Urban System Research Articles

Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-Temporal Convolutional Network-Gated Recurrent Unit model

Urban real-time flood forecasting is crucial for flood prevention and sustainable development, but it poses challenges due to data inputs and activation functions selection in data-driven models without sufficient focus of geographic heterogeneity. In this study, a novel Seasonal Trend Decomposition using Loess (STL)-Temporal Convolutional Network (TCN)-Gated Recurrent Unit (GRU) model is proposed to improve urban real-time flood forecasting accuracy, twenty-one different activation functions are considered for geographic heterogeneity. Experiments are conducted at six urban drainage system locations in Odense, Denmark. The results show that: (1) STL effectively prepares data for forecasting using TCN and GRU models, leading improved performance compared to single models. STL-TCN-GRU deep learning model demonstrates strong applicability in urban forecasting, achieving an overall accuracy of 0.0079, 0.0140, 0.0482 and 0.9581 in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe efficiency coefficient (NSE), respectively. (2) Softsign emerges as the best activation function for forecasting in lower drainage system locations, with average accuracy of 0.0094, 0.0018, 0.0049 and 0.9938 in MAE, RMSE, MAPE and NSE, respectively. Furthermore, Softsign proves to be the best activation function for forecasting in middle drainage system locations, with average accuracy of 0.0047, 0.0081, 0.0309 and 0.9547 in MAE, RMSE, MAPE and NSE, respectively. Swish is the best activation function for forecasting in upper drainage system locations, with average accuracy of 0.0052, 0.0080, 0.0627 and 0.9060 in MAE, RMSE, MAPE and NSE, respectively. This study provides valuable insights for urban real-time flood forecasting modeling with high accuracy and evidence for activation function selection in data-driven models like STL-TCN-GRU for geographic heterogeneity.

Optimizing pump operations in water distribution networks: Balancing energy efficiency, water quality and operational constraints

Optimizing pump operation in urban water distribution systems represents a critical task for enhancing energy efficiency. Despite extensive research, achieving optimal pump operation remains a challenge due to the presence of conflicting objectives, such as minimizing energy costs and ensuring adequate pressure head availability. The recent emphasis on maintaining distributed water quality further increases the complexity of the problem. Mathematically, the optimization problem manifests as a non-convex and non-continuous search space, necessitating the application of non-derivative optimization techniques, which are highly susceptible to constraints. This work presents a comprehensive analysis of optimal pump operation, considering energy efficiency and water quality as primary objectives. A series of constraints are progressively incorporated into the model to evaluate their impact on the final operational strategy. Initially, single-objective optimization is employed using the Particle Swarm Optimization (PSO) algorithm, a robust and widely adopted approach in water resources research. Subsequently, a multi-objective algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is utilized to identify the Pareto front, encompassing both energy consumption and water quality indicators. The results demonstrate that constraints exert a significant influence on the final energy consumption and the convergence of the optimization process. Moreover, by simultaneously considering both objectives, this study proves the feasibility of identifying an optimal operational scheme that minimizes energy consumption while maintaining acceptable water quality.

Assessment of the Potential Contribution of the Urban Green System to the Carbon Balance of Cities

Reducing greenhouse gas emissions is a crucial challenge in urban areas characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars have analyzed both the ability of species-specific and layout-specific green infrastructure to increase carbon sequestration and the best location sites for new green infrastructure to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement based on vegetation species-specific performance and the local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of urban park performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between building demand and tree supply to support local decisions about the best locations for new green infrastructure and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructure based on the demanded ecosystem service.

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures.

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.

Working of E-Ticketing for Public Buses

The transition from manual, cash-based ticketing procedures to a cutting-edge digital system represents a pivotal advancement in modernizing India's public bus transportation. This research paper delves into the working model of e-ticketing for public buses, aimed at addressing long-standing challenges of wait times, operational hurdles, and inefficiencies inherent in traditional systems. By leveraging state-of-the-art digital technology, this initiative aims to revolutionize the public transportation experience, enhancing security, convenience, and operational efficiency for both administrators and passengers. Thus, to provide an agile and smooth ticketing experience, we have proposed the smart application. [4] Key objectives of this research include the elimination of manual cash transactions, reduction of wait times, and mitigation of operational inefficiencies. Through the implementation of a digital ticketing system, the paper explores how transaction processes are streamlined, providing passengers with a seamless interface for ticket purchases. Not only does this innovation enhance transaction security, but it also fosters a more agile and responsive public bus transportation system. The research assesses the tangible benefits of the digital ticketing solution, highlighting its role in increasing passenger flexibility and reducing congestion through shorter wait times and improved traffic flow. Real-time data analytics are examined as a critical tool for administrators, enabling enhanced resource allocation and informed decision-making processes. This paper presents a comprehensive analysis of the working model of e-ticketing for public buses, offering insights into its functionality, implementation challenges, and the transformative impact it has on India's public transport sector. By promoting a more sophisticated and user-friendly framework, the research underscores the potential of digital ticketing solutions to redefine the landscape of public transportation, paving the way for a more efficient and accessible urban mobility system in India. Key Word: E-ticketing, Public buses, Digital technology, Operational efficiency, Urban mobility

Innovative Approaches to Urban Drainage System : A Review

Conventional urban drainage systems are often unable to cope with extreme rainfall and rapid urbanization, resulting in flooding and water pollution. Innovative approaches are needed to improve the performance and sustainability of urban drainage systems. This research aims to explore and analyze innovative approaches in urban drainage systems to improve efficiency, sustainability and resilience to climate change. This type of research is literature research. Data collection techniques are direct observation through journal databases and documentation. Data analysis in the research is qualitative data analysis. The results concluded a promising innovative approach to improve the performance and sustainability of urban drainage systems. Integrated application of this innovative approach can help create more resilient, sustainable and environmentally friendly urban drainage systems, contributing to healthier and climate change resilient cities. Further research is needed to evaluate the long-term effectiveness and scalability of this approach in various urban contexts.

The Role of Digital Management and Smart Technologies for Sports Education in a Dynamic Environment: Employment, Green Growth, and Tourism

ABSTRACT Digitalization management, sports education, tourism and smart urban technologies are crucial for urban energy systems and environmental sustainability. Environmental sustainability is a critical global challenge, and countries emphasize combating the rising environmental issues. Undoubtedly, the globe has proposed several initiatives to address rising ecological matters, but the problem remains intact. This study shows a new pattern, including problems and vital solutions with fresh insights. Despite countries launching initiatives to combat environmental degradation (ED), ecological concerns continue to persist. Empirical research has considered income, urbanization, digitization, sports employment, tourism, and green energy as core sustainability determinants in selected European economies (2010–2021). Applying a series of estimators, we determined that income, urbanization, sports, and tourism are the leading causes of ED, while digitization and green energy contribute significantly to sustainable development. Additionally, digitization plays a significant mediating role in sustaining urban energy systems. The study offers helpful implications for achieving desired sustainability levels.

Unlocking Economic Resilience: A New Methodological Approach and Empirical Examination under Digital Transformation

Economic resilience is crucial for urban sustainability as it ensures stability and growth in the face of external shocks, promotes social cohesion and inclusivity, fosters environmental sustainability, and enhances cities’ adaptability to future challenges. This study expands the conventional perspective on economic resilience beyond the context of shocks, focusing on the inherent resilience of regional economic systems. A novel method for quantifying economic resilience is introduced, emphasizing system sensitivity and adaptability. Using Chinese prefecture-level city data and an econometric model, we empirically examine how Fintech, a major digital transition in current urban systems, affects economic resilience. The findings reveal that Fintech has a substantial positive effect on economic resilience, primarily through the upgrading of industrial structures and technological innovation. Furthermore, there is significant regional heterogeneity in the impact of Fintech on economic resilience, with more pronounced contributions in the east, central, and western regions of China, as opposed to the northeast. Additionally, the impact of Fintech on economic resilience is more substantial in large-scale cities. The promotion of economic resilience through digital transformation serves as a potent risk prevention measure. Understanding the role of economic resilience in urban systems holds valuable implications for countries worldwide.

Toward governance, resilience, and economic outcomes in urban energy transition based on renewable energy allocation

The accelerating global urbanization demands a paradigm shift in urban energy systems towards sustainability. This paper presents an integrated approach using Ant Colony Optimization (ACO) to optimize the transition from conventional to renewable energy sources in urban areas. The study focuses on minimizing operational costs and emissions by strategically allocating resources between conventional units, hydrogen production, and renewable energy sources. Through extensive simulations and analysis, our approach demonstrates its effectiveness in achieving a balance between economic viability and environmental sustainability. We provide a detailed breakdown of operational costs and emissions, offering valuable insights for policymakers and urban planners. The results underscore the pivotal role of ACO in guiding decision-making processes, ensuring cities transition to cleaner, more efficient energy systems. This research not only contributes to academic discourse but also provides actionable solutions for building greener and economically viable urban energy infrastructures. The methodologies and insights presented here offer a valuable framework for shaping the future of urban energy landscapes in the face of growing environmental challenges.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Are cities genuinely healthy? Diagnosis of urban development from the perspective of adaptive capacity

As urban diseases continue to emerge with the acceleration of urbanization, the transformation of urban governance concepts and systems must be urgently promoted. In dealing with the complex systemic problem of city governance, the concept of adaptation in complex adaptative system (CAS) theory has shown obvious advantages. This approach provides a new perspective for solving this problem in the face of the unprecedented complexity of current disasters. However, research on urban adaptation is currently hindered by imprecise terminology, significant bias, and insufficient empirical evidence. This study reinterprets the concept of urban adaptation in conjunction with CAS theory. The formula for the functional expression of urban adaptation is derived using dissipative structure theory. Indicators are then selected to describe the system of urban adaptive capacity. An adaptive decision support framework is established that covers 29 quantitative and 8 qualitative indicators. The adaptive performance of 12 sample cities is analyzed. Finally, this study provides corresponding planning paths for optimizing urban adaptation based on the evaluation results. The research findings can assist city managers in comprehending the actual situation of cities and provide a basis for formulating targeted governance programs.

Coupling material and energy flow analysis with life cycle assessment to support circular strategies at the urban level

Abstract Purpose This study aims to investigate how the coupling of Material and Energy Flow Analysis (MEFA) with Life Cycle Assessment (LCA) under an urban metabolism (UM) perspective (referred to as the UM-LCA approach) can support the design, evaluation, and monitoring of urban-level circular strategies. For this purpose, we apply the UM-LCA approach to the urban area of Umeå (Sweden) by expanding the goal and scope of a recent MEFA study conducted by the authors of the article. Methods The modeling combines MEFA with LCA and is performed both retrospectively and prospectively. The MEFA was performed in the prior study following a bottom-up approach to map and quantify material and energy flows in the urban system at the sectoral level. The quantified flows are used in the present study to construct the life cycle inventory (LCI) model of the urban system. The LCI model is first used as a basis of the retrospective LCA (rLCA), which is conducted to assess the environmental performance of the urban system with its sectors. The LCI model is then modified according to future scenarios and is used as a basis of the prospective LCA (pLCA), which is performed to assess the environmental implications of implementing circular strategies in the future. Results and discussion The rLCA shows that the construction and household sectors are major drivers of environmental impacts in the urban system, with households being the largest contributors to 11 out of 12 analyzed impact categories. It also reveals the most impacting flows within these sectors, including food in households and steel in the construction sector. These findings indicate that the construction and household sectors and their most impacting flows should be prioritized in a circular strategy. Furthermore, the pLCA highlights that a future circular strategy promoting reductions in the material used in these two sectors could lead to higher reductions in all impact categories, ranging from 4.3 to 8.6%, than a strategy focused only on recycling, which could lead to reductions ranging from 0.2 to 1.2%. Conclusions This study concludes that the UM-LCA approach has great potential to comprehensively analyze, both retrospectively and prospectively, the environmental performance of an urban system providing valuable insights that could support policy makers in designing, assessing, and monitoring urban-level circular strategies. However, further research should address identified limitations of the UM-LCA approach (e.g., limited available data, inability to consider social and economic aspects) to facilitate its applicability and enhance its comprehensiveness.

Blockchain for urban governance: Enhancing trust in smart city systems with advanced techniques

The rapid urbanization and increasing energy demands necessitate innovative solutions for efficient energy governance in smart cities from the social science perspective. This paper introduces a comprehensive framework for Urban Energy Governance (UEG), focusing on the collaboration of three urban systems in energy exchange considering social and environmental impacts. To address the challenges of security, energy forecasting, and nonlinear optimization, we employ advanced techniques, including blockchain technology, Long Short-Term Memory (LSTM) networks for accurate energy forecasting, and a modified Particle Swarm Optimization (PSO) algorithm for solving the proposed nonlinear optimization problem. The utilization of blockchain enhances the security and transparency of energy transactions, fostering trust among stakeholders. Additionally, LSTM models leverage historical data to provide precise energy forecasts, optimizing resource utilization. The modified PSO algorithm tailors optimization to the specific nonlinearities of the proposed problem, ensuring efficient and effective decision-making in the context of Urban Energy Governance. Through a multi-faceted approach, our framework contributes to the advancement of smart city systems, providing a secure, forecast-driven, and optimized energy governance model for sustainable urban development.

Optimization of municipal solid waste collection system: systematic review with bibliometric literature analysis

AbstractMunicipal solid waste management (MSWM) requires significant planning and objective definitions of each of its stages. Waste collection and transportation stages are of utmost importance, and they represent a significant cost of the process. In this context, a systematic study using bibliometric analysis was conducted, seeking to identify and understand the applied methodologies and tools, as well as which parameters and approaches are employed to optimize the solid urban waste collection system. The analysis portfolio features 12 publications, focusing on the optimization of municipal solid waste (MSW) collection systems, mostly with the aim of optimizing routes for transporting waste from the collection points to the final destination, with the goal of reducing the costs of this stage. Results highlight how these studies only consider as optimization criteria the reduction of the travelled distance and lack other dimensions (such as an environmental impact perspective). Some of the studies demonstrate the vital role of technology in optimizing the waste collection operation, from the use of geographic information systems (GIS) to using sensors or smart bins. Future research efforts should widen the scope of what is considered in optimizing the waste collection system.

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Water-energy trajectories for urban water and wastewater reveal the impact of city strategies

The global water industry has a greater emphasis on energy management than ever before. The confluence of rising energy demand and costs, and net-zero greenhouse gas emission targets means the sector must rapidly transition to a new ‘energy future’. Yet, few cities have assessed the long-term energy use of their water and wastewater systems. Here, we undertake a novel and integrated assessment of the historical trends of energy use for water and wastewater in three Australian and two US cities, collectively 17 million people. The key research question is what were the historical trends of energy use for water supply and wastewater treatment, and what can we understand about the drivers? The research contributes a first systematic time-series assessment of energy trajectories of both water and wastewater, across multiple cities. Uniquely, it integrates long-term (up to 20 years) energy dynamics in a comparative analysis. The work also contributes a qualitative analysis of driving factors behind the observed energy variations. The time-series analysis (2001−2020) identifies how energy use is evolving through time in widely differing climate, urban and water infrastructure conditions. The cities studied demonstrated downward trends in water use by 30–42% and wastewater collected by 5–30%, primarily due to water conservation and drought-related restrictions. Annual per-capita energy use for water supply reduced in Los Angeles (−58%, from 276 to 116.5 kWh/p/a), San Diego (−59%, from 503.7 to 204.2 kWh/p/a), Sydney (−26%, from 40.6 to 30.1 kWh/p/a) and increased in Melbourne (+859%, from 15.7 to 150.6 kWh/p/a) and Perth (+139%, from 118.1 to 281.9 kWh/p/a). Compared to water supply, energy use for wastewater was far more stable (it varied between 45 and 85 kWh/p/a), and not the crucial contributor to overall energy use dynamics. The significant increase in seawater desalination is identified as the primary driver of increased energy use in the three Australian cities. To offset this huge demand, developing renewable energy generation emerged as the key strategy. It causes high fluctuation of renewable energy use shares (Sydney: 317 GWh, accounting for 48.5% energy use for water and wastewater in 2011; compared to 19 GWh, accounting for only 2.5% in 2008). In contrast, both Los Angeles and San Diego managed to considerably reduce energy use by decreasing their imported water volume and energy intensity (the result of an adjusted supply portfolio). However, the absence of consistently comprehensive water/energy/renewable energy data remains a significant hurdle for a thorough quantitative analysis of drivers. Given these observations, it is evident that detailed quantitative analysis for influencing factors (e.g. water use, climate, infrastructure upgrading, sustainability targets), requires separately reported energy use for both water supply and wastewater, reported water from categorized sources and significant other data. By addressing these issues, we can have a clear path towards an energy-efficient, sustainable urban water system with net-zero emissions.

Viral invasion, incubation, and outbreak under the normalized operation of urban systems: a spatial cognition-driven transmission model of infectious diseases

ABSTRACT Large-scale epidemics, such as COVID-19, pose significant threats to human health and social stability due to their rapid and covert transmission, emerging as one of the main challenges to maintaining a well-functioning urban system. During the pandemic, the spatial transmission mechanisms and their influencing factors of infectious diseases have received as a central topic. Scholars concentrated on researching the phase of extensive dissemination and mandatory intervention, utilizing multiple datasets. However, there remains a notable gap in the investigation of virus spatial transmission mechanisms under normalized urban operations due to limitations in detection timeliness and data collection issues. Moreover, compared to various intervention strategies, the influence of human behavioral dynamics and spatial cognition remains understudied. The paper simulates invasion, incubation, and outbreak of the Omicron variant in Zhuhai City in 2022, validating the results against real-world epidemic data. By comparing spatial diffusion patterns under three levels of spatial cognition, it sheds light on the delayed impact of human spatial cognitive abilities on urban pandemic outbreaks and their influence on virus invasion orders. The simulation model and revealed diffusion mechanisms will provide important guidance for the proactive prevention and control of unpredictable large-scale epidemics in future urban systems.

Resilience framework for urban water supply systems planning

ABSTRACT As the concept of resilience is becoming a criterion in planning, water utilities are seeking support and practical guidance to enhance their conventional risk-based planning processes. This paper presents a resilience framework for urban water supply systems planning during the transition towards integrated water resources management. Based on a synthesis of literature across engineering, ecological and social sciences, resilient system performance is defined using crossings of fail-safe and safe-fail thresholds as key indicators. System performance curves conceptually illustrate the capabilities withstanding, absorptive, restorative, adaptive, transformative, and anticipative (WARATA), during sudden and gradual disruptions. Sustainability goals are explicitly considered in the resilience framework, and the role of transformative and anticipative capabilities to facilitate transitions is discussed. Specifically, the desirability of physical design and predicted community consequences from performance impact and collapse can be used in resilience management to prioritize between fail-safe and safe-fail system capacities. Finally, key considerations for operationalizing the framework are summarized, including how issues related to social justice can be addressed when simulating performance and deriving metrics. While this paper focuses on urban water supply, the framework could be applied to other service-providing infrastructure where resilience-based planning supported by quantitative evidence is required to inform investments.

Socio-hydrological modelling using participatory System Dynamics modelling for enhancing urban flood resilience through Blue-Green Infrastructure

Cities are complex systems characterised by interdependencies among infrastructural, economic, social, ecological, and human elements. Urban surface water flooding poses a significant challenge due to climate change, population growth, and ageing infrastructure, often resulting in substantial economic losses and social disruption. Traditional hydrological modelling approaches for flood risk management, while providing invaluable support in the analysis of hydrological dynamics of floods, lacks an understanding of the complex interplay between hydrological and non-hydrological (i.e., social, environmental, economic) aspects in an urban system, hindering effective flood risk management strategies. In this context, socio-hydrological modelling methods offer a complementary perspective to traditional hydrological models by integrating hydrological and social processes, thereby enhancing the understanding of the complex interactions driving flood resilience.The present work proposes a participatory socio-hydrological modelling approach based on System Dynamics (SD) to quantitatively analyse the interactions and feedback between flood risk and different aspects of the urban system. By combining scientific expertise with stakeholder knowledge, the modelling approach aims to provide decision-makers with a comprehensive understanding of flood dynamics and the effectiveness of resilience-building measures. Furthermore, the role of Blue-Green Infrastructure (BGI) in enhancing urban flood resilience, considering its interplay with grey infrastructure and interactions with various sub-systems, is explored.The results reveal i) the contribution of SD quantitative modelling in supporting the analysis of interactions between flood risk reduction measures and different sub-systems thus offering decision-makers actionable insights into the multifaceted nature of flood risk and resilience; ii) the added value provided by the combination of scientific and stakeholder knowledge in tailoring the model to the case study, quantifying socio-hydrological modelling dynamics limitedly explored in the scientific literature and supporting the selection of measures for increasing flood resilience; iii) the ability of BGI to provide not only hydrological benefits (mainly about the reduction of surface runoff) but also multiple social and environmental benefits (i.e., the co-benefits), especially when coupled with well-functioning grey infrastructure. Reference is made to one of the case studies of the CUSSH and CAMELLIA projects, namely Thamesmead (London, United Kingdom), a formerly inhospitable marshland currently undergoing a process of urban regeneration, with an increasing vulnerability to flooding.

Train Rescheduling of Urban Rail Transit Under Bi-Direction Disruptions in Operation Section

As urban rail transit systems expand rapidly and passenger volumes grow, disruptions in operations increasingly occur, leading to adverse effects such as train delays and passenger retention. This paper introduces an organizational scheme to address train rescheduling in urban rail transit during bidirectional operational disruptions. It uses crossovers for short-turning services on either side of the disruption. Train rescheduling following a disruption is categorized into three stages: the disruption response phase, the disruption duration phase, and the recovery phase. This study presents a cooperative adjustment model for the network train timetable with the optimization objectives of reducing total passenger waiting time and the penalty time incurred from exceeding train capacity. The model considers the interrupted line and other lines connected to the interrupted line, employing a genetic algorithm for solution. Real operational data from a local subway line in a city are used as an example to adjust the train operation of urban rail transit with the bidirectional interruption. The results show that the short-turning strategy can better ensure the service level under the urban rail transit disruptions. Furthermore, the network train timetable collaborative adjustment model outperforms strategies that only modify the timetable of the disrupted line, effectively reducing both total passenger waiting time and penalty time for exceeding train capacity. This approach enhances operational efficiency and service levels in urban rail transit.