Urban Simulation Research Articles

Simulation and prediction of Shanghai urban spatial change based on random forest and CA-Markov model

Abstract. In the context of rapid urbanization, accurate assessment of urban expansion has become increasingly important for urban sustainable development, and smart growth theory has been put forward to avoid urban sprawl. Previous studies about urban expansion simulation focused only on ecological constrain which prevent urban growth from developing in specific regions. However, government decision-making and urban planning greatly influence urban development and limit the disorderly expansion of the urban. In this paper, we consider planning policies into urban simulation and uses the ecological protection red line, farmland protection red line and cultural protection control line as limiting factors for future urban simulation. Choosing Shanghai as the study area, we integrated Random Forests Algorithm (RFA), Markov chain and Cellular Automata (CA) to simulate urban expansion in 2015, and further predict the urban expansion in 2020, 2025 and 2030. The results show that the overall accuracy of urban land use simulation in 2015 is 93.86%, and the kappa coefficient is 0.8577. The model has a good simulation effect. Furthermore, the predicted results in 2020, 2025 and 2030 show that the urban land area in Shanghai is still increasing, and the spatial distribution of urban land has obvious circle structure and regional differences. The urban areas within 10km from the city center are growing slowly, and the region within 30km from the city center is growing faster, and there are more new urban points from 2025 to 2030. But in the area 30km away from the city center, different administrative areas show different urban growth phenomena. Among them, there are a large number of new urban points in the junction area between Songjiang District and Jinshan District, which may be the focus of future urban development planning in Shanghai. The proposed model and the results can help planners study the evolution of urban patterns and develop further urban planning.

Human-based dynamics of mental workload in complicated systems.

As a dynamic system in which different factors affect human performance via dynamic interactions, mental workload needs a dynamic measure to monitor its factors and evidence in a complicated system, an approach that is lacking in the literature. The present study introduces a system dynamics-based model for designing feedback mechanisms related to the mental workload through literature review and content analysis of the previous studies. A human-based archetype of mental workload was detected from the data collection process. The archetype is presented at various stages, including dynamic theory, behavior over time, leverage points and model verification. The real validation of the dynamic model was confirmed in an urban train simulator. The dynamic model can be used to analyze the long-term behavior of the mental workload. Decision-makers can benefit from the developed archetypes in evaluating the dynamic impact of their decisions on accident prevention in the complicated systems.

A digital workflow to quantify regenerative urban design in the context of a changing climate

The regenerative approach to design goes beyond limiting the environmental impact of the built environment and towards the enrichment of the ecosystem, adaptation to climate change, and the improvement of human health. This concept is being applied to buildings through new standards such as the Living Building Challenge, yet examples of implementation of regenerative design at the urban scale are rare. While this is a promising direction for sustainable design, in theory new metrics, design tools and workflows need to be developed to translate regenerative design concepts into practice effectively. Among other factors, barriers to implementation remain rooted in the shortcomings of existing urban simulation tools to evaluate a wide range of performance metrics simultaneously. This paper thus proposes a prototype workflow to evaluate regenerative performance using existing evaluation tools in a single digital workflow. A series of existing and customised plugins, most of which are already in use and open source, were integrated into a multi-parametric workflow based on the Grasshopper visual programming tool. The workflow was tested on Malaga as a case study and incorporated key performance indicators related to outdoor human thermal comfort, biophilia, daylight performance, and energy use and production, based on data exchange and synergies across the different tools. These indicators were evaluated for present and future climate scenarios obtained from a weather generator. This paper demonstrates the potential of this workflow to receive visual feedback on various aspects of regenerative urban design, thus enabling designers to more effectively pursue an evidence-based urban design process.

Predicting Presence in Urban Outdoor Spaces

This article explores the ability to predict contextual variations in the size of a local population using a model developed by fusing local sensed data sources. The purpose is to demonstrate that pervasive computing and data science can improve knowledge and forecasts about people–place interactions, as an alternative to urban simulations that rely upon static administrative statistics and generalized models of behavior. To demonstrate, a simple question is asked: can we forecast the size of a population in an urban open public space and how it will vary due to dynamic environmental and social conditions? To answer the question, a prediction model is developed from a year of daily WiFi device counts and sensed weather conditions.

A Belief-Desire-Intention Agent-based procedure for urban land growth simulation. A case study of Tehran Metropolitan Region, Iran

A Belief-Desire-Intention Agent-based procedure for urban land growth simulation. A case study of Tehran Metropolitan Region, Iran

Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan

In this research, the SLEUTH urban growth model is calibrated and validated for the first time to post Soviet Union cities. The aim of the study is to monitor, assess, simulate and compare the spatiotemporal urban growth dynamics and spatial patterns of the two largest cities Almaty and Astana using free remote sensing data. The urban expansion metrics and SLEUTH model are used to assess the urban growth dynamics. Though the capital has been moved to Astana from Almaty in 1998, Almaty is still developing faster than Astana. The urban growth simulation results from SLEUTH show Astana will surpass the urban growth of Almaty to emerge as the largest city in Kazakhstan by 2030. Astana may experience more leapfrog and ribbon developments. In Almaty, the urban growth may likely to take place in north and north-west parts.

A computational analysis of construction – operation – environment cost of real estate development projects

Similar to many highly populated cities in South East Asia, Ho Chi Minh City (HCMC) is the largest city of Vietnam acting as the economic driver for the whole country. Its urban life attractions and economic opportunities have drawn people across the country and the region, resulting in a high demand of housing. HCMC becomes a hot spot for many local and foreign real estate investors and developers. Many property development projects in HCMC have been rapidly increasing in recent years. However, many real estate developers usually pay more attention on profit-related criteria while neglecting sustainable aspects, partly due to lack of knowledge and an appropriate tool. A construction - operation – environment cost analysis workflow based on an urban simulation tool coupled with other scripting and modelling tools is proposed to quantify environmental cost and operational cost besides construction cost for a real estate development project in HCMC. Three alternative design scenarios are derived from a baseline at the early stage of design process originally provided by a developer. Computational analysis from these scenarios on construction cost in comparison to operational and environmental cost provides more information to the investment decision making process allowing the developer not only to meet their investment return expectation but also to take environmental impact and residents’ expense during the project’s life cycle.

The importance of gully flow modelling to urban flood simulation

ABSTRACTUrban flood simulation often ignores or simplifies the function of underground gully systems due to data and computational limitations. To discover the influence of gullies on urban flooding, a novel approach is proposed in this study, by fully-coupling a 1D gully flow model (GFM), a 1D sewer flow model (SFM), and a 2D overland flow model (OFM) to simultaneously simulate the flow exchanges between surface, gullies and sewer pipes. This fully-coupled approach is compared with a simplified approach which directly introduces surface water into sewer pipes without being via gullies. The validation results show that the fully-coupled approach considerably reduces the underestimation of flood extent by 27% compared with the simplified approach. Without considering the capacity of lateral tubes between gullies and sewer pipes, the simplified approach over-drains the surface water into sewer pipes. The modelling of gully flow is crucial for correctly evaluating the efficiency of drainage systems.

The general equilibrium effects of high‐occupancy vehicle lanes on congestion, sprawl, energy use, and carbon emissions

AbstractHigh‐occupancy vehicle (HOV) lanes have been promoted to encourage carpools, reduce traffic congestion, and improve air quality. At the partial equilibrium level, commuting with three workers per automobile clearly reduces highway congestion, lowers carbon emissions, and saves energy compared with three single drivers. This paper develops a numerical urban simulation model to generate the general equilibrium effects of HOV lanes on urban spatial structure, energy use, and greenhouse gas emissions. The major findings are that while HOV lanes reduce traffic congestion and improve welfare, the fall in transportation cost leads to urban sprawl, which results in higher dwelling energy use and a larger carbon footprint. Overall, the HOV lane policy has little effect on total energy consumption and carbon emissions. This is another classic case of general equilibrium effects reversing the partial equilibrium effects of an urban policy. In contrast, a gasoline tax policy leads to less urban sprawl but is less effective at lowering energy consumption and carbon emissions. Imposing congestion tolls is a more effective tool at reducing traffic congestion, saving energy, and lowering carbon emissions.

Analyses of urban radiative field simulations

Analyses of urban radiative field simulations

Practical modelling of tidal propagation under fluvial interaction in the Mekong Delta

ABSTRACTEstuarine/river engineers often encounter a problem that it is hard to specify an upstream boundary of a delta in the numerical modeling because of lack of good quality discharge data. This paper proposes a practical procedure to simulate tidal propagation and damping over a river stretch using a commonly used shallow-water equation model without imposing an upstream discharge boundary. The interaction between tides and river flows could be easily evaluated by applying seasonally-varying Manning’s n friction values. The Hau River, one of the main streams of the Mekong River, is studied by analyzing water levels measured at three different locations in the estuary. The nearly straight-line geometry of the Hau River allows us to neglect flood plain sinuosity, making it possible to extract tidal damping induced purely by fluvial influence. A series of analyses, including spectrum analysis and hydrodynamic simulation, suggests that tidal damping in the estuary of the Mekong Delta can be readily reproduced using the numerical model with Manning’s n values of 0.025–0.032 [s m−1/3] for the flood season and 0.018–0.025 for the dry season. The present study demonstrates that changes in Manning’s n value according to river flow conditions result in more reliable estimations than simply using a constant value throughout the year. The proposed procedure will be of great benefit not only for tidal modeling but also river and urban flood simulations, saline intrusion modeling and sea-level rise projections in the estuary of the Mekong River.

Microsimulating labour market job-worker matching

This paper introduces a new agent-based microsimulation (ABM) model of urban labour markets, in which workers actively seeking employment in each time period are matched with vacant jobs. The model is designed to operate within the Integrated Land Use, Transportation, Environment (ILUTE) urban simulation model system. In the current model application, 1986 is taken as the base year, with 20-year simulations being run (1986–2006) to test the model’s performance within a known historical time-period.

The General Equilibrium Effects of HOV Lanes on Congestion, Sprawl, Energy Use, and Carbon Emissions

High-occupancy vehicle (HOV) lanes have been promoted to encourage carpools, reduce traffic congestion, and improve air quality. At the partial equilibrium level, commuting with three workers per automobile clearly reduces highway congestion, lowers carbon emissions, and saves energy compared to three single drivers. This paper develops a numerical urban simulation model to generate the general equilibrium effects of HOV lanes on urban spatial structure, energy use, and greenhouse gas emissions. The major findings are that while HOV lanes reduce traffic congestion and improve welfare, the fall in transportation cost leads to urban sprawl, which results in higher dwelling energy use and a larger carbon footprint. Overall, the HOV lane policy has little effect on total energy consumption and carbon emissions. This is another classic case of general equilibrium effects reversing the partial equilibrium effects of an urban policy. In contrast, a gasoline tax policy leads less urban sprawl but is less effective at lowering energy consumption and carbon emissions. Imposing congestion tolls is a more effective tool at reducing traffic congestion, saving energy, and lowering carbon emissions.

Modeling urban growth in a metropolitan area based on bidirectional flows, an improved gravitational field model, and partitioned cellular automata

ABSTRACTSimulating urban landscape dynamics in metropolitan areas has attracted much attention lately, but the difficulty remains. Although large-scale urban simulation studies consider spatial interaction as an important factor, spatial interaction cannot be accurately measured based on a single element flow, and its effects may not strictly follow a distance decay function. Furthermore, different cities may require different transition rules. In this study, we combined bidirectional flows of population and information and an improved gravitational field model to model the urban spatial interaction, and we then integrated a partitioned cellular automata (CA) model to simulate the urban growth for different cities in the Yangtze River middle reaches megalopolis. It was found that the simulation results generated by the CA model considering spatial interaction are significantly improved. Furthermore, partitioned conversion thresholds can effectively improve the model performance. The proposed model showed a much better performance in the simulation of subordinate cities surrounding the core cities, than for the core cities and fringe cities. We suggest that large-scale urban simulation should pay more attention to the development of partitioned transition rules. The effects of intercity urban flows should also be considered in the simulation of small- and medium-sized cities near the regional cores.

Urban Influences on the Spatiotemporal Characteristics of Runoff and Precipitation during the 2009 Atlanta Flood

AbstractThe 2009 Atlanta flood was a historic event that resulted in catastrophic damage throughout the metropolitan area. The flood was the product of several hydrometeorological processes, including moist antecedent conditions, ample atmospheric moisture, and mesoscale training. Additionally, previous studies hypothesized that the urban environment of Atlanta altered the location and/or overall quantities of precipitation and runoff that ultimately produced the flood. This hypothesis was quantitatively evaluated by conducting a modeling case study that utilized the Weather Research and Forecasting Model. Two model runs were performed: 1) an urban run designed to accurately depict the flood event and 2) a nonurban simulation where the urban footprint of Atlanta was replaced with natural vegetation. Comparing the output from the two simulations revealed that interactions with the urban environment enhanced the precipitation and runoff associated with the flood. Specifically, the nonurban model underestimated the cumulative precipitation by approximately 100 mm in the area downwind of Atlanta where urban rainfall enhancement was hypothesized. This notable difference was due to the increased surface convergence observed in the urban simulation, which was likely attributable to the enhanced surface roughness and thermal properties of the urban environment. The findings expand upon previous research focused on urban rainfall effects since they demonstrate that urban interactions can influence mesoscale hydrometeorological characteristics during events with prominent synoptic-scale forcing. Finally, from an urban planning perspective, the results highlight a potential two-pronged vulnerability of urban environments to extreme rainfall, as they may enhance both the initial precipitation and subsequent runoff.

A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

Predicting the future urban growth and it's impacts on the surrounding environment using urban simulation models: Case study of Ibb city – Yemen

This paper aims at predicting the future urban growth and its impact on the Yemeni city of Ibb. It adopts a combined urban simulation model using both Cellular Automata (CA) and Fuzzy Set, integrated in a Geographic Information Systems (GIS) platform. The research methodology includes three main stages: (1) preparation of historical data on land uses (2003, 2013), (2) simulation of data using urban simulation models (CA & Fuzzy set) in LanduseSim software environment, and (3) visualizing data. The model was validated using the pixel matching method for both the simulation map and actual map for the year of 2013. The match ratio was 93.76% for all layers and 89.40% for the urban layer. Accordingly, the final simulation was completed to the year 2033. The results show a horizontal distribution of urban growth with a high percentage of increase in urban areas; from 28.41% in 2013 to 43.11% in 2033. This increase occurs at the expense of agricultural and natural land. The study recommends a reconsideration of the the city expansion strategies by decision makers in government in order to ensure ecological balance.

CELLULAR AUTOMATA MODELLING APPROACH FOR URBAN GROWTH

Cellular Automaton (CA) consists of a regular grid cells of which states change according to simple repetitive rules regulated by their contiguous and adjacent cells, which often expresses an unexpected complexity. Thus, CA is one of the major techniques to imitate and/or assess complex behaviors of natural systems. CA can be applied to physical and biological phenomena, such as turbulence in fluid, patterns of biological growth, and wildfire, and also some human-induced phenomena such as urban growth that is the main target of this review. In 1970s, cellular approach was initially adopted in geography, showing the clue to the urban growth application. To overcome the limitation or constraints the conventional standard cell-space models inherently include, alternative formulations were theoretically proposed in 1980s. And the pioneering work applied to realistic cities was conducted in 1990s. Subsequently, numerous models have been presented by relaxing original rules to express reality and by introducing some additional techniques such as geographical information system and system dynamics, thus far. This paper reviews 87 published cellular automata studies on urban growth simulations, urban land use change assessments, urban planning and related information from 18 countries, and examines the characteristics of each relaxation method. In addition, the scale problems are frequently discussed in the validation of the CA model is addressed.

Integrating Performance-Based Engineering and Urban Simulation to Model Post-Earthquake Housing Recovery

The efficacy of various types of intervention measures intended to facilitate post-earthquake housing recovery can be evaluated ahead of time by using simulation models to quantify their benefits and tradeoffs. Towards this end, this paper presents a conceptual framework comprised of three components for modeling post-earthquake housing recovery. The modeling framework starts with a probabilistic assessment of building-level damage using recovery-based limit states that characterize post-earthquake functionality, inhabitability, and repairability. These limit states are the basis for the second component, which includes two different utility-based models for representing post-earthquake household decision making. Stochastic models to probabilistically quantify building-level recovery trajectories comprise the third and final component of the framework. Collectively, these alternative models can integrate the effect of building states, available resources, household decisions, and endogenous factors such as lifeline restoration. The modeling framework can be scaled to model spatiotemporal scenarios of housing recovery to inform jurisdictional-level policies, plans, and interventions to increase residential community resilience.

Modeling Patterns of Land Use in Chinese Cities Using an Integrated Cellular Automata Model

This paper introduces an urban growth simulation model applied to the full scope of China. The model uses a multicriteria decision analysis to calculate the land conversion probability and then integrates it with a cellular automata model. A nonlinear relationship is incorporated in to the model to interpret the impacts of different Land Use and Cover Change driving forces. The Analytical Hierarchical Process is also implemented to compute the variance between weights of different factors. Multiple sizes of neighborhood and different urban ratios in the model rules are tested, and a 5 × 5 neighborhood and an urban threshold of 0.33 are chosen. The study demonstrates the importance of spatial analysis on socioeconomic factors, population, and Gross Domestic Product in land use change simulation modeling. The model fills the gap between the purely economic theory simulation model and the geographic simulation model. The nationwide urban simulation is an example that addresses the lack of urban simulation studies in China and among large-scale simulation models.