Urban Network Research Articles

A Two-Stage Multi-Agent Deep Reinforcement Learning Method for Urban Distribution Network Reconfiguration Considering Switch Contribution

A Two-Stage Multi-Agent Deep Reinforcement Learning Method for Urban Distribution Network Reconfiguration Considering Switch Contribution

Advancing complex urban traffic forecasting: A fully attentional spatial-temporal network enhanced by graph representation

Accurate urban traffic forecasting is essential for intelligent transportation systems (ITS). However, the majority of existing forecasting methodologies predominantly concentrate on point-based forecasts (e.g., traffic detector forecasts). A limited number of them pay attention to the urban bidirectional road segments and the complex road network topology. To advance accurate traffic forecasting in complex urban scenarios, this paper proposes a Graph Representation enhanced Fully Attentional Spatial-Temporal network (GR-FAST). First, we construct a refined bidirectional road network graph (BRG) to depict the urban road network topology more accurately, particularly focusing on the turning patterns at intersections. Then, we adopt the graph representation methodology and introduce spatial information encoding (SIE) to explicitly characterize the significance of roads and network structure from multiple perspectives. Enhanced by SIE, spatial attention can capture spatial dependencies from both road network topologies and traffic pattern similarities, thereby forming a unified urban spatial cognition. Finally, a multi-scale residual perception (MRP) module is designed to balance the interplay of short-term temporal variability and long-term periodicity. Experiments on a real-world urban dataset from Wuhan, China, demonstrate that GR-FAST outperforms the state-of-the-art deep learning methods, achieving an improvement of 9.19%. Furthermore, ablation studies suggest that the explicit incorporation of complex road spatial topologies can significantly enhance forecasting accuracy.

Optimizing urban bus network based on spatial matching patterns for sustainable transportation: A case study in Harbin, China.

The rapid economic development and accelerating urbanization have led to a significant mismatch between the urban bus network allocation and the population flow. Therefore, this paper investigates this challenge by exploring the intricate relationship between the population flow dynamics, traffic congestion conditions, and the efficient allocation of bus resources. In response, two key indexes were introduced based on spatial matching patterns to assess the urban bus network: the Population-Bus Match Index evaluates the matching degree between supply and demand, and the Population-Congestion Match Index evaluates the matching degree between utilization and saturation. Additionally, two distinct optimization strategies have been proposed to enhance the urban bus network. The first optimization strategy considers the bus network's current status, while the second aspires to an idealized scenario. Subsequently, the potential contributions of each bus station in reducing CO2 emission reduction after implementing the two optimization strategies are quantified. Utilizing a case study focused on Harbin, the proposed methods are validated. The findings unveil a substantial misalignment between supply and demand within the bus network during peak periods, with nearly half of the bus stations experiencing a disparity between utilization and saturation. Comparative experiments across different optimization strategies reveal that the second optimization strategy significantly outperforms the first, but the first optimization strategy has a higher degree of CO2 emission reduction contribution. The results of this study provide decision-makers with an environmentally oriented vantage point for the discerning selection of optimization strategies and leave valuable insights for urban areas confronting transportation challenges.

Efficient and secure service function chain deployment method for delay optimization in air traffic information network

The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.

Photosynthetic Electron Flows and Networks of Metabolite Trafficking to Sustain Metabolism in Photosynthetic Systems

Photosynthetic eukaryotes have metabolic pathways that occur in distinct subcellular compartments. However, because metabolites synthesized in one compartment, including fixed carbon compounds and reductant generated by photosynthetic electron flows, may be integral to processes in other compartments, the cells must efficiently move metabolites among the different compartments. This review examines the various photosynthetic electron flows used to generate ATP and fixed carbon and the trafficking of metabolites in the green alga Chlamydomomas reinhardtii; information on other algae and plants is provided to add depth and nuance to the discussion. We emphasized the trafficking of metabolites across the envelope membranes of the two energy powerhouse organelles of the cell, the chloroplast and mitochondrion, the nature and roles of the major mobile metabolites that move among these compartments, and the specific or presumed transporters involved in that trafficking. These transporters include sugar-phosphate (sugar-P)/inorganic phosphate (Pi) transporters and dicarboxylate transporters, although, in many cases, we know little about the substrate specificities of these transporters, how their activities are regulated/coordinated, compensatory responses among transporters when specific transporters are compromised, associations between transporters and other cellular proteins, and the possibilities for forming specific ‘megacomplexes’ involving interactions between enzymes of central metabolism with specific transport proteins. Finally, we discuss metabolite trafficking associated with specific biological processes that occur under various environmental conditions to help to maintain the cell’s fitness. These processes include C4 metabolism in plants and the carbon concentrating mechanism, photorespiration, and fermentation metabolism in algae.

Optimising Dynamic Traffic Distribution for Urban Networks with Answer Set Programming

Abstract Answer set programming (ASP) has demonstrated its potential as an effective tool for concisely representing and reasoning about real-world problems. In this paper, we present an application in which ASP has been successfully used in the context of dynamic traffic distribution for urban networks, within a more general framework devised for solving such a real-world problem. In particular, ASP has been employed for the computation of the “optimal” routes for all the vehicles in the network. We also provide an empirical analysis of the performance of the whole framework, and of its part in which ASP is employed, on two European urban areas, which shows the viability of the framework and the contribution ASP can give.

FORMA MILICIANA E SEUS PARADIGMAS NO ESPAÇO-TEMPO DO URBANO NA METRÓPOLE DO RIO DE JANEIRO

The article presents five hypotheses regarding our evaluation of the current emergence of the geographical space of the militia form. To construct this hypothesis, the Metropolitan West of Rio de Janeiro (MWRJ) has been selected as the preferred locus for the rise of this social form. In an exercise of regionalization, we connect the terms of this socio-spatial presence within the scope of the current center-periphery relationships transcribed in the Brazilian urban network. Presenting the hypotheses and weaving them from cartographic data, we bring elements to the understanding of the negative character of the modernization entangled in this fragment of the metropolis of Rio de Janeiro in recent decades. Keywords: Metropolitan West of Rio de Janeiro; Milita Form; Regionalization; Center-periphery Relation; Late Modernization

Mapping illegal trade routes of live cheetahs from the Horn of Africa to the Arabian Peninsula.

Less than 7000 cheetahs (Acinonyx jubatus) persist in Africa. Although human-wildlife conflict, habitat degradation, and loss of prey are major threats to cheetah populations, illegal trade in live cubs for pets may have the most significant impact on populations in the Horn of Africa. We developed a novel, stepwise decision support tool to predict probable trafficking routes by leveraging the power of distinct modeling approaches. First, we created a cheetah habitat suitability index (HSI) to determine where source cheetah populations may occur. We then created a trafficking network model linking known and predicted cheetah populations with documented destinations in the Arabian Peninsula. A significant area in Eastern Ethiopia and Northern Somalia was estimated to harbor undocumented cheetahs. When these predicted populations were used as a supply source, the trafficking network model showed multiple routes passing through Somaliland and across the Gulf of Aden to Yemen, supporting the notion that undocumented cheetahs may be supplying pet market demands. Though we demonstrate how our decision support tool can inform law enforcement, conservation strategies, and community engagement, we caution that our results are not fully validated due to limited accessibility, alternative trafficking routes, and the cryptic nature of illegal wildlife trade.

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Asynchronous decentralized traffic signal coordinated control in urban road network

AbstractThis study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

Construction of urban tomographic synthetic aperture radar double-tier permanent scatterer network combined with region-growing algorithm

Construction of urban tomographic synthetic aperture radar double-tier permanent scatterer network combined with region-growing algorithm

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development: A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization. Urban integration is an emerging trend in metropolitan areas. This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development. Rail transit in metropolitan areas offers both technical and economic advantages, improving the efficiency of time and space resource utilization, fostering economic cooperation, and ultimately contributing to an integrated development model. However, the integration of rail transit networks faces several challenges, including road network planning, technical standards, and operational organization. Using the Wuhan metropolitan area as a case study, this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Principal Component Analysis (PCA) of MALDI-TOF for the Identification of Waterborne Pathogenic Bacteria

The study focuses on the application of Principal Component Analysis (PCA) combined with MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry) for the identification of waterborne pathogenic bacteria in urban water networks. In this comprehensive research, 168 bacterial strains were meticulously isolated from the water networks of Abidjan, Côte d'Ivoire, a region known for its inadequate wastewater treatment infrastructure. The analysis aimed to rapidly and precisely identify these bacterial pathogens, leveraging the power of MALDI-TOF MS and the sophisticated data reduction capabilities of PCA. This approach not only accelerates the identification process but also enhances the accuracy of detecting various pathogens. The study identified a diverse range of pathogens, including Pseudomonas aeruginosa, Escherichia coli, Salmonella, Shigella, Vibrio cholerae, Morganella morganii, Proteus mirabilis, Acinetobacter baumannii, Klebsiella pneumoniae, Providencia rettgeri, Providencia stuartii, Aeromonas hydrophila, Bacillus cereus, Lysinibacillus fusiformis, Lysinibacillus sphaericus, and Staphylococcus aureus. These findings underscore the critical public health risks posed by microbial contamination in water networks, particularly in areas with deficient waste management systems. This study highlights the necessity for improved wastewater management practices, robust public health strategies, and regular monitoring to mitigate the risks associated with waterborne pathogens. Moreover, the integration of PCA with MALDI-TOF MS proves to be a powerful tool for enhancing the efficiency and accuracy of pathogen identification in environmental water samples, offering a promising solution for better public health protection and water quality management in urban settings.

Quantitative assessment of congestion diffusion and cascading effect under rainfall-flood disasters: A case study of Nanjing, China

Urban road networks are frequently disrupted by flooding rainfall-flood disasters, which can cause severe traffic disruptions and leading to traffic congestion due to cascading effect. This paper investigates the reliability issues under rainfall-flood conditions. A coupled model, integrating a rainfall-flood model with an improved cascading failure model, is proposed to assess how rainfall intensities and flooding will influence traffic congestion and bring network instability. Utilizing an improved Nonlinear Load-Capacity model, we quantify the impact of congestion and analyze cascading processes under various rainfall-flood conditions. The case study in Nanjing, China reveal that, when congestion causes network pressure to exceed the traffic percolation threshold, traffic congestion diffusion becomes more pronounced, putting excessive strain on other passable roads. Network cascading failures due to traffic congestion diffusion can lead to excessive focus on the remaining passable roads, resulting in a sharp increase in the average importance. The significance of this work lies in its provision of an effective method for predicting potential network disruptions and cascading failures in advance, thereby enhancing post-disaster road operations.

Unveiling the lifeblood of cities: Identifying urban ecological networks from the perspective of biodiversity conservation

Urban biodiversity faces threats from habitat loss, landscape fragmentation, and human disturbances. Ecological networks (ENs) can enhance habitat connectivity and bolster population resilience to disruptions. To safeguard the biodiversity of Nanjing's urban area, this study selected understory insectivorous birds as indicator species for biodiversity, employing a comprehensive approach integrating the maximum entropy (MaxEnt) model and the area threshold method to delineate ecological source areas. Key indicators directly linked to the species distribution were extracted based on the habitat suitability assessment results, and an ecological resistance surface was crafted using spatial principal component analysis. Subsequently, the circuit theory model was applied to pinpoint ecological corridors, pinch points, and barrier points. The findings unveiled the following: (1) The normalized difference vegetation index (NDVI), human activities (HA), and canopy height (CH) were critical indicators influencing biodiversity. The response curves of the NDVI and CH were positively correlated with the probability distribution of indicator species, while the response curve of the HA showed an overall negative correlation with the same distribution. (2) The spatial distribution characteristics of the ecological network revealed a ‘one axis, two cores, intersecting’ pattern. Ecological sources were supported by mountainous forests and riverside green spaces, symmetrically distributed on both sides of the Yangtze River. Ecological corridors were forming continuous tree belts along highways and riverways, predominantly concentrated on the eastern side of the Yangtze River. Ecological pinch points and barriers were primarily located at the intersections of fragmented green spaces and developed areas in the northeastern and southeastern regions of the study area. (3) This study finally identified 30 ecological source areas, 65 ecological corridors, 8 pinch points, and 8 barrier points. This study presents a construction paradigm for urban ENs from the vantage point of biodiversity conservation, including both regional specificity and universal applicability, providing vital theoretical underpinnings and pragmatic insights for urban sustainability.

Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.

Interplay between Network Position and Knowledge Production of Cities in China Based on Patent Measurement

The urban knowledge network in China has undergone in-depth development in recent decades, intimately connecting the position characteristics of cities in the knowledge network to their knowledge production performance. While existing research focuses predominantly on the unidirectional relationship between network position and the knowledge production of cities, there is a notable dearth of studies exploring the bidirectional relationship between the two constructs. By proposing a conceptual framework, this paper empirically examines the interplay between network position and knowledge production of cities through simultaneous equation models. The results revealed a mutually reinforcing relationship between network position and knowledge production, and this relationship exhibits heterogeneous characteristics and spillover effects. Specifically, cities in the periphery block and the central-western region benefit more from the effect of network position on knowledge production, while cities in the core block and the eastern region benefit more from the effect of knowledge production on network position. Moreover, the interactive effect between network position and knowledge production of cities is significantly affected by the network position characteristics and knowledge production performance of their neighboring cities in geographically adjacent regions and relationally adjacent regions. These findings enhance the understanding of urban network externalities and the connotations of the knowledge production function.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Detecting IoT Cyberattacks: Advanced Machine Learning Models for Enhanced Security in Network Traffic

The IoT is one of the most revolutionary technological advancements of the contemporary era, embedding networked devices into nearly every aspect of human life, from smart homes and wearables to industrial systems and healthcare applications in the U.S.A. The immediate need for better cybersecurity in the U.S.A. arises from the increasing sophistication and frequency of cyberattacks on IoT systems. Machine learning and AI have emerged as promising technologies to deal with the security challenges IoT systems pose. Unlike traditional rule-based systems, ML models learn from large datasets to identify deviations from the normal behavior pattern that signifies malicious activity. The prime objective of this research is to design, curate, evaluate, and deploy state-of-the-art machine learning models that improve the detection of cyberattacks over IoT network traffic. This research used a well-established dataset that emulates IoT network traffic consisting of benign and malicious activities. Benchmarks like the UNSW-NB15, CICIDS2017, and TON_IoT have been in extensive use by researchers in this domain because they contain a rich variety of network traffic created by various IoT devices and systems along with corresponding labels that classify normal and associated with specific types of cyberattacks: DDOS, MITM, and botnet attacks. Data preprocessing and cleaning ensured that the dataset was consistent, complete, and in a format that helps machine learning algorithms learn from it. Imputation techniques used the feature's mean/median/mode to handle missing values. In this research project, two machine learning algorithms were used in the experiment, notably, Logistic Regression and Random Forest. In this study, the machine learning algorithms used in the experiment undertaken for the current research project are Logistic Regression and Random Forest. The performance of Random Forest was superior to Logistic Regression in almost all metrics. While Logistic Regression provided a strong baseline, it struggled with detecting attacks, as evidenced by its lower recall and higher number of false negatives. This implied that Logistic Regression was less reliable in detecting cyberattacks, which could be critical in real-world cybersecurity settings. By contrast, Random Forest attained impressive accuracy and significantly diminished the number of false negatives. Its higher precision and recall demonstrate that it is better suited for detecting attacks in this dataset, offering a more reliable solution for cyberattack detection.