing systems with the high capacity of metro transit significantly enhances both connectivity and efficiency in urban transportation, promoting eco-friendly travel options and supporting sustainable urban development. Current studies primarily examine how these two transportation modes work together to enhance urban travel efficiency and convenience. However, there is still a lack of discussion on the spatial heterogeneity of the competitive and complementary relationships between two modes across different built environments. This study selects Shenzhen as a case study and employs a data-driven approach to explore the relationships between bike-sharing and the metro system in practical application, including competition, connection, and complementation. The OPGD model is deployed to assess how the built environment influences these dynamics. The results reveal that bike-sharing typically complements the metro system, with longer ride durations occurring mainly in the urban core areas. Conversely, competitive interactions between these two modes are less frequent and associated with shorter rides, typically occurring in locales with a high density of metro stations. Educational, service, and residential factors are the main influences on people's choice of the "bike-sharing + metro" travel mode. The built environment exerts a greater impact on competitive relationships and less on complementary ones.

Does built environment improvement promote transport equity for internal migrants in Beijing?

Numerous studies and practices leverage improvements in the built environment to encourage green transportation due to its significant potential to shape travel behaviors. Yet, the equity impacts of these improvements on vulnerable populations remain underexplored. This study seeks to address the question: whether built environment improvement strategies advance transport equity? Using a travel survey from Beijing and combining the Capability Approach with the Random Parameter Logit model, we develop a multilevel discrete choice model to estimate capability changes for four hypothetical scenarios of built environment improvement and conduct transportation equity analysis using Maximax principle. Our results show that increasing the mix of land use is prioritized to enhance commuting equity for carless internal migrants. Expanding bus route coverage is recommended to alleviate commuting inequality for low-income internal migrants. However, increasing road density or decreasing the distance to the nearest metro station may not effectively mitigate commuting inequities for disadvantaged internal migrants.

Effects of foundation pit excavation on adjacent metro stations and shield tunnel structures: a study on horizontal displacement

PurposeThe aim of this study is to investigate the horizontal displacement effects of foundation pit excavation on adjacent metro stations and shield tunnel composite structures. It seeks to develop a theoretical calculation method capable of accurately assessing these engineering impacts, aiming to provide practical assistance for engineering applications.Design/methodology/approachThis study introduces a model for shield tunnel segments incorporating rotation and misalignment, considering the constraints of metro stations. It establishes a displacement model for tunnel-station combinations during foundation pit excavation, deriving a formula for calculating station-proximal tunnel horizontal displacements. The method's accuracy is validated against field data from three engineering cases. The research also explores variations in tunnel displacement, inter-ring shear force, misalignment and rotation angle under different spatial relationships between pits, tunnels and stations.FindingsThis study models uneven deformation between stations and tunnels due to bending stiffness and shear constraints. It enhances the misalignment model with station-induced shear effects and introduces coefficients for their mutual interaction. Results show varied responses based on pit-station-tunnel positioning: minimal displacement near pit edges (coefficients around 0.1) and significant effects near pit centers (coefficients from 0.4 to 0.5). “Whip effect” from station constraints affects tunnel displacement, shear force, misalignment and rotation, with fluctuations decreasing with distance from excavation areas.Originality/valueThis study demonstrates significant originality and value. It introduces a novel displacement model for tunnel-station combinations considering station constraints, addressing theoretical calculations of horizontal displacement effects from foundation pit excavation on metro stations and shield tunnel structures. Through validation with field data and parameter studies, the concept of influence coefficients is proposed, offering insights into variations in structural responses under different spatial relationships. This research provides crucial technical support and decision-making guidance for optimizing designs and facilitating practical construction in similar engineering projects.

Creating Future Stations: The Application of Large Generative Models in Metro Design

New technologies, including the use of generative models, are beginning to actively influence design decisions in the decoration of metro stations. The study of the possibilities of using generative artificial intelligence as an art design tool in the decoration of metro stations is relevant, because it can significantly accelerate the design process, reducing time and labour costs for the development and optimization of projects. The novelty of the article lies in the fact that for the first time the process of interaction between the designer and software and hardware of large generative models is considered in detail on the example of designing the interior of a metro station on the theme “ocean”.The problem of creation of original design project in conditions of generative models at the moment is not solvable without direct control on the part of a human, as this technology, for lack of human perception, first of all orientate on expressive means contained in training databases. Therefore, when designing metro stations using generative models, images generated through text often lose the semantic and emotional meanings that the designer put into them. As a result, the generated design project does not fully conform to the designer’s intent and requires further manual adjustment and optimization. Thus, as long as there is a need for conscious and purposeful communication of ideas and feelings through art and design, the problem of endowing a work with meaning using generative models will require human involvement.At the same time, it should be noted that in the artist’s work, the use of generative pre-trained transformers is manifested in the ability to easily create realistic visual effects, to execute images in different styles, focusing on the creative individual manner of famous masters, and to improve design ideas. The advantage of this technology is that it can reduce the designer’s workload while improving the efficiency and accuracy of the design.

The impact of heterogeneous accessibility to metro stations on land use changes in a bike-sharing context

The integration of urban rail transit and land use has been adopted as a crucial approach to fostering compact development in cities. Proximity to rail transit stations can increase the probability of land use changes, while few studies have analyzed the spatial heterogeneity of the impact of rail transit on land use changes. This study proposes a distance-decay function to delineate the spatial heterogeneity of metro station accessibility using bike-sharing data and examines the impact of metro stations accessibility on land use changes. Jiading New Town in Shanghai, China, is selected as our study case. By utilizing a non-linear distance-decay function to delineate metro station accessibility as a driving factor for training neural networks in a vector-based cellular automata model, an improvement in simulation accuracy is achieved compared to the model using a linear distance-decay function. This study could help establish more efficient strategies for promoting integrated development of rail transit and land use. The significance of this study lies in the generality of the optimized land use vector-based cellular automata model considering the spatial heterogeneity of metro station accessibility, which could also be applied to other situations, such as considering the accessibility to bus stops and road networks.

Modelling study on pedestrian evacuation dynamics considering exit selection behaviour under flood disaster

Flood spreading in metro stations is a dangerous hazard that frequently causes casualties and property losses. However, pedestrian evacuation under flood spreading scenarios has not been fully investigated. To fill this gap,we propose an extend Floor-Field model incorporating flood spreading and exit selection behaviour of individuals. In the model, the dynamic flood spreading process on metro platforms is expressed by using a simulation software named Mike21. The optimal exit selected by pedestrians in each step is decided on three factors, that is pedestrian density around the exit, pedestrian distance to the exit, and water depth at the exit. Meanwhile, the movement speed of pedestrain also changes with the water depth at each time step. Based on this, the transition probability to neighbor cell including static field, dynamic field, height field, and water flow field is calculated. This results in a flood evacuation model considering the pedestrian's behaviour when choosing an exit. The effects of flooding, pedestrian density, different exit selection behaviours, water flow field, and inlet flow on evacuation were thoroughly analysed. The analysis demonstrates that the likelihood of flooding significantly impacts the evacuation of pedestrians from the subway station; the more flooding is taken into account, the more influential the impact. In addition, the effect of inlet flow on evacuation is also noteworthy; considering exit selection behaviour can significantly increase the effectiveness of evacuation. The study's findings can be used to develop evacuation plans for flooded metro stations.

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

Identifying the Key Factors of Intermodal Travel Using Interpretative Ensemble Learning

Intermodal travel is considered an effective method for achieving sustainable urban transportation. Understanding the factors influencing intermodal travel is crucial. Due to the relatively small proportion of intermodal trips within cities, datasets are significantly imbalanced, leading to poor performance of traditional logit models. In this paper, we develop a novel interpretable ensemble learning (IEL) model to identify key factors through voting by five types of machine learning models. We test our model on two datasets with different numbers of features. The results show that travel duration, travel distance, vehicle ownership, and distance to the nearest metro station are the key factors influencing intermodal travel, cumulatively contributing nearly 70% in the JDS2021 dataset with 14 features and nearly 80% in the SHS2019 dataset with 8 features. Furthermore, we analyze the interpretability of our model and compare it with the logit model. Our model enriches the methodology for modeling intermodal travel behavior.

Remote control of the technical condition of metro trains using seismological tools

As urban road congestion increases and people shift to using the subway, the safety of under-ground transportation becomes a priority. Therefore, it is necessary to establish a monitoring sys-tem for subway trains to detect malfunctions that occur during operation. The article proposes an engineering seismology method for remote monitoring of the technical condition of metro trains based on the analysis of micro seismic vibrations. This method allows remote control of the dynamic parameters of each train and their deviations from the norm. The authors demonstrate how a seismic recorder placed in a building adjacent to a metro station can be used to monitor changes in the amplitude-frequency spectrum of vibro seismic recordings. It is used to determine the arrival and departure times of trains, and it is shown that observations are best made for departures due to their more pronounced signal amplitude. The recorded signal of a metro train should fall within a certain frequency range, which is determined based on its average values, and any deviation of the observed signal spectrum from this norm indicates changes in the train's condition. The proposed approach not only helps improve passenger safety, but also reduces the risk of emergencies associated with technical malfunctions of rolling stock. In the future, it is possible to develop software equipped with a database of statistical data, which will allow even more efficient control of metro operations and prompt response to any deviations, thus ensuring a high level of safety and comfort for passengers.

A Multi-Objective Prediction XGBoost Model for Predicting Ground Settlement, Station Settlement, and Pit Deformation Induced by Ultra-Deep Foundation Construction

Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are more popular than physical models because they can handle dynamic process data. However, these models frequently fail to establish an appropriate balance between accuracy and generalization capacity when dealing with multi-objective prediction. This work proposes a multi-objective prediction model based on the XGBoost algorithm and introduces the Random Forest Bayesian Optimization method for hyperparameter self-optimization and self-adaptation in the prediction process. This model was trained with monitoring data from a deep foundation pit at Luomashi Station of Chengdu Metro Line 18, which are characterized by a sand and pebble stratum, cut-and-cover construction, and a depth of 45.5 m. Input data of the model included excavation rate, excavation depth, construction time, shutdown time, and dewatering; output data included settlement, ground settlement, and pit deformation at an operating metro station only 5.7 m adjacent to the ongoing pits. The training effectiveness of the model was validated through its high R2 scores in both training and test sets, and its generalization ability and transferability were evaluated through the R2 calculated by deploying it on adjacent monitoring data (new data). The multi-objective prediction model proposed in this paper will be promising for monitoring the data processing and prediction of settlement of surrounding buildings for ultra-deep foundation pit engineering.

Integrating Metro Stations with the Adjacent Urban Fabric Using TOD Principles: A Case of Agargaon Metro Station, Dhaka

Transit-Oriented Development (TOD) principles offer a promising framework for integrating metro stations with their surrounding urban fabric, promoting sustainable urbanization and efficient transportation systems. Dhaka, one of the fastest-growing cities globally, faces significant challenges in traffic congestion, air pollution, and urban sprawl. Introducing metro systems offers a promising solution to alleviate these issues and enhance urban mobility. This study presents a case study of Agargaon Metro Station in Dhaka City, examining its integration with the adjacent urban fabric using TOD principles. Through a combination of field observations, spatial analysis, and stakeholder interviews, the study evaluates the current state of Agargaon Metro Station. On-site assessments examined the station's physical infrastructure, accessibility, and connectivity with nearby areas, while Geographic Information Systems (GIS) analyzed spatial data, including land use patterns and transportation networks. Semi-structured interviews with urban planners, government officials, and community members provided insights into the challenges and opportunities for implementing TOD at Agargaon. The findings reveal that the station is underutilized as a TOD hub, with inadequate pedestrian infrastructure and mixed-use developments, leading to poor connectivity and accessibility. The study highlights the necessity of improved land use planning, policy support, and community engagement to enhance the station's role in fostering TOD. These recommendations, if implemented, could alleviate traffic congestion, improve air quality, and create more livable urban spaces, thereby enhancing Dhaka's overall quality of life. Additionally, the research contributes to the social and economic dimensions of urbanization by offering a framework that can be adapted to similar metro stations in Dhaka and other rapidly urbanizing cities.

An MCDM-GIS framework for assessing flooding resilience of urban metro systems

Extreme rainstorms pose significant challenges to the operation of urban metro systems. This study proposes a data-driven framework for evaluating the flood resilience of metro systems, considering the spatio-temporal differences in rainfall patterns. Furthermore, it offers recommendations to bolster resilience against flooding, ensuring the safety and continuity of metro systems in the face of increasing climatic challenges. The proposed framework adheres to the tenets of design science research, a methodological approach that synergizes theoretical and empirical aspects. This framework was examined by assessing flood resilience at 254 metro stations in Wuhan. The study reveals insights into the spatial distribution of flood resilience among metro stations, with those located in suburban areas being riskier than their counterparts in city centers. Additionally, the implementation of emergency flood response strategies and the execution of pre-disaster drills play a pivotal role in reducing the potential losses caused by flooding.