Urban System Research Articles

Determining the climate adaptive capacity of urban actors: a case study of Mongla port-townin Bangladesh

Purpose In an urban environment, different types of actors (individuals, households, community groups, institutions, governmental organizations, nongovernmental organizations (NGO), community-based organizations (CBO) and public and private organizations, etc.) play critical roles in enhancing urban resilience to adapt to the impacts of climate change. To identify and prioritize aspects of climate resilience planning, this study aims to examine the potential impacts of climate change on diverse urban systems and evaluate the capacities of various actors to adapt to climatic stress. Design/methodology/approach This research selects Mongla, a coastal as well as port town in south-western Bangladesh that faces climate risks including frequent cyclones, storm surges and salinity intrusion due to sea-level and temperature rise, as reported in the National Climate Vulnerability Index 2018. This research uses the methodology proposed by ICLEI South Asia’s Climate Resilient Cities Action Plan and accesses different urban actors’ adaptive capacity for contributing to enhancing climate resilience based on three broad characteristics: the capacity to organize and respond, availability of resources and access to information. The cumulative scores of these characteristics aid in determining the climate adaptive capacity of each urban actor. Findings The identified 53 actors are grouped into four categories: government (local and national); international/local NGOs, CBOs and associations; community representatives and private sectors and groups and individuals. The group of NGOs, CBOs and associations has a higher overall adaptive potential than all other actor groups, according to the study. When it comes to practicing adaptive capacity, government institutions are in the second-highest position. But, the Khulna development authority, the government agency that is in charge of overseeing all forms of urban development, scored poorly on all fragile urban systems. However, the performance of the fourth group of urban actors is very subpar when it comes to adapting to climatic stress, which emphasizes the need for focused interventions. Research limitations/implications Variations in adaptive capacities to climate change across different actors are particularly useful for targeting actors with lower levels of climate adaptive capacities in Mongla town. Originality/value This paper particularly addresses the research gap in assessing the adapting capacity of urban actors in Mongla port-town. Policymakers and practitioners can create and carry out targeted interventions that address the particular needs and concerns of vulnerable actors by making use of the insights obtained from this kind of study, thereby assisting in the development of climate resilience in any urban area.

GIS-based spatial approaches to refining urban catchment delineation that integrate stormwater network infrastructure

Rapid urbanization and escalating climate change impacts have heightened stormwater-related concerns (e.g., pluvial flooding) in cities. Understanding catchment dynamics and characteristics, including precise catchment mapping, is essential to accurate surface water monitoring and management. Traditionally, topography is the primary data set used to model surface water flow dynamics in undisturbed natural landscapes. However, urban systems also contain stormwater drainage infrastructure, which can alter catchment boundaries and runoff behavior. Acknowledging both natural and built environmental influences, this study introduces three GIS-based approaches to enhance urban catchment mapping: (1) Modifying DEM elevations at inlet locations; (2) Adjusting DEM elevations along pipeline paths; (3) Applying the QGRASS plug-in to systematically incorporate infrastructure data. Our evaluation using the geographical Friedman test (p > 0.05) and Dice Similarity Coefficient (DSC = 0.80) confirms the statistical and spatial consistency among the studying methods. Coupled with onsite flow direction validation, these results support the feasibility and reliability of integrating elements of nature and built infrastructure in urban catchment mapping. The refined mapping approaches explored in this study offer improved and more accurate and efficient urban drainage catchment zoning, beyond using elevation and topographic data alone. Likewise, these methods bolster predictive stormwater management at catchment scales, ultimately strengthening urban stormwater and flooding resilience.

Urban traffic-parking system dynamics model with macroscopic properties: a comparative study between Shanghai and Zurich

Analyzing the dynamics of parking traffic can better represent the real dynamic states of road networks, thereby allowing for a deeper analysis of the parking system’s impact. This paper comparatively investigates the impact of parking policies on two traffic networks with different infrastructure, socio-economic, and policy characteristics. Parking space, average parking duration, and parking fee policies were analyzed as a function of cruising distances and cruising time with indirect effects on traffic emissions. Empirically, the system dynamics model application is tested and validated with the macroscopic data from two central business districts (CBDs) in Shanghai (Xujiahui area) and Zurich (Bahnhofstrasse area). Results showed Bahnhofstrasse CBD is more sensitive against the policy shifts with relatively higher elasticity and indicated greater responsiveness in aggregating traffic emissions when compared with Xujiahui CBD. The findings of this study may provide an overall framework to empirically assess the performance of different traffic conditions and strategies on urban parking systems.

Trihalomethanes monitoring and their seasonal variation in urban municipal water supply system in North India

Trihalomethanes monitoring and their seasonal variation in urban municipal water supply system in North India

Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.

Scenario-Based Green Infrastructure Installations for Building Urban Stormwater Resilience—A Case Study of Fengxi New City, China

Global climate change has precipitated a surge in urban flooding challenges, prompting the imperative role of green infrastructure (GI) as the linchpin of sponge city construction to enhance urban sustainability and resilience. But the evaluation of urban stormwater resilience faces challenges due to the lack of a comprehensive evaluation framework taking the intrinsic features of the resilience system into account and the insufficient coverage of alternative scenarios’ performance under multiple rainfall return periods. This study, focusing on Fengxi New City, China, evaluates the suitability of GI (i.e., green roofs, rain gardens, and permeable pavements) and constructs a stormwater management model (SWMM) for urban stormwater hydrological simulation. This study also establishes a comprehensive urban stormwater resilience evaluation system and uses quantitative methods to unify the performances of scenarios under different rainfall return periods. Our analytical findings elucidate that the suitability of GI is predominantly concentrated in the northern and western areas of the study area, with the smallest suitable area observed for permeable pavements. Divergent GIs exhibit disparate performances, with rain gardens emerging as particularly efficacious. Importantly, the combination of multiple GIs yields a synergistic enhancement in resilience, underscoring the strategic advantage of adopting a diverse and integrated approach to GI implementation. This study facilitates a deeper understanding of urban stormwater resilience and assists in informed planning decisions for GI and sponge cities.

Challenges and Solutions for Ride-Hailing Motorbike Drivers in Vietnam: An Analysis of Working Conditions and Health

In the digital age, ride-hailing motorbike services have become an indispensable part of the urban transportation system in Vietnam, attracting thousands of workers with their flexibility and income potential. However, occupation also poses numerous health and safety challenges. The job requires long working hours, exposure to harsh weather conditions, and highly competitive pressure, leading to physical and mental health issues such as muscle pain, fatigue, and stress. This research article employs a survey method to collect data from ride-hailing motorbike drivers to analyze the labor burdens they endure. Based on the findings, the study suggests solutions such as improving working conditions, developing health support programs, and implementing more flexible policies to enhance the quality of life and job satisfaction among workers, thereby contributing to the sustainable development of the industry in Vietnam.

Evaluating nickel removal efficacy of Filtralite under laboratory conditions: Implications for sustainable urban drainage systems

Our study deals with the critical issue of heavy metal pollution in urban runoff; we focused particularly on the threat posed by heavy metals such as nickel to living organisms and water resources owing to their toxicity at low concentrations and non-biodegradability. In this study, we evaluated the efficacy of Filtralite, a filtration medium with excellent hydraulic properties, in removing nickel from water through laboratory experiments and advanced simulation tools. We analysed the geochemical processes involved in nickel removal, including precipitation and adsorption. Importantly, we implemented an innovative approach for numerical modelling using HP1, by combining HYDRUS-1D and PHREEQC for modelling solute transport in porous media and geochemical reactions, respectively. The experimental results revealed that the Filtralite-laden column achieved a removal efficiency of 93.5 % for injected nickel. We observed a significant increase in the effluent pH from 7.0 to 11.5 during the interaction with Filtralite, affecting the solubility of the nickel phases. The model showed the highest nickel concentration in the initial column layers because of rapid chemical precipitation and adsorption upon contact with Filtralite. High-resolution scanning electron microscopy images confirmed the presence of amorphous precipitates around the soil particles, aligning with the changes in pH. This study contributes significantly to environmental engineering and urban water management by introducing an innovative numerical model in HP1 to accurately simulate the removal of nickel using Filtralite. These findings validate the efficiency of Filtralite in extracting nickel from aqueous solutions. Thus, Filtralite is a strong candidate for inclusion in sustainable urban drainage systems.

Resisting the heat wave: Revealing inequalities in matching between heat exposure risk and healthcare services in a megacity

Climate warming and the growing urban heat island effect are exacerbating the unequal public health crisis in megacities, and thus it is important to assess the equity of matching relationship between the healthcare services and the heat exposure risk. We selected 255 census units in Tianjin city for the study, and calculated a heat exposure index based on the universal thermal climate index (UTCI) and constructed a comprehensive urban healthcare services evaluation system to measure the degree of mismatch between the two. The results showed the following. (1) The heat exposure risk and healthcare services followed an increasing pattern from the Outer suburb area (OSA) to the Metropolitan development area (MDA) and then to the Main urban area (MUA). (2) The overall imbalance in matching was low in the OSA, whereas the healthcare services were relatively insufficient in the MDA, and the heat exposure risk and healthcare services were relatively balanced in the MUA. (3) The degree of matching between the core and peripheral parts of the MUA was severely polarized, with an overall pattern of excess healthcare in the core area and a high risk of heat exposure in the peripheral areas. The results can provide effective guidance for rationalizing the allocation of healthcare resources from a heat exposure perspective, and provide a policy basis for risk management and fund investment.

Participatory mapping in exploratory scenario planning: Necessity or luxury?

Exploratory scenarios based on participatory approaches proved helpful in dealing with uncertainty and complexity in urban and transport systems. Such scenarios usually create coherent and manageable narratives that capture broad patterns and trends at global scales. However, significant difficulties exist in bridging exploratory scenario narratives with spatial and local realities, which is crucial for guiding policymaking in urban, regional, and mobility planning. This paper explores whether and how exploratory scenario narratives and participatory mapping processes can establish a reciprocal relationship, wherein narrative-based maps contribute to spatially shaping scenario narratives. It uses an experimental approach involving three participatory workshops with experts and local stakeholders specifically tailored to the mapping of land use and transportation-related scenario narratives for the year 2050 in the Henares Corridor (Madrid, Spain). The spatial transformations mapped during workshops have indicated that participatory mapping processes have an impact on scenario narratives in different ways, enabling the identification of three categories of mapping episodes with different meanings: (i) complementing narratives, (ii) expanding narratives, and (iii) modifying narratives. The paper closes by examining the implications of the obtained findings for policymaking, including a reflection on the research’s limitations and potential for application in real-world planning processes.

Establishment of urban green corridor network based on neural network and landscape ecological security

The planning and construction of urban ecological corridors play a role in restoring and improving the ecological environment of cities, promoting the movement of other species and biological factors living in urban environments and wider regions. It also profoundly influences and enriches the spiritual and cultural experiences of people living in cities and nearby spaces. The reason for the emphasis on green corridors is that many cities and towns are constantly expanding, and the urban green space system has not yet formed an effective network. At the same time, factors such as the loss of the urban natural environment, biodiversity reduction, and environmental degradation have led to the need to build urban green corridors to deal with risks. By improving the neural network model, this paper predicted the construction land scale of the urban green corridor network, which was used to adjust the land use structure of the green corridor and optimize the land use layout. This paper aims to use the upgraded neural network method to predict the scale of urban green corridor network building land, which helps to evaluate the ecological security status. It can solve the dynamic solution problem of multi-indicator variable weight problems, overcoming the influence of subjective factors in the weight-setting process. The experiment adopted the improved neural network model for prediction. The results showed that its accuracy was much higher than the gray prediction model, which has improved by about 14.81%. This paper fully proved that the improved neural network model had a high degree of fit and feasibility for predicting the land scale of urban green corridor networks. It is directly related to the rationality and practicability of the urban green corridor network planning scheme, which plays a role in guaranteeing the ecological security of the landscape.

An attempt to augment performance of machine learning models in a pilot-scale urban wastewater treatment system

The challenge we face is striking a balance between ensuring the standards for wastewater treatment processes and minimizing energy consumption. As such, accurate predictive models become critical. With machine learning algorithms having the ability to discern relationships between inputs and outputs, broad interest has been shown in their applications in wastewater treatment. A key research focus is on effectively adjusting these generic machine learning algorithms to adapt to frequent fluctuations of water quality, inflow volume, and environmental variations. This study developed a Nonlinear Autoregressive neural network (NARX) model having an adaptive real-time updating module to predict the ammonium concentration in the treated effluent. The model has exhibited high predictive accuracy on the testing data, achieving an R2 value of 0.997. By incorporating an adaptive real-time updating module, the model's capability to predict effluent ammonium concentration on novel data sets has been significantly improved, with the R2 value for these predictions increasing from an initial 0.918 to a more refined range of 0.966 to 0.984. Our findings confirm that integrating real-time updating capabilities with machine learning algorithms substantially improves the stability and predictive performance of the models. These outcomes will prompt a broader integration of artificial intelligence in the domain of environmental engineering, which shall foster the implementation of more accurate and eco-friendly wastewater treatment strategies.

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

Precondition Study of a Sponge City: Comprehensive Assessment of the Vulnerability of an Urban Rainwater System

This study introduces the concept of urban rainwater system vulnerability and identifies the indicator factors that affect the vulnerability of rainwater systems. Using the analytic hierarchy process (AHP), an index system for the vulnerability assessment of the rainwater system was established, and a vulnerability assessment model for the rainwater system was constructed. By integrating vulnerability degree, recurrence period, and water depth of ponding, a vulnerability assessment framework for urban rainwater systems was developed. Taking a newly developed urban area in the Suzhou High-Tech Zone as an example, we calculated the vulnerability degree of the urban rainwater system in this area to be 0.6497, indicating a high level of vulnerability and poor system safety. When encountering rainfall with a recurrence period p > 5 years, the city is likely to experience severe waterlogging. Through the analysis and evaluation of the rainwater system’s vulnerability, while clarifying the current state of the rainwater system, it can provide a scientific reference basis for the system’s upgrade, transformation, and optimized operation and management. Although the selection of factors may not be entirely comprehensive, this method allows for adjustments based on the composition and operation of different rainwater systems.

Analysis and development of criteria for the operational efficiency of urban electric buses

Problem. Currently, manufacturers and research specialists primarily utilize the amount of autonomous mileage and/or the specific electricity consumption of autonomous DEEs per unit of mileage to describe the general characteristics of urban electric buses. However, the structural mass and the key operational parameter, the nominal passenger capacity, are often disregarded. Therefore, comparing electric buses of the same type with identical dimensional parameters based solely on these criteria is illogical and incorrect. Goal. This study aims to establish criteria for the operational efficiency of urban low-floor electric buses of various types, considering autonomous DEE and their charging or replacement systems. These criteria should account for structural mass and nominal passenger capacity, facilitating a proper comparative assessment and the selection of the most optimal options for use in urban passenger transportation systems. Methodology. Criteria for the operational efficiency of city electric buses of various types are proposed based on an analysis of electricity consumption required for both movement and the operation of heating, ventilation, and air conditioning systems. The criteria consider the mass in the equipped state and the main parameter - nominal passenger capacity. Results. Several operational efficiency criteria for urban electric buses, incorporating mass parameters and passenger capacity, are suggested. The feasibility of their application is demonstrated through a comparison of five sketch projects of electric buses of various types with modern models. Originality. The proposed criteria for the operational efficiency of urban electric buses offer a method for adequately comparing any selected models, regardless of their types and technical specifications. Practical value. The proposed criteria for the operational efficiency of urban electric buses of various types can be beneficial for transport company specialists in selecting suitable rolling stock for specific routes. Additionally, design bureau and scientific and technical center specialists can utilize these criteria during the design process of new urban electric transport models.

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