A Retrospect and Prospect of Urban Models: Reflections after Interviewing Michael Batty

Based on an interview with Prof. Michael Batty, a leading scholar in urban modeling, this paper reviews the history of urban models, comments on their present developments and pictures their prospects. The first section briefly overviews the family tree of urban models and then looks further into the causes for their failure in planning application in the first upsurge of related research in the 1960 s and 1970 s. The second section summarizes the current development of urban models, which can be counted as the second upsurge of related research. It is estimated that more research attention will be paid to building dynamic, disaggregate, micro and problem-oriented models, with a combination of top-down and bottomup modeling methods. This can be more capable of tackling the growing urban complexity and uncertainty. Meanwhile, the "big data era" also poses more opportunities for the development of urban models. The last section introduces three new trends in the theoretical, educational and research development of urban models, which compose the idea of "new science of cities" from Michael Batty, the start-up of quantitative urban research education and the establishment of Beijing City Lab.

Modeling urban land use dynamics using Markov-chain and cellular automata in Gondar City,Northwest Ethiopia

Modeling urban land-use dynamics is critical for urban experts’and infrastructure managers’planning.This study attempts to explore the land-use/land-cover(LULC)dynamics of Gondar using satellite images from 1984 to 2020.Markov-Chain and Cellular Automata(MC-CA)models have been recognized as performing well in predicting urban land-use change.However,only a few models work in Ethiopia in general,and no study in Gondar has applied this approach to study urban land-use patterns.Therefore,Gondar land-use/land cover changes of Gondar were predicted using the MC-CA model in IDRISI.The built-up area in Gondar city covered 1413 ha(3%of the total area)in 1984 and increased to 2380 ha(5%)in 1994;21153 ha(45.5%)in 2004;22622 ha(48.7%)in 2014;and 23427 ha(50.5%)in 2020.The area has been predicted to reach 57.5%in the 2050s,showing a faster increase that will cause a very vast loss of farmland.This will increase urban sprawl challenges as well as overall environmental disequilibrium in the preceding decade.Thus,innovative and careful structures and systems in urban planning are required to secure a sustainable urban future and to make our cities livable and competitive in the paradigm of sustainable cities.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

APPLICATION OF WRF/UCM IN THE SIMULATION OF A HEAT WAVE EVENT AND URBAN HEAT ISLAND AROUND GUANGZHOU

This paper evaluated the performance of a coupled modeling system,Weather Research and Forecasting(WRF)/Urban Canopy Model(UCM),in the simulation of a heat wave event which occurred around Guangzhou during late June through early July,2004.Results from three experiments reveal that the UCM with new land data(hereafter referred to as E-UCM)reproduces the best 2-m temperature evolution and the smallest minimum absolute average error as compared with the other two experiments,the BPA-Bulk Parameterization Approach with new land data(E-BPA)and the UCM with original U.S. Geological Survey land data(E-NOU).The E-UCM is more useful in capturing the temporal and spatial distribution of the nighttime Urban Heat Island(UHI).Differences in surface energy balance between the urban and suburban areas show that low daytime albedo causes more absorption of solar radiation by urban areas.Due to the lack of vegetation which inhibits cooling by evapotranspiration,most of the incoming energy over urban areas is partitioned into sensible heat flux and therefore heats the surface and enhances the heat wave.During nighttime,the energy in the urban area is mainly from soil heat flux.Although some energy is partitioned as outgoing long wave radiation,most of the soil heat flux is partitioned into sensible heat flux due to the small latent heat flux at night.This leads to the development of nighttime UHI and the increase of the magnitude and duration of heat waves within the municipality.

Influences of Structures on Urban Ventilation: A Numerical Experiment

Numerical simulations are carried out for the disturbed flow caused by three six-story buildings and a twenty-story tall tower respectively, as well as the distribution of automobile exhaust gas from a nearby road, based on the Peking University Model of Atmospheric Environment. The results show that the ventilation is better around the tall tower than around the three six-story residential buildings for the same number of households in the same urban region.

An iterative approach to an integrated land use and transportation planning tool for small urban areas

There has been increasing interests in developing land use models for small urban areas for various planning applications such as air quality conformity analysis. The output of a land use model can serve as a major input to a transportation model; conversely, transportation model output can provide a critical input to a land use model. The connection between the two models can be achieved by an accessibility measure. This paper presents an iterative approach to solving a regression-based land use model and a transportation model with combined trip distribution- assignment. A case study using data from a small urban area is presented to illustrate the application of the proposed modeling framework. Tests show that the procedures can converge, and the modeling framework can be a valuable tool for planners and decision-makers in evaluating land use policies and transportation investment strategies.

Modelling of Nature-Based Solutions (NBS) for Urban Water Management—Investment and Outscaling Implications at Basin and Regional Levels

This manuscript is an attempt to demonstrate effectiveness of nature-based solutions (NBS) and measures to reduce risk of flooding and environmental impact in urban settings. The nature-based solutions (NBS) were assessed as scenarios from experience of urban storm drainage and sewerage systems based on practices that improve urban water management through modelling using urban stormwater management model (SWMM). The model has been applied in a typical urban environment in the second city in Botswana, the City of Francistown, which has a population of more than one hundred thousand. By considering the 2-yr and 10-year storm events in a calibrated SWMM, NBS scenarios from a mix of low impact and drainage measures were considered. The considered NBS scenarios were used to determine their effectiveness in terms of reducing and controlling peak runoff, flood volumes, infiltration and evapotranspiration in the study area, which are vital in assessing the opportunity and challenge for sustainable management of water resources and associated tradeoff of investments in the urban contexts. The study demonstrates the usefulness of implementing effective measures for achieving NBS in urban context and possibility of outscaling at basin and regional levels.

A Delivery Approach Modeling for Urban Freight Restocking

This paper proposes a modeling system developed in order to analyze the urban freight transport and logistics within urban and metropolitan areas. A review of models developed to simulate this segment of mobility is also reported. The review analysis highlights the limits of models for the ex-ante assessment of city logistics measures. For this reason this paper proposes a new modeling system approach for the assessment of city logistics measures. It is made of different steps approaching problems related to quantity OD （Origin-Destination） flows, restocking type OD flows, delivery OD flows, delivery OD flows for time slice and vehicle type, and vehicle OD flows. This modeling system has been specified and calibrated using some surveys carried out in the inner area of Rome where more than 500 truck drivers and more than 600 retailers have been interviewed.

Using Impervious Surfaces to Detect Urban Expansion in Beijing of China in 2000s

The change of impervious surface area(ISA) can effectively reveal the gradual process of urbanization and act as a key index for monitoring urban expansion. Experiencing rapid growth of the built environment in the 2000 s, urban expansion of Beijing has not been fully characterized through ISA. In this study, Landsat TM images of Beijing in 2001 and 2009 were obtained, and the eight-year urban expansion process in Beijing was analyzed using the ISA extracted by means of the vegetation-imperious surface-soil(V-I-S) model. From the spatial variation in ISA, the ring structure of urban expansion in Beijing was significant during the study period, with decreasing urban density from the city center to the periphery. In the ring road analysis, the most dramatic changes of ISA were found between the fifth ring and the sixth ring. This area has experienced the most new residential development, and is currently the main source of urban expansion. The typical profile lines revealed the directional characteristics of urban expansion. The east-west profile was the most urbanized axes in Beijing, while ISA change in the east-north profile was more significant than in the other five profiles. Moreover, the transition matrix of ISA levels revealed an increase in urban density in the low density built areas; the Moran′s I index showed a clear expansion of the central urban area, which spread contiguously; and the standard deviational ellipse indicated the northeast was the dominant direction of urban expansion. These findings can provide important spatial control guidelines in the next round of national economic and social development planning, overall urban and rural planning, and land use planning.

Conceptual Model for Urban Competitiveness

Urban competitiveness is always the focus of researches on urban economy. The variety of definitions and evaluation systems of urban competitiveness are the results of lack of accurate and comprehensive understanding on urban competitiveness. This paper reviews five groups of definitions on urban competitiveness and establishes a conceptual model for urban competitiveness.

Unified Data Model of Urban Air Pollution Dispersion and 3D Spatial City Model:Groundwork Assessment towards Sustainable Urban Development for Malaysia

Understanding the behavior of urban air pollution is important en route for sustainable urban development (SUD). Malaysia is on its mission to be a developed country by year 2020 comprehends dealing with air pollution is one of the indicators headed towards it. At present monitoring and managing air pollution in urban areas encompasses sophisticated air quality modeling and data acquisition. However, rapid developments in major cities cause difficulties in acquiring the city geometries. The existing method in acquiring city geometries data via ground or space measurement inspection such as field survey, photogrammetry, laser scanning, remote sensing or using architectural plans appears not to be practical because of its cost and efforts. Moreover, air monitoring stations deployed are intended for regional to global scale model whereby it is not accurate for urban areas with typical resolution of less than 2 km. Furthermore in urban areas, the pollutant dispersion movements are trapped between buildings initiating it to move vertically causing visualization complications which imply the limitations of existing visualization scheme that is based on two-dimensional (2D) framework. Therefore this paper aims is to perform groundwork assessment and discuss on the current scenario in Malaysia in the aspect of current policies towards SUD, air quality monitoring stations, scale model and detail discussion on air pollution dispersion model used called the Operational Street Pollution Model (OSPM). This research proposed the implementation of three-dimensional (3D) spatial city model as a new physical data input for OSPM. The five Level of Details (LOD) of 3D spatial city model shows the scale applicability for the dispersion model implementtation. Subsequently 3D spatial city model data commonly available on the web, by having a unified data model shows the advantages in easy data acquisition, 3D visualization of air pollution dispersion and improves visual analysis of air quality monitoring in urban areas.

Urban Heat Island： Dynamic Simulation, Assessment and Measuring Mitigation in Cities of Extreme Dry Weather

Increasing urbanization in the cities of northern Mexico reflects a general trend to increased temperatures, so it is likely that heat waves amplify the frequency and intensity in urban centers, mainly located in arid and semiarid as Mexicali city with extremely arid climate, very hot in summer and cold and rainy in winter. Mexicali, Baja California, Mexico is located at N32°38＇ and W115°20＇. The urban area is expanded over 14,890 hectares, with a population rise the 689,775. In the last four decades has experienced an accelerated industrial growth and mismatched land uses, for example： most of the industrial parks were established before the 1980 in what was the outskirts of the city, but nowadays practically are inside of the urban area contributing to the increase the urban temperature. The heat islands profile shows that are intensified in industrial areas as well as trade and services. The preliminary scenarios of climate change for Mexicali indicate that for the decade of 2080 the temperature will increase between 4.2℃ and 4.4℃. This paper addresses in a simulation context, an industrial and commercial city sector and their ability to implement urban heat island mitigation strategies. The simulation of this process requires several spatial analysis tools and specific knowledge about the processes that increase urban temperatures. In this work, only land use, land cover and buildings are considered. The proposed method takes into account the actual spatial organization to analyze trends for the proposed growth areas.

Improving the WRF/urban modeling system in China by developing a national urban dataset

Accurate modeling of urban climate is essential to predict potential environmental risks in cities.Urban datasets,such as urban land use and urban canopy parameters(UCPs),are key input data for urban climate models and largely affect their performance.However,access to reliable urban datasets is a challenge,especially in fast urbanizing countries.In this study,we developed a high-resolution national urban dataset in China(NUDC)for the WRF/urban modeling system and evaluated its effect on urban climate modeling.Specifically,an optimization method based on building morphology was proposed to classify urban land use types.The key UCPs,including building height and width,street width,surface imperviousness,and anthropogenic heat flux,were calculated for both single-layer Urban Canopy Model(UCM)and multiple-layer Building Energy Parameterization(BEP).The results show that the derived morphological-based urban land use classification could better reflect the urban characteristics,compared to the socioeconomic-function-based classification.The UCPs varied largely in spatial within and across the cities.The integration of the developed urban land use and UCPs datasets significantly improved the representation of urban canopy characteristics,contributing to a more accurate modeling of near-surface air temperature,humidity,and wind in urban areas.The UCM performed better in the modeling of air temperature and humidity,while the BEP performed better in the modeling of wind speed.The newly developed NUDC can advance the study of urban climate and improve the prediction of potential urban environmental risks in China.

AN APPROACH ON SEVERAL PROBLEMS OF CHINA'S URBANIZATION DEVELOPMENT

China, having urique national conditions, is facing a basic contradiction between large numbers of less educated surplus rural labor forces and limited capacities of urban employment and infrastructure on the urbanization process.Hence, the urbanization model should prevail in comprehensive and several ways: (1)On occupational and spatial shift rural surplus labors, two fashions of 'leaving land bot not countryside' and 'leaving both land and countryside' should be integrated with joint effort of cities and countryside. (2) On urbanization motive, urban construction will be invested by not only the govermment (from the upper), but the locality, personnel and foreigners (from the lower). (3) On urban scales, metropolis,large, medium-sized and small cities and towns should play fully their roles to absorb rural surplus lanors. Thereby, the present urban development policy needs to be rectified, which this paper has discussed particularly. (4) On account of great regional disparities in China, urban development should perform different models in different regions. In addition, this paper has also focused on the problem and its primary countermeasures of 'labor force mass', a hot issue closely related to urbanization and rural labor transfer.

Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network

An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.

Application of fuzzy optimization model in the evaluationof urban flood-waterloggedvulnerabilitybased on the synthetic weight ofgame theory

The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calculate the subjective and objective weight of the evaluation indicators, and combine them by game theory. So we can obtain synthetic weight based on objective and subjective weights. The evaluation of urban flood-waterlogged vulnerability as target layer, a single variable multi-objective fuzzy optimization model is established. We use the model to evaluate flood-waterlogged vulnerability of 13 prefecture-level city in Hunan, and compare it with other evaluation method. The results show that the evaluation method has certain adaptability and reliability, and it＇ s helpfid to the construction planning of urban flood control.

Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

Impacts of Roof/Ground Mitigation Strategies on Improving the Urban Thermal Environment and Human Comfort over the Yangtze River Delta, China

The combined effects of global warming and the urban heat islands exacerbate the risk of urban heat stress. It is crucial to implement effective cooling measures in urban areas to improve the comfort of the thermal environment. In this study, the Weather Research and Forecasting Model(WRF), coupled with a single-layer Urban Canopy Model(UCM), was used to study the impact of heat mitigation strategies. In addition, a 5-km resolution land-cover dataset for China(ChinaLC), which is based on satellite remote sensing data, was adjusted and used, and 18 groups of numerical experiments were designed, to increase the albedo and vegetation fraction of roof/ground parameters. The experiments were conducted for four heatwave events that occurred in the summer of 2013 in the Yangtze River Delta urban agglomeration of China. The simulated results demonstrated that, for the single roof/ground schemes, the mitigation effects were directly proportional to the albedo and greening. Among all the experimental schemes, the superposed schemes presented better cooling effects. For the ground greening scheme, with similar net radiation flux and latent heat flux, its storage heat was lower than that of the roof greening scheme, resulting in more energy flux into the atmosphere, and its daytime cooling effect was not as good as that of the roof greening scheme. In terms of human thermal comfort(HTC), the improvement achieved by the ground greening scheme was better than any other single roof/ground schemes, because the increase in the relative humidity was small. The comprehensive evaluation of the mitigation effects of different schemes on the thermal environment presented in this paper provides a theoretical basis for improving the urban environment through rational urban planning and construction.

Effectiveness of urban distributed runoff model for discharge and water depth calculation in urban drainage pipe networks

Effective urban land-use re-planning and the strategic arrangement of drainage pipe networks can significantly enhance urban flood defense capacity.Aimed at reducing the potential risks of urban flooding,this paper presents a straightforward and efficient approach to an urban distributed runoff model(UDRM).The model is developed to quantify the discharge and water depth within urban drainage pipe networks under varying rainfall intensities and land-use scenarios.The Nash efficiency coefficient of UDRM exceeds 0.9,which indicates its high computational efficiency and potential benefit in predicting urban flooding.The prediction of drainage conditions under both current and re-planned land-use types is achieved by adopting different flood recurrence intervals.The findings reveal that the re-planned land-use strategies could effectively diminish flood risk upstream of the drainage pipe network across 20-year and 50-year flood recurrence intervals.However,in the case of extreme rainfall events(a 100-year flood recurrence),the re-planned land-use approach fell short of fulfilling the requirements necessary for flood disaster mitigation.In these instances,the adoption of larger-diameter drainage pipes becomes an essential requisite to satisfy drainage needs.Accordingly,the proposed UDRM effectively combines land-use information with pipeline data to give practical suggestions for pipeline modification and land-use optimization to combat urban floods.Therefore,this methodology warrants further promotion in the field of urban re-planning.