Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

The Mechanism Behind Urban Population Growth and Shrinkage from the Perspective of Urban Network Externalities

Urban shrinkage is a global phenomenon,and it will coexist with urban growth for many years.At the same time,the network connection between cities continuously improved due to the construction of the transportation and information networks.However,the relationship between urban network externalities and urban population growth/shrinkage remains unclear.Therefore,based on high-speed railway(HSR)flow data,a spatial econometric model is used to explore the mechanism behind urban population growth and shrinkage from the perspective of network externalities in China.The results indicate that:1)the urban network experiences a certain clubbing effect.Growing cities that are strongly connected are concentrated along China’s main railway lines and the southeastern coastal areas,while shrinking cities that are weakly connected are distributed at the periphery of the network.2)Moreover,the network externality disregards spatial distance and together with the agglomeration externality influences the growth and shrinking of cities.3)Urban economic development still promotes the development of Chinese cities.However,the improvement of the urban economy has a negative cross-regional spillover effect on neighboring cities due to urban competition.4)Lastly,Local spillovers of urban network externalities are positive,while cross-regional ones are negative.Consequently,the government needs to promote the construction of multi-dimensional network connections between cities to promote cities’sustainable development.This study reveals the relationship between urban network externalities and urban development,enriches the theories of network externalities and urban growth/shrinkage,and provides a reference for regional coordinated development.

Waterlogging risk assessment based on self-organizing map(SOM)artificial neural networks:a case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research

With the acceleration of urbanization in China,the discharge of domestic sewage and industrial wastewater is increasing,and accidents of sewage spilling out and polluting the environment occur from time to time.Problems such as imperfect facilities and backward control methods are com-mon in the urban drainage network systems in China.Efficient drainage not only strengthens infrastructure such as rain and sewage diversion,pollution source monitoring,transportation,drainage and storage but also urgently needs technical means to monitor and optimize production and operation.Aiming at the optimal control of single-stage pumping stations and the coordinated control between two-stage pumping stations,this paper studies the modelling and optimal control of drainage network systems.Based on the Long Short Term Memory(LSTM)water level prediction model of the sewage pumping stations,and then based on the mechanism analysis of drainage pipe network,the factors that may cause the water level change of pumping station are obtained.Grey correlation analysis is carried out on these influencing factors,and the prediction model is established by taking the factors with a high correlation degree as input.The research results show that compared with the traditional prediction model,the LSTM model not only has higher prediction accuracy but also has better inflection point tracking ability.

Seismic vulnerability assessment of urban buildings and traffic networks using fuzzy ordered weighted average

Urban buildings and urban traffic network are considered as the vital arteries of cities which have particular effects especially after the crisis in the search and rescue operations. The aim of this study is to determine the vulnerability of urban areas especially, buildings and traffic networks using multicriteria geographic information systems and decisionmaking methods. As there are many effective criteria on the seismic vulnerability that they have uncertain and vague properties, the method of this paper is applying fuzzy ordered weighted average(OWA) to model the seismic vulnerability of urban buildings and traffic networks in the most optimistic and pessimistic states. The study area is district 6 of Tehran that is affected by the four major faults, and thus will be threatened by the earthquakes. The achieved results illustrated the vulnerability with different degrees of risk levels including very high, high, medium, low and very low. The results show that in the most optimistic case 14% and in the pessimistic case 1% of buildings tolerate in very low vulnerability. The vulnerability of urban street network also indicates that in the optimistic case 12% and in the pessimistic case at most 9% of the area are in appropriate condition and the North and NorthEast of the study area are more vulnerable than South of it.

Travel Patterns and Street Networks: A Novel Visualization Methodology of City-Level Traffic Flows and Network Usability

Street Networks, knitted in the urban fabric, facilitate spatial movement and control the flow of urbanization. The interrelation between a city’s spatial network and how the residents travel over it has always been of high interest to scholars. Over the years, multifaceted visualization methods have emerged to better express this travel trend from small to large scale. This study proposes a novel approach to 1) visualize city-wide travel patterns with respect to the street network orientation and 2) analyze the discrepancies between travel patterns and streets to evaluate network usability. The visualizations adopt histograms and rose diagrams to provide several insights into network-wide traffic flows. The visualization of four New York City (NYC) boroughs including Queens, Brooklyn, Bronx, and Staten Island was generated for the daily traffic and the average hourly flows in the morning and evening rush hours. Then the contrasts between built-in street network topology and travel orientation were drawn to show where people travel over the network, travel demand, and finally which segments experience high or light traffic, revealing the true picture of network usability. The findings of the study provide an insight into the novel and innovative approach that can help better understand the travel behavior lucidly and assist policymakers in decision making to maintain a balance between urban topology and travel demands. In addition, the study demonstrates how to further investigate city street networks and urbanization from different diverse dimensions.

Integration of Neural Networks and Cellular Automata for Urban Planning

This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.

Coordinated chaos control of urban expressway based on synchronization of complex networks

We investigate the problem of coordinated chaos control on an urban expressway based on pinning synchronization of complex networks. A node coupling model of an urban expressway based on complex networks has been established using the cell transmission model（CTM）. The pinning controller corresponding to multi-ramp coordinated controller was designed by using the delayed feedback control（DFC） method, whose objective is to realize periodical orbits from chaotic states. The concrete pinning control nodes corresponding to the subsystems of regulating the inflows from the on-ramps to the mainline were obtained and the parameters of the controller were optimized by using the stability theory of complex networks to ensure the network synchronization. The validity of the proposed coordinated chaos control method was proven via the simulation experiment. The results of the examples indicated that the order motion on urban expressway can be realized, the wide-moving traffic jam can be suppressed, and the operating efficiency is superior to that of the traditional control methods.

Mapping Urban Networks through Inter-Firm Linkages: The Case of Listed Companies in Yangtze River Delta, China

Recently, literature on urban network research from the perspective of ?rm networks has been increasing. This research mainly used data from the headquarters and branches of all 2581 listed manufacturing companies in the Yangtze River Delta from 1990 to 2017, and studied the urban network through an interlocking network model that quantifies the links between enterprises. The results showed that the spatial distribution of listed manufacturing industries in the Yangtze River Delta was relatively concentrated, and cities such as Shanghai, Nanjing, and Hangzhou were hot spots for the spatial distribution of listed manufacturing industries. However, Fuyang, Suqian, Chizhou, Lishui and other network edge cities were less distributed in manufacturing. The urban network of the Yangtze River Delta has significant hierarchical characteristics. The urban network of the Yangtze River Delta presents a multi-center network development mode with Shanghai as the center and Nanjing, Hangzhou, and Hefei as the sub-centers. Moreover, we found that the development of inter-city connections in the Yangtze River Delta was driven by network mechanisms of priority attachment and path dependence. The radiating capacity and agglomeration capacity of cities in the Yangtze River Delta have a strong polarization characteristic. The core cities such as Shanghai, Nanjing, Hangzhou, and Hefei have much higher network radiation capabilities than network aggregation capabilities. However, other non-core cities and network edge cities have weak network radiation capabilities, and mainly accept network radiation from core cities. It enriches the research of urban networks based on real inter-?rm connections, and provides ideas for the wider regional study and the combination of econometric techniques and social network analysis.

Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks

This paper proposes a new approach for detecting human survivors in destructed environments using an autonomous robot. The proposed system uses a passive infrared sensor to detect the existence of living humans and a low-cost camera to acquire snapshots of the scene. The images are fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation, which considerably reduces the cost of image processing, data transmission, and power consumption. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity, and the relative color matching between the body and the surrounding environment.

Materializing the “Urban Region” Concept by Integrating Ecological Networks and Central Place Theory

This paper attempts to integrate urban development and ecological conservation by applying and syncretizing the “urban region” concept from landscape ecology and the “city-region” concept from socioeconomics. First, various concepts pertaining to regions are discussed and then, the integration of ecological conservation and urban development on a regional scale is introduced. Subsequently, the Greater Pearl River Delta, in China, is used as a case study area and landscape ecology’s “urban region” concept is applied to produce a landscape spatial arrangement framework for an urban region. This framework is achieved through the following steps: conceiving the study area as a region that consists of two urban regions, arranging ecological conservation landscapes by establishing a regional ecological network within the urban-region rings;and formulating an urban development strategy using central place theory. The resulting landscape spatial arrangement solution includes natural protection areas that cover half of the study area, several key strategic urbanizing locations, and suggestions for the strict protection of certain agricultural land-use areas. We believe that this framework facilitates a feasible exploration of land-use planning on a regional scale, although more in-depth studies are required to refine this approach.

Analysis of robustness of urban bus network

In this paper, the invulnerability and cascade failures are discussed for the urban bus network. Firstly, three static models（bus stop network, bus transfer network, and bus line network） are used to analyse the structure and invulnerability of urban bus network in order to understand the features of bus network comprehensively. Secondly, a new way is proposed to study the invulnerability of urban bus network by modelling two layered networks, i.e., the bus stop-line network and the bus line-transfer network and then the interactions between different models are analysed. Finally, by modelling a new layered network which can reflect the dynamic passenger flows, the cascade failures are discussed. Then a new load redistribution method is proposed to study the robustness of dynamic traffic. In this paper, the bus network of Shenyang City which is one of the biggest cities in China, is taken as a simulation example. In addition, some suggestions are given to improve the urban bus network and provide emergency strategies when traffic congestion occurs according to the numerical simulation results.

The Structure and Pattern of Urban Network in the Lanzhou-Xining Urban Agglomeration

In this paper,we use factor analysis to evaluate the urban comprehensive quality of each city in the Lanzhou-Xining(Lan-Xi)urban agglomeration.The time distance was obtained by using GIS spatial analysis,and the structure and pattern of the spatial network were analyzed by using the gravity model and social network analysis method.The results show that:1)The scale effect of the Lan-Xi urban agglomeration is gradually emerging,and it is gradually forming the urban agglomeration with Lanzhou and Xining as the core,the Lan-Xi high-speed railway as the axis,and a high-dense connection.2)Lanzhou and Xining are at the core of the Lan-Xi urban agglomeration,which has a strong attraction and spreads to neighboring cities.3)In the network structure of the Lan-Xi urban agglomeration,Lanzhou,Baiyin,Gaolan,Yuzhong,Yongdeng,Dingxi,Lintao,Xining,Ledu,Huangzhong,Ping’an,Minhe and Datong are located in the network core position,which have the superiority position and lead to the entire regional communication enhancement and the regional integration development.4)This urban agglomeration has significant subgroups,eight tertiary subgroups and four secondary subgroup;the tertiary subgroups which compose secondary subgroup have a close connection and mutually influence each other.5)The Lanzhou Metropolitan Area and the Xining Metropolitan Area have an important impact on the surrounding cities,and the peripheral cities are basically controlled by the central city.The Dingxi subgroup,Lintao-Linxia subgroup,Gonghe subgroup have more structural holes than the subgroups within the Lanzhou Metropolitan Area and the Xining Metropolitan Area,so the peripheral cities of these subgroups have relatively less connection with surrounding cities.

An evolutionary model of urban bus transport network based on B-space

In this paper, an evolutionary model of bus transport network in B-space is developed. It includes the effect of the overlapping ratio of new route on network performance and overcomes the disadvantage, i.e. lack of economic consideration, in the evolutionary bus transport network model in P-space proposed by Chen et al （2007）. The degree distribution functions are derived by using the mean-field method and the master equation method, separately. The relationship between the new stop ratio of a route, λ, and the error in exponential of degree distribution function from the mean-field method is developed as ASlope= λ/（1 -λ） ＋ ln（1-λ）. Finally, the bus transport networks of Hangzhou and Nanjing are simulated by using this model, and the results show that some characteristic index values of the simulated networks are closer to the empirical data than those from Chen＇s model.

Measuring Network Configuration of the Yangtze River Middle Reaches Urban Agglomeration:Based on Modified Radiation Model

The objective of this study is to develop a framework for re-examining and re-defining the classical concepts of spatial interaction and reorganization in the urban system.We introduce a modified radiation model for spatial interactions,coupled with migration big data,transport accessibility algorithm,and city competitiveness assessment for efficient distribution of the inter-city flow through the network.The Yangtze River Middle Reaches(YRMR)urban agglomeration(UA)is chosen as the case study region to systematically identify and measure its spatial configuration and to gain insights for other UAs‘sustainable development in China.The results are also compared with those computed by the classical gravity model to systematically discuss the applicability of spatial interaction laws and models,and related practical policies for regional sustainable development are discussed based on the findings as well.The conclusions are highlighted below:1)Combining with the?city network paradigm‘and?central place theory‘can better express the spatial configurations of city systems in the context of?space of flows‘;2)The results validate the potentialities of a multi-analysis framework to assess the spatial configurations of city network based on the improved radiation model and network analysis tools;3)The applications of spatial interaction models should be considered according to the specific geographical entity and its spatial scale.

Construction of Ecological Network for Urban Green Space System from the Perspective of Landscape Ecology:A Case Study of Wanzhou District, Chongqing Municipality

Green spaces in Wanzhou District, Chongqing Municipality were not closely linked and failed to form an integrated ecological network, this paper analyzed and assessed current situation of green spaces in the local area from the perspective of landscape ecology, and then established the landscape ecology-oriented ecological network construction, restructured green paces in Wanzhou District using the landscape composition of "patch–corridor–matrix", and connected the scattered green patches using green corridors, so as to form a reasonable ecological network of green spaces.

Importance Analysis of Urban Rail Transit Network Station Based on Passenger

Current urban rail transit has become a major mode of transportation, and passenger is an important factor of urban rail transport, so this article is based on passenger and the degree of the road network structure, calculating the point intensity of stations of urban rail transit, and then reaching a station importance by integrating many point intensities in a survey cycle time, and getting the station importance of urban rail transit network through concrete examples.

Examination and Interpretation of the Quantitative Validity in China’s Corporate-based Urban Network Analysis

As a matter of expediency, most existing corporate-based urban networks can only be quantitatively measured by either counting the number of linkages or calculating the product of estimated service values. However, the impreciseness arising due to the limits of quantitative analysis may prove fatal to studies about non-market economies like China. Employing the capital investment dataset as an example, we build a capital-weighted intervention network as well as an unweighted control network to carry out an examination of the quantitative validity in China’s corporate-based urban network analysis. Both the overall spatial pattern and top city-dyads within the capital-weighted network witness Beijing, as the most dominant city, overshadow the performance of the others, and the unweighted network shows multilateral interactions between China’s top cities including Beijing, Shanghai, Shenzhen, and Guangzhou. To further interpret the noticeable differences, we divide the overall network into two subnetworks, inferred by focusing on state-owned enterprises(SOEs) and private enterprises. The results show that the public and private sectors have separately created vastly different subnetworks in China and that SOEs play a much more significant role in terms of capital. Besides fresh insights into China’s urban network, this study provides a cautionary tale reminding researchers of the essentiality and complexity when making a quantitative distinction between different linkages.