Introducing a novel model of belief–desire–intention agent for urban land use planning

Land use planning is a potentially demanding search and optimization task that has been challenged by numerous researchers in the field of spatial planning. Agent and multi-agent systems are examples of the modern concepts, which have been gaining more attention in challenging spatial issues recently. Although the efficiency of belief, desire, and intention (BDI) architecture of agents is validated in varieties of sciences, its uses in Geospatial Information Systems (GIS) and specifically among spatial planners is still burgeoning. In this paper, we attempted to integrate the concepts of BDI agent architecture into spatial issues; as a result, a novel spatial agent model is designed and implemented to analyze the urban land use planning. The proposed approach was checked in urban land use planning problems using a case study in a municipal area. The result of implementation showed the effects of spatial agents' behaviors such as intention, commitment, and interaction on their decision.     

Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques

The study focuses on mapping spatiotemporal patterns and detecting the potential drivers of traffic congestion with multi-source data. First, based on real-time traffic data retrieved from an online map, the k-means clustering algorithm was applied to classify the spatiotemporal distribution of congested roads. Then, we applied a geographical detector (Geo-detector) to mine the potential factors for each spatiotemporal pattern. The results showed six congestion patterns for intra-regional roads and inter-regional roads on weekdays. On both intra-regional and inter-regional roads, congestion density reflected by building height was the strongest indicator during the morning peak period. Public facilities such as hospitals, tourist sites and green spaces located near areas of employment or residential areas contributed to congestion during and off-peak hours. On intra-regional roads, the sparse road network and greater distance from the city center contribute to congestion during peak hours. On inter-regional roads, the number of bus stops contributed most to the early evening peak congestion, while the design of the entrances to large buildings in mixed business areas and public service areas increased the level of congestion. The results suggest that land use should be more mixed in high-density areas as this would reduce the number of trips made to the city center. However, mixed land-use planning should also be combined with a detailed design of the microenvironment to improve accessibility for different travel modes in order to increase the efficiency of traffic and reduce congestion. The innovative approach can be potentially applied in traffic congestion and land use planning studies elsewhere based on real-time multi-source data.     

An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data

Remote sensing data from both Landsat 5 and Landsat 7 systems were utilized to assess urban area thermal characteristics in Tampa Bay watershed of west-central Florida, and the Las Vegas valley of southern Nevada. To quantitatively determine urban land use extents and development densities, sub-pixel impervious surface areas were mapped for both areas. The urban-rural boundaries and urban development densities were defined by selecting certain imperviousness threshold values and Landsat thermal bands were used to investigate urban surface thermal patterns. Analysis results suggest that urban surface thermal characteristics and patterns can be identified through qualitatively based urban land use and development density data. Results show the urban area of the Tampa Bay watershed has a daytime heating effect (heat-source), whereas the urban surface in Las Vegas has a daytime cooling effect (heat-sink). These thermal effects strongly correlated with urban development densities where higher percent imperviousness is usually associated with higher surface temperature. Using vegetation canopy coverage information, the spatial and temporal distributions of urban impervious surface and associated thermal characteristics are demonstrated to be very useful sources in quantifying urban land use, development intensity, and urban thermal patterns.     

Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation

Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration.This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures.The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions.     

Impacts of urban form on air quality in metropolitan areas in the United States

Among 221 metropolitan areas (MAs) in the United States (US), this study explored the impact of urban form, either urban compactness or urban sprawl, on two types of air quality in 2014: NO_x emissions from road traffic and annual average NO2 concentrations. Urban form was quantified using Smart Growth America (SGA) sprawl indexes with density, land use mixing, centeredness, and street connectivity. NO_x emissions from road traffic were derived from the National Emissions Inventory (NEI). Through modeling NO2 concentrations using land use regression (LUR), with satellite-based estimates and kriging, this study measured NO₂ concentrations within MAs in the US The study results showed that higher levels of urban form scores (i.e., higher compactness) and land use mixing were associated with lower per-person NO_x emissions from road traffic. In addition, higher levels of centeredness were associated with lower NO₂ concentrations, but the effect was moderate. On the other hand, regional rainfall and solar insolation had more significant associations with NO₂ concentrations than metropolitan urban form. Meanwhile, localized emissions sources had significant associations with local-level NO₂ concentrations. This study provides additional evidence on the relationship between urban form and air quality in the US MAs. The study suggests that high compactness-oriented development and the reduction of localized emission sources may be effective in reducing NO_x emissions from road traffic and local NO₂ concentrations, respectively. However, future studies need to explore the impact of urban form at both the MA and local levels on NO₂ concentrations and develop a more accurate national NO₂ concentration prediction model.     

Urban growth modeling of Kathmandu metropolitan region, Nepal

The complexity of urban system requires integrated tools and techniques to understand the spatial process of urban development and project the future scenarios. This research aims to simulate urban growth patterns in Kathmandu metropolitan region in Nepal. The region, surrounded by complex mountainous terrain, has very limited land resources for new developments. As similar to many cities of the developing world, it has been facing rapid population growth and daunting environmental problems. Three time series land use maps in a fine-scale (30 m resolution), derived from satellite remote sensing, for the last three decades of the 20th century were used to clarify the spatial process of urbanization. Based on the historical experiences of the land use transitions, we adopted weight of evidence method integrated in cellular automata framework for predicting the future spatial patterns of urban growth. We extrapolated urban development patterns to 2010 and 2020 under the current scenario across the metropolitan region. Depending on local characteristics and land cover transition rates, this model produced noticeable spatial pattern of changes in the region. Based on the extrapolated spatial patterns, the urban development in the Kathmandu valley will continue through both in-filling in existing urban areas and outward rapid expansion toward the east and south directions. Overall development will be greatly affected by the existing urban space, transportation network, and topographic complexity.     

基于改进遗传算法的VMS选址研究与分析

随着城市交通的高速发展,交通拥堵和交通事故频繁发生,城市交通诱导作为智能交通系统的重要组成部分,其产生和发展刻不容缓,可变信息板(VMS)是交通诱导系统中实现交通信息传递的重要工具。针对智能交通诱导系统在国内应用的不成熟,采用改进型遗传算法对VMS在路网中的布点位置进行了全局索优,对基本遗传算法中的编码方式,选择方法进行了改进,同时采用动态衰减变异概率进行变异操作,并运用多目标优化算法将多目标函数单一化,针对某一路段上的VMS对该路段下游不同位置的影响程度的不同,引入衰减影响因子;然后以复杂虚拟路网为仿真实例进行了VMS布局仿真检验,结果表明,该方法以VMS效用和经济成本为评价指标,较好地实现了交通路网中VMS选址的最优分布,同时达到了节约资源的目的,具有一定的科学性和实用性。     

基于道路风险评估的城市路网实时路径选择

为了缓解城市交通拥堵、避免交通事故的发生,城市路网的路径选择一直以来是一个热门的研究课题.随着边缘计算和车辆智能终端技术的发展,城市路网中的行驶车辆从自组织网络朝着车联网(Internet of vehicles,IoV)范式过渡,这使得车辆路径选择问题从基于静态历史交通数据的计算向实时交通信息计算转变.在城市路网路径选择问题上,众多学者的研究主要聚焦如何提高出行效率,减少出行时间等.然而这些研究并没有考虑所选路径是否存在风险等问题.基于以上问题,首次构造了一个基于边缘计算技术的道路风险实时评估模型(real-time road risk assessment model based on edge computing, R³A-EC),并提出基于该模型的城市路网实时路径选择方法(real-time route selection method based on risk assessment, R²S-RA). R3A-EC模型利用边缘计算技术的低延迟,高可靠性等特点对城市道路进行实时风险评估,并利用最小风险贝叶斯决策验证道路是否存在风险问...     

A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery

“Urban Sprawl” is a growing concern of citizens, environmental organizations, and governments. Negative impacts often attributed to urban sprawl are traffic congestion, loss of open space, and increased pollutant runoff into natural waterways. Definitions of “Urban Sprawl” range from local patterns of land use and development to aggregate measures of per capita land consumption for given contiguous urban areas (UA). This research creates a measure of per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population of 50,000 or greater. Nighttime satellite imagery obtained by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is used as a proxy measure of urban extent. The corresponding population of these urban areas is derived from a grid of the block group level data from the 1990 U.S. Census. These numbers are used to develop a regression equation between Ln(Urban Area) and Ln(Urban Population). The ‘scale-adjustment’ mentioned in the title characterizes the “Urban Sprawl” of each of the urban areas by how far above or below they are on the “Sprawl Line” determined by this regression. This “Sprawl Line” allows for a more fair comparison of “Urban Sprawl” between larger and smaller metropolitan areas because a simple measure of per capita land consumption or population density does not account for the natural increase in aggregate population density that occurs as cities grow in population. Cities that have more “Urban Sprawl” by this measure tended to be inland and Midwestern cities such as Minneapolis-St. Paul, Atlanta, Dallas-Ft. Worth, St. Louis, and Kansas City. Surprisingly, west coast cities including Los Angeles had some of the lowest levels of “Urban Sprawl” by this measure. There were many low light levels seen in the nighttime imagery around these major urban areas that were not included in either of the two definitions of urban extent used in this study. These areas may represent a growing commuter-shed of urban workers who do not live in the urban core but nonetheless contribute to many of the impacts typically attributed to “Urban Sprawl”. “Urban Sprawl” is difficult to define precisely partly because public perception of sprawl is likely derived from local land use planning decisions, spatio-demographic change in growing urban areas, and changing values and social mores resulting from differential rates of international migration to the urban areas of the United States. Nonetheless, the aggregate measures derived here are somewhat different than similar previously used measures in that they are ‘scale-adjusted’; also, the spatial patterns of “Urban Sprawl” shown here shed some insight and raise interesting questions about how the dynamics of “Urban Sprawl” are changing.     

The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises

The relationship between location and land use patterns is one of the classic theoretical issues in urban studies. Classic models based on the monocentricity hypothesis have limitations in the interpretation of modern urban structure. China has experienced institutional transformation in recent decades, and the interaction of national government policy, market forces and the natural environment has influenced urban planning in Chinese metropolises, resulting in urban structures with special characteristics. This paper examines the distribution of location and land use intensity, and tested the Alonso model by the relationship between them in five Chinese metropolises using Point of Interest data, space syntax methodology, the grid weighted statistical method and the Geographically Weighted Regression (GWR) model. Universal patterns about the scaling relation between intensity of land use types and the centrality of location are revealed. The elasticity of land use types to location, from high to low sensitivity, is commercial, residential then industrial land in most of the five metropolises studied. The sensitivity sequence of land use studied suggests that the hypothetical model based on the classical Alonso model can explain the spatial structure of modern metropolises in China to some extent, especially for the commercial land. But the order of sensitivity of residential land and industrial land to location does not conform to the model. The spatial heterogeneity in land use intensity and centrality were explored and the factors embedded were discussed. It can be found that the relation between centrality and land use intensity conforms to power law. In most of the metropolises studied, when the scaling relation between land use intensity and centrality is super linear, the sequence of the frequency value from high to low are commercial, residential and industrial land; when the scaling relation is sublinear, the sequence of the frequency value is industrial, residential and commercial land.     

A Land Evaluation Model (LEM): concept, design, and application

Land use decisions become more important as growing populations exert increasing demands on available land for agriculture, recreation, urbanization and forestry. These decisions should be based on a comprehensive evaluation of all possible land uses. The LEM will make such an evaluation possible. It considers not only land capability but other relevant issues, such as society's long-range objectives regarding land, energy supplies, domestic food requirements, urban expansion, international commodity demands and balance of payment goals which influence policy decisions on land use. The LEM allows planners and policy-makers to explore regional objectives and determine the strategic importance of various uses for certain land areas. When fully developed, LEM will be able to study these potential changes and examine how these changing conditions might alter land use options.     

Revealing deep semantic commercial patterns: Insights from urban landscape depiction

Commercial functions play a significant role in economic growth and sustainable development. Influenced by socioeconomic changes which were investigated based on commercial behaviours using big geo-data, urban environment shows polycentric structures and further leads to more complex commercial landscapes. Thus, research considered environmental consequences of urban growth to better understand the commercialization process. However, only general commercial areas or certain commercial retail locations were investigated with the effects of urban environmental landscapes in existing studies. The decomposition of the commercial patterns such as accommodation and catering functions remains undiscussed. To fill this gap, this paper proposes a framework to decompose commercial patterns based on insight from different urban landscapes. First, we depict urban landscapes using spatial metrics, including the density of points of interest (POIs), percentage of landscape of buildings, landscape shape index of buildings and density of roads. Then, we examine the spatial distribution of the decomposed commercial patterns by delineating potential deep semantic topics. Finally, a structural equation model is utilised to determine the relations among urban landscapes and commercial patterns. The results show that four types of urban landscapes exhibit different spatial patterns, revealing various perspectives of commercial characteristics. Meanwhile, the decomposed commercial patterns, including those for catering and accommodation functions, display a heterogeneous distribution. These commercial patterns are directly affected by various configurations of buildings, POIs and roads. On this basis, suggestions are offered to improve commercial pattern development, including integrating urban landscape construction, organising the commercial pattern distribution and enhancing the harmony between the urban environment and commercial land uses.     

Spatialization of China’s Population Data based on Multi-source Data

The population data is usually calculated by administrative districts. When comprehensive analysis is carried out with other spatial data, there are often many problems such as different scales. The spatial data of population data is the process of exploring the spatial distribution of population. The final result can be other spatial correlation research. It lays the foundation and can also be directly applied to disaster impact assessment and other fields, which has important academic significance and application value. The population spatialization model is based on the spatial relationship between population and land use and traffic road network data. The population is divided into urban population and rural population. The land use type is empowered by the relative weight method, and the traffic network data is used to correct it. Spatial distribution of rural population; analysis of different ways of affecting different levels of traffic road network data, comprehensive distribution of urban population, and finally through the planning and other methods to superimpose and integrate different types of data. The results of population data spatialization results show that China's population is very sensitive to altitude and topography. Low-altitude plains and terraces only account for 16.49% of China's land area, while the population living on it accounts for 54.88% of the total population. On the contrary, China's mountains The proportion of hills is 55.78%, but only 22.11% of the population is distributed. The eastern population is concentrated in the Huanghuaihai area, the Sichuan Basin, the Yangtze River small and medium-sized tourist, and the Northeast Plain. The urban population in China presents a typical “point-axis” distribution. The distribution along the main traffic lines is more prominent.     

基于Storm的高速公路实时交通指数评估方法的研究与实现

随着高速公路飞速的发展,出现的拥堵增加、监控困难等问题也日益严峻。首先本文在北京市城市交通拥堵指数的基础上,提出了利用交通指数对高速公路运行状况进行评估的方法,然后基于Storm大数据平台,实现了交通指数评估系统中的数据处理、分析、指数后台计算关键算法,最后通过手机APP、微信公众号等形式,实时地发布高速公路交通指数。实验结果表明：高速公路交通指数不仅可以反映整体的高速运行状况,也可以反映特定高速或者特定高速区间的实时拥堵情况,还可以实时监测高速路网的拥堵情况,为居民出行提供参考,对缓解道路拥堵起到积极作用。     

多源数据融合的中国人口数据空间化研究

人口数据通常是以行政区为单元进行统计,在与其他空间数据进行综合分析时往往存在尺度不一等诸多问题。人口数据空间化研究是探究人口空间分布规律的过程,其最终成果可为人口分布与其他数据的空间关联研究奠定基础,同时也可直接应用于灾害影响评估等领域,具有重要的学术意义和应用价值。基于人口分布和土地覆盖、交通路网数据的空间关系构建人口空间化模型,将人口划分为城镇人口和农村人口,通过相关权重法（Relative Weight）对土地覆盖类型赋权,利用交通路网数据修正,对农村人口进行空间展布;分析不同等级交通路网数据影响方式的不同,对城镇人口进行展布,最后通过规划求解等方法将不同类型数据叠加融合。人口数据空间化结果表明：我国人口分布对海拔地形十分敏感,低海拔平原和台地面积仅占我国国土面积的16.49%,而生活在其上的人口却占总人口的54.88%,相反,我国山地丘陵面积占比为55.78%,但是仅分布22.11%的人口;东部人口集中于黄淮海地区、四川盆地、长江中小游以及东北平原等地;中国城镇人口呈现典型的“点轴”分布特点,沿主要交通线的集中分布比较明显。     

基于排队长度均衡的交叉口信号配时优化策略

城市交通的快速发展和交通需求的不断增长,使得城市交通拥堵日益严重,由此造成的时间延误和经济损失越来越大.鉴于此,提出了基于排队长度均衡的交叉口信号配时优化策略,根据排队长度均衡的控制思想,实时动态调整各相位绿灯时间,以达到排队长度均衡的控制目标,保证了绿灯时间的充分利用.通过近似动态规划方法的引入,使得该算法具有自学习和自适应的特性,不依赖于交通流模型.仿真结果验证了算法的有效性.     

SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

In order to alleviate traffic congestion for vehicles in urban networks, most of current researches mainly focused on signal optimization models and traffic assignment models, or tried to recognize the interaction between signal control and traffic assignment. However, these methods may not be able to provide fast and accurate route guidance due to the lack of individual traffic demands, real-time traffic data and dynamic cooperation between vehicles. To solve these problems, this paper proposes a dynamic and real-time route selection model in urban traffic networks (DR2SM), which can supply a more accurate and personalized strategy for vehicles in urban traffic networks. Combining the preference for alternative routes with real-time traffic conditions, each vehicle in urban traffic networks updates its route selection before going through each intersection. Based on its historical experiences and estimation about route choices of the other vehicles, each vehicle uses a self-adaptive learning algorithm to play congestion game with each other to reach Nash equilibrium. In the route selection process, each vehicle selects the user-optimal route, which can maximize the utility of each driving vehicle. The results of the experiments on both synthetic and real-world road networks show that compared with non-cooperative route selection algorithms and three state-of-the-art equilibrium algorithms, DR2SM can effectively reduce the average traveling time in the dynamic and uncertain urban traffic networks.     

Urban traffic congestion estimation and prediction based on floating car trajectory data

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-scale urban areas. Recently, some estimation and prediction methods have been proposed to predict the traffic congestion with respect to different metrics such as accuracy, instantaneity and stability. Nevertheless, there is a lack of unified method to address the three performance aspects systematically. In this paper, we propose a novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently. In this method, floating cars are regarded as mobile sensors, which can probe a large scale of urban traffic flows in real time. In order to estimate the traffic congestion, we make use of a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows. To predict the traffic congestion, an innovative traffic flow prediction method using particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion state. Experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.     

Measuring accessibility of urban scales: A trip-based interaction potential model

In the interaction process of city space and citizens’ activity pattens, the regular travel behavior under the cumulative impacts of urban land use is required to be assessed. The aim of this study is to formulate and estimate attraction choice models that provide measurements of accessibility on various scales reflecting the choice of people to travel to facilities or activity places and characterize the interaction between land use patterns and transportation facilities. Based on a dataset inclusive of big data from varied sources, measurement methodologies are proposed encompassing the multidimensional aspect of the accessibility estimation issues. We sketch the characteristics of service facilities and travel impedance in the calibration processes. Logit models and gravity models are applied to simulate the impacts of different scales of trip length on the accessibility scores. Accessibility is aggregated over spatial elements of different scales and trips and herein the spatial accessibility of study zones is estimated as well as the potential of citizens’ travel choices and activity patterns. Such spatial interaction models have potential implication for enhancing our understanding of the cumulative environmental influences on citizen’s travel behavior and vice versa. It can be a substantial part of a more composed proposal of life convenience of residential citizens reflecting the happiness of living in an urban community.     

遗传算法在土地利用优化中的应用

城市土地利用优化是从社会的长远与整体利益的角度,来规范一定时段内城市土地利用的结构、布局和方式,是一个典型的多目标优化问题.本文利用遗传算法的全局优化搜索能力,构建了一个城市土地利用的优化模型.该模型采用与土地利用现状图相对应的二维矩阵编码,利用层次分析法构建了顾及土地功能协调性、土地比例协调性、人口、交通4个方面的多目标适应度函数.应用该模型对某城市的城市土地利用空间结构进行了优化配置.结果表明,该模型能够反映城市土地利用的客观规律,优化后的方案能很好地符合城市规划上的各种需要.