Model Of Urban System Research Articles

CHUWD-H v1.0: a comprehensive historical hourly weather database for U.S. urban energy system modeling

Reliable and continuous meteorological data are crucial for modeling the responses of energy systems and their components to weather and climate conditions, particularly in densely populated urban areas. However, existing long-term datasets often suffer from spatial and temporal gaps and inconsistencies, posing great challenges for detailed urban energy system modeling and cross-city comparison under realistic weather conditions. Here we introduce the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0, a 23-year (1998–2020) gap-free and quality-controlled hourly weather dataset covering 550 weather station locations across all urban areas in the contiguous United States. CHUWD-H v1.0 synthesizes hourly weather observations from stations with outputs from a physics-based solar radiation model and a reanalysis dataset through a multi-step gap filling approach. A 10-fold Monte Carlo cross-validation suggests that the accuracy of this gap filling approach surpasses that of conventional gap filling methods. Designed primarily for urban energy system modeling, CHUWD-H v1.0 should also support historical urban meteorological and climate studies, including the validation and evaluation of urban climate modeling.

Assessing the role of sustainable water bodies in urban drainage systems to mitigate urban flooding: A case study of Gurgaon, Haryana, India

For the last 5 years, Gurgaon a city in India has been facing an issue of urban flooding due to illicit encroachments over the local waterbodies, poor drainage system and increasing rainfall. In this study, Remote sensing data are employed to find the most flooded areas identified using Partial Least Square Regression and 18 new retention ponds are proposed to build a Sustainable Drainage System (SuDS) in open space and barren lands. In SWMM, the Urban Drainage System (UDS) model is simulated using 24-h rainfall hyetograph from hourly PERSIANN-CSS rainfall data (yearly rainfall events) and 7-h rainfall hyetograph from half-hourly IMERG Global Precipitation Data (extreme rainfall events) from 2000 to 2023. After comparing both UDS and SuDS in SWMM, it is found that the flood volume has decreased significantly from 240 CMS to 180 CMS (for yearly rainfall) and 500 CMS to 350 CMS (7-h rainfall hyetograph). The study also compares the structural resilience of the drainage system under the conditions of no link failure and single link failure scenarios. In no failure situation, 20% more resilience has been achieved for yearly rainfall and 10% more for extreme rainfall events. In single link failure conditions, SuDS is helping to reach 20–47% resilience for yearly rainfall events and 7–30% resilience for extreme rainfall events. Thus, this study helps to achieve SDGs 11 and 13 to build a resilient and climate-adaptive urban drainage in Gurgaon. The study gives significant insights regarding the competency of urban waterbodies to city planners and policymakers.

Mathematical Modeling and Indirect Carbon Emission Reduction Analysis of Urban Wastewater Treatment Systems Under Different Temperature Conditions

In the context of achieving the two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as a case study to accurately calculate indirect emissions related to energy and chemical consumption within the energy-intensive wastewater treatment industry. Sumo software was employed for precise mathematical modeling. Considering the operational characteristics of wastewater treatment plants in cold regions, this study innovatively divided the annual operation cycle into two periods, namely normal temperature and low temperature, and determined the optimal operational parameters under a low-carbon mode. The results indicate that precise regulation of dissolved oxygen concentration to 0.5–1.5 mg/L (normal temperature period) and 1–2 mg/L (low temperature period) can significantly reduce carbon emissions related to electricity consumption by 13,781.9 t CO2-eq. From the perspective of chemical consumption, adjusting the dosage of polyaluminum chloride (PAC) to 75% and sodium acetate to 70% during the normal temperature period can lead to a reduction in indirect carbon emissions of 1614.4 t CO2-eq compared to the same period last year. During the low-temperature period, by reducing the dosage of polyaluminum chloride to 80% and sodium acetate to 75%, the indirect carbon emissions can be reduced by 1557.3 t CO2-eq compared to the corresponding period last year. After optimization, USD 1.49 million can be saved. This study simulated the operation conditions of cold-region urban wastewater treatment plants at different times to effectively control carbon emissions resulting from energy and chemical consumption in wastewater treatment. This result can provide innovative ideas for energy saving and carbon reduction in cold-region wastewater treatment plants.

An Integrated SIMUS–Game Theory Approach for Sustainable Decision Making—An Application for Route and Transport Operator Selection

The choice of management strategy for companies operating in different sectors of the economy is of great importance for their sustainable development. In many cases, companies are in competition within the scope of the same activities, meaning that the profit of one company is at the expense of the other. The choice of strategies for each of the firms in this case can be optimized using game theory for a non-cooperative game case where the two players have antagonistic interests. The aim of this research is to develop a methodology which, in non-cooperative games, accounts for the benefits of different criteria for each of the strategies of the two participants. In this research a new integrated sequential interactive model for urban systems (SIMUS)–game theory technique for decision making in the case of non-cooperative games is proposed. The methodology includes three steps. The first step consists of a determination of the strategies of both players and the selection of criteria for their assessment. In the second step the SIMUS method for multi-criteria analysis is applied to identify the benefits of the strategies for both players according to the criteria. The model formation in game theory is drawn up in the third step. The payoff matrix of the game is formed based on the benefits obtained from the SIMUS method. The strategies of both players are solved by dual linear programming. Finally, to verify the results of the new approach we apply four criteria to make a decision—Laplace’s criterion, the minimax and maximin criteria, Savage’s criterion and Hurwitz’s criterion. The new integrated SIMUS–game theory approach is applied to a real example in the transport sector. The Bulgarian transport network is investigated regarding route and transport type selection for a carriage of containers between a starting point, Sofia, and a destination, Varna, in the case of competition between railway and road operators. Two strategies for a railway operator and three strategies for a road operator are examined. The benefits of the strategies for both operators are determined using the SIMUS method, based on seven criteria representing environmental, technological, infrastructural, economic, security and safety factors. The optimal strategies for both operators are determined using the game model and dual linear programming. It is discovered that the railway operator will apply their first strategy and that the road operator will also apply their first strategy. Both players will obtain a profit if they implement their optimal strategies. The new integrated SIMUS–game theory approach can be used in different areas of research, when the strategies for both players in non-cooperatives games need to be established.

Integration of Circular Economy and Urban Metabolism for a Resilient Waste-Based Sustainable Urban Environment

An unsustainable rate of resource production and consumption is evident in urban environments. The absence of innovative approaches in conjunction with the exponential urbanization and expansion of the global population will inevitably result in substantial environmental consequences. There are two emerging alternatives: circular economy (CE) and urban metabolism (UM). The integration of these principles into novel methodology casts doubt on the linear model of contemporary economic and urban systems, which includes extraction, production, utilization, and disposal. In the development of a distinctive urban framework known as circular urban metabolism, the current study has illustrated the application of these principles. We design this study to motivate urban planners and decision-makers to investigate, develop, and supervise ecologically sustainable cities. Scholars from a variety of academic disciplines, intrigued by the intricacies of urban planning, design, and administration, can foster interdisciplinary collaboration in the circular urban metabolism (CUM) region. To address the research question, we implemented a bibliometric analysis, which involved the examination of 627 pertinent research papers, utilizing the R (R 3.6.0+) statistical programming language. The results emphasize the fundamental characteristics and significance of CUM in the management of refuse. In addition, the findings underscore the importance of creating a novel framework that incorporates the principles of urban political ecology, CUM, sustainability, and the novel dimension of waste metabolism. It is the goal of this framework to emphasize the significance of recycling in the informal sector as a waste management strategy in low- and medium-income countries (LMICs).

Machine learning-based rainfall forecasting in real-time optimal operation of urban drainage systems

This paper presents a rainfall forecast-based real-time optimal operation model of urban drainage systems (UDSs) investigating the role of rainfall forecasts in predictive real-time operation of UDSs. Two rainfall forecast models, namely long short-term memory (LSTM) and optimized-by-PSO (particle swarm optimization) support vector regression, are linked to an integrated simulation–optimization model. For any given operation policies, the rainfall-runoff stormwater management model (SWMM) simulates the urban system’s operation under flooding conditions in which the operation policies are optimized by the metaheuristic harmony search (HS) optimizer. The objective function of the HS algorithm is minimizing flood volumes at control points. The presented approach is therefore a dynamic predictive real-time operation (PRTOP) model that continuously updates rainfall forecasts and operational decisions during flood events. The performance of the developed forecast-based integrated SWMM-HS simulation–optimization modeling framework is tested through its application in a real urban system case study located in Tehran, Iran. Results demonstrate that a notable reduction in flood volume (11.8%) is achieved by using the LSTM-driven rainfall forecasts in the proposed PRTOP model compared to a reactive real-time operation (RTOP) model not utilizing rainfall forecasts.

Urban community security system model from the perspective of resilience: a study on Chinese cities

To enhance urban governance and bolster community disaster resilience, it is urgent to innovate and develop an urban community security system to cope with the complex and volatile social security situation. From the perspective of resilience, the concept, layout, and structure of the security system are organized. Considering the current situation and development trend of the social security system, and based on the constructed cost-capability model, the cost and benefit of the two modes are analyzed and compared. Subsequently, from the perspective of multi-agent participation, a game model between governments and enterprises is constructed. The evolutionary stability strategies of enterprises are analyzed, and simulation analysis is conducted on the factors influencing enterprises. Finally, to propose targeted improvement measures based on the community security capabilities of different communities, a grey fuzzy comprehensive evaluation method is employed. The community security comprehensive capabilities of the top 10 cities in China are quantitatively analyzed and evaluated. The results indicate that the urban community security system from a resilience perspective is more comprehensive compared to the traditional security system. Adopting mode 2 is relatively effective to improving government emergency management capability. In a certain range, reducing C, increasing F, reducing R2, and increasing h can achieve the effect of promoting enterprise to participate in construction. The urban community security capability is within the upper-medium level in general, close to the level of good. The urban community security system constructed in this paper can provide a theoretical basis for the government to formulate policies of the urban community security system and serve as a reference for strengthening the comprehensive capability of the urban community security.

Re-ISSUES—Renewable Energy-Linked Interoperable Smart and Sustainable Urban Environmental Systems

Smart cities will be smart if they improve their citizens’ quality of life; to do so, it is essential to listen to citizens and collaborate with service and technological companies. For that, digitalization seems essential. Environmental management systems are complex and expensive. If their lifecycle costs are reduced, these systems would be more sustainable. This can be achieved through citizen collaboration (CS), the use of low-cost Internet of Things (IoT) devices, and collaboration with local renewable energy businesses. All this leads to a real interoperability challenge. Systems engineering offers a valid framework for managing information and knowledge for environmental systems. It offers a range of guides for processes that can improve the quality of the related information and the reusability of knowledge throughout the lifecycles of these systems. After quantifying the opportunity and the cost for a motivational case of atmospheric neighborhood odor impact and introducing trends and opportunities in energy management, the authors propose a model for renewable energy-linked interoperable smart and sustainable urban environmental systems (re-ISSUES). The model’s ontology is used to discover research trends and potential for improvements to the model itself, enabling semantic interoperability and knowledge reuse.

Extraction of the essential elements for urban systems modelling – A word-to-vector approach

Due to its ever-evolving nature, urbanisation continues to escalate in complexity, further exacerbating the urban sustainability challenges. This necessitates the need for evidence-based policymaking enabled by modelling approaches, to facilitate informed decisions, and propel and gravitate towards urban sustainability. The major constraint is that of identifying the essential characteristics for consideration when modelling cities as complex systems, in a structured manner that integrates these characteristics, cognisant of their relative importance. The distinctive urban systems, corresponding system characteristics and interdependencies impacting the modelling of cities as complex systems, can be identified from peer-reviewed literature. The limiting constraint is, although there is widely available information on cities in research databases, the ability to use this literature for a quantitative model has not been proven, presenting a research gap. This approach results in significant complexities. In order to resolve these complexities, this study seeks a systems-based approach including a 2-tier structured protocol, leveraging qualitative-to-quantitative techniques to automatically extract the key systems which impact the development of city models. Through a systematic literature review, data on 13 key systems is qualitatively extracted from research databases such as Scopus and ScienceDirect, for the duration 2014 – 2024. Through word2vector analysis, machine learning techniques are utilised to perform the quantitative mapping of each urban system into corresponding system characteristics, and quantitatively illustrate them based on relative importance. The results illustrate that this proposed method is significant to characterize the essential systems that constitute a city as a complex system, based on machine learning and text analytics.

Towards an Urban Planning Scenario Model System—A Tool for Exploring Urban Uncertainty: A Case Study of Diaozhen, China

The ‘Urban Interaction’ project aims to develop an urban planning model system at the scale of towns or small cities consisting of three modules: growth forecast, land-use decision, and evaluation. This paper presents the framework of the model system to identify and discuss the assumptions and theoretical basis of the model system. The model system will be driven by scenario planning theory and sustainable urban development principles. It will export land-use planning based on selected urban development scenarios and urban planning theories. This paper takes Diao Town in Jinan as an example. Applying GIS spatial analysis and hierarchical analysis, this paper determines the suitability of land use and the weights of different influencing factors, combined with the land-use conflict identification model, for land-use decision-making. Finally, the assessment module verifies whether the planning scheme complies with laws and regulations to achieve an active, reactive response to uncertainty. The paper discusses the ‘uncertainty’ of urban planning and proposes a creative, flexible, and timely planning platform that allows planners and other participants to model and visualize their scenarios.

Adaptive control of transportation infrastructure in an urban environment using a real-coded genetic algorithm

The management of urban areas requires the development of an effective strategy for the evolution of transportation infrastructure to ensure the smooth flow of traffic and pedestrians. A crucial component of this infrastructure is the traffic light system, which plays a vital role in traffic control and traffic safety. Improving the efficiency of traffic control systems in intelligent transportation systems (ITS) has a significant impact on a city’s economy. As a result, the cost of fuel for road users can be reduced, and their level of social comfort can be improved, among other benefits. This paper proposes a novel approach to optimizing traffic flows in smart cities, based on the combined use of the genetic optimization algorithm and the ITS simulation model we developed. The proposed method aims to enhance the efficiency of existing traffic control systems and achieve optimal traffic flow patterns, thereby contributing to a more sustainable and efficient urban environment. The optimization algorithm shown here aggregates the objective functions using a simulation model of a real region of the Moscow road network. The model includes intersections, pedestrian crossings and other features that are implemented in the AnyLogic system. The research aims to create a decision-support system for managing urban transport infrastructure. This system will be used to optimize the duration of traffic light phases in order to minimize the time vehicles spend passing through key nodes in the urban road network. It will also optimize pedestrian flow, reducing the impact of traffic on the environment and improving fuel efficiency. By applying this approach, the capacity of the street network can be significantly increased. Additionally, the negative effects of traffic flow on the environment can be reduced by optimizing fuel use and reducing waiting times at intersections managed by traffic lights. The research methodology involves the development of a hybrid evolutionary search algorithm, the creation of a simulation model for transportation and pedestrian flows in the AnyLogic and a series of optimization experiments that demonstrate the effectiveness of the proposed approach when applied to the modeling of complex urban transportation systems.

A post-disaster load supply restoration model for urban integrated energy systems based on multi-energy coordination

Urban integrated energy systems (UIES) have emerged as a promising solution to address the challenges of urban energy supply and consumption. However, UIES are vulnerable to extreme disasters, such as earthquakes, hurricanes, or floods, which can disrupt the energy infrastructure and lead to power outages and energy shortages. This study introduces a novel approach aimed at enhancing the resilience and load recovery capabilities of UIES in the face of extreme events. The proposed approach encompasses several key innovations, including the comprehensive coordination of local energy sources, prioritized restoration of critical loads, and a node-based modeling approach for gas and heat networks. Firstly, by fully integrating electricity, gas, and heat sources, the overall resilience of UIES is significantly improved, ensuring higher levels of load recovery even in the event of energy supply shortages. Secondly, the critical load recovery is prioritized by load classification, and the weights set can fully consider the connection status and load level of nodes to ensure priority provisioning of important node loads. In addition, the node-based modeling approach allows for accurate consideration of the flow of gas and heat in the pipe during modeling of the pipe energy storage. Finally, the case study section conducts simulation experiments with the UIES E33-G20-H6 test system to verify the effectiveness of the proposed approach and to demonstrate its potential in enhancing the post-disaster load recovery capability of UIES.

Assessing future urban flood hazard: A comprehensive approach to estimating the implications of future rainfall scenarios

AbstractEstimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures.

Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model

Urbanization and climate change are two major challenges of the 21st century, and the effects of climate change, combined with the urbanization of coastal areas, increase the frequency of coastal flooding and the area exposed to it, resulting in increased risk of flooding and larger numbers of people and properties being vulnerable. An urban growth modeling system was used to simulate future growth scenarios along the coast of the Vendée region in western France, and the potential exposure to flooding with each scenario was evaluated. The model used was an Artificial Neural Network combined with a Markov Chain, using data obtained by the remote sensing and geographic information system techniques to predict three future urban growth scenarios: business as usual, environmental protection, and strategic urban planning. High-risk flood areas and future sea level projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change were then used to assess future flood risk under each growth scenario in the study area. According to the results, the different growth scenarios are associated with different development patterns, and the strategic urban planning scenario significantly reduces the risk of flooding compared to the other two scenarios. However, the rise in sea level considerably expands the areas vulnerable to flooding. Finally, the methodology adopted can be used to prepare for the impact of climate change and develop strategies to mitigate the risk of flooding in the future.

Capability of the building-resolving PALM model system to capture micrometeorological characteristics of an urban environment in Vienna, Austria

Understanding and evaluating possible changes in thermal conditions of urban settlements are crucial for risk assessment, adaptation to climate change and sustainable urban development. This study presents the results of a micro-scale measurement campaign conducted in Vienna, Austria, to investigate the effects of natural and artificial surfaces on temperature and humidity variability. The observational data set is used for the evaluation of the newly developed building-resolving urban climate model system PALM. Dragino LHT65 LoRaWAN air temperature and humidity sensors were installed to monitor local-scale variations. On selected summer days, drones equipped with thermal imaging cameras were used to provide hourly surface temperature. The monitoring data show higher temperatures near buildings and overall strong spatial and temporal variations. The model demonstrates the capability to simulate the main thermal characteristics of the study area, although it slightly overestimates temperatures at night. Compared to the measurements, it has a less pronounced spatial variability in air temperature and relative humidity, but a larger one in surface temperature. The analysis confirmed that the urban climate model has the potential to appropriately assess microclimate conditions and therefore, contribute to future-oriented urban planning.

A spatially explicit framework for assessing ecosystem service supply risk under multiple land‐use scenarios in the Xi'an Metropolitan Area of China

AbstractRapid global urbanization has perturbed ecosystem structures and functions, resulting in ecological risk and threatening sustainable human well‐being and socioeconomic development. However, scientific indicators to analyze ecosystem service (ES) risk patterns need to be explored in detail. In addition, studies on ES supply risk are stagnating on historical or status explorations, especially from the view of disturbance from land‐use changes. This study seeks to develop a framework for modeling past‐future ES supply risk pattern evaluation and probing into ES risk patterns under different future land‐use scenarios. To achieve this objective, the framework integrates the Future Land Use Simulation (FLUS) model, the Intelligent Urban Ecosystem Management System (IUEMS) model, and an established indicator system incorporating ES supply trend, hotspots and coldspots, and ES trade‐offs, and synergies. The results show that: (1) In 2050, the supply of climate regulation in the Xi'an Metropolitan Area (XMA) will increase, while that of carbon sequestration and recreation will decrease. In 2050, the supply of climate regulation is the highest under ecological protection (EP) scenario, while the supply of carbon sequestration and recreation are the highest under cropland protection (CP) scenario. (2) From 2000 to 2050, the hotspots and coldspots of climate regulation increase in both natural development (ND) scenario and CP scenario. Notably, CP scenario experiences the most significant reduction in extremely significant hotspots and coldspots of carbon sequestration. From 2000 to 2050, at the regional and pixel scales, climate regulation and carbon sequestration mainly show trade‐offs, and carbon sequestration and recreation show synergies. (3) ES supply risk in XMA is high in the center and low in the north and south. The ES supply risk from 2000 to 2050 is increasing, with expanding “extremely high risk”, “high risk”, and “extremely safe” areas. ES supply risk management should adhere to more strict land‐use policies and guidelines, management zoning for areas with different levels of ES risk, and an accurate understanding of ES trade‐offs and synergies for scientific risk management. This study could provide theoretical and technical references for ES risk assessment research and promote scientific ecological risk management.

A Data-Driven Design Approach of an Energy Data Exchange Platform

The current digitalization of energy systems, particularly in urban contexts, lags behind the requirements set by urban energy system modelling, energy management, the development of digital twins, and other data-driven services. Additionally, existing energy data lacks standardized formats and coherence, hindering its full utilization. To address this challenge, this paper introduces a use case-driven design approach tailored to reflect the requirements for data integration and privacy set by city stakeholders. The objective is to design a data exchange platform (DEP) capable of serving a diverse range of use cases. This platform will facilitate the integrated utilization of energy data in energy management systems, digital twin applications, and other activities aimed at extracting valuable insights. These efforts are specifically designed to support decision-making processes of public stakeholders, private investors, and service providers. Consequently, the platform holds potential to attract additional investments in both the clean energy transition and the digitalization of energy infrastructure.

Stackelberg Game-based Coordinative Dispatch of EV Charging Stations and Urban Distribution Systems Considering Temporal-spatial Dynamic Traffic Flow

This paper proposes a Stackelberg Game-based coordinative dispatch model of electric vehicle charging stations (EVCSs) and urban distribution systems (UDS) considering temporal-spatial dynamic traffic flow. The model is formulated hierarchically and consists of an upper-level problem and two lower-level problems. Firstly, a detailed EVCS dispatching model considering both electricity prices and charging service prices is developed in the upper-level. Then the electricity pricing model of UDS and travel decision model of EV users are proposed in the lower model. Particularly, a temporal-spatial dynamic model is developed to capture the traffic flow evolution in urban transport network preciously. EVCS, UDS operator (DSO) and EV users can make decisions independently as different stakeholders. Finally, a multi-layer iterative framework is designed to capture the tripartite benefit equilibrium of distribution grid, EV charging stations, and EV users, and to realize the effective solution of the model. Numerical studies based on a modified IEEE 33-bus system demonstrate the effectiveness and accuracy of our proposed approach.

A Single Data Extraction Algorithm for Oblique Photographic Data Based on the U-Net

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.

Urban Rail System Modeling and Simulation Based on Dynamic Train Density

To further improve the simulation calculation ability of urban rail traction systems during the peak operation period and provide an accurate and reliable simulation tool for the subsequent train schedule and energy storage system design, a multi-train circuit model with a bilateral power supply was established in this paper, and a power calculation algorithm based on dynamic train density was designed. The circuit topology in the model can be dynamically adjusted according to the number of trains to improve the operation rate. Based on the spatial and electrical data of a real section of the subway, the urban rail circuit model was built on the MATLAB platform, and the actual operation data of the subway was imported for verification. The experimental results show that the multi-train model can accurately reflect the influence of voltage fluctuations on the traction system under different train running conditions, and the results fit the actual operation conditions. By comparing the influence of different train intervals on the RBE (regenerative braking energy) utilization, the results show that the optimal RBE utilization rate can be achieved by adjusting the train interval in the peak period.