Marie Reinbigler, Romain Rouffet, Peter Naylor
et al.
The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult to study in real cities using sensor data (satellites or in-situ stations) due to their coarse spatial and temporal resolution. Among the factors contributing to this effect are the properties of urban materials, which differ from those in rural areas. To analyze their individual impact and to test new material configurations, a high-resolution simulation at the city scale is required. Estimating the current materials used in a city, including those on building facades, is also challenging. We propose HeatMat, an approach to analyze at high resolution the individual impact of urban materials on the UHI effect in a real city, relying only on open data. We estimate building materials using street-view images and a pre-trained vision-language model (VLM) to supplement existing OpenStreetMap data, which describes the 2D geometry and features of buildings. We further encode this information into a set of 2D maps that represent the city's vertical structure and material characteristics. These maps serve as inputs for our 2.5D simulator, which models coupled heat transfers and enables random-access surface temperature estimation at multiple resolutions, reaching an x20 speedup compared to an equivalent simulation in 3D.
Francesco Marzolla, Matteo Bruno, Hygor P. M. Melo
et al.
In the quest for more environmentally sustainable urban areas, the concept of the 15-minute city has been proposed to encourage active mobility, primarily through walking and cycling. An urban area is considered a ``15-minute city" if every resident can access essential services within a 15-minute walk or bike ride from their home. However, there is an ongoing debate about the effectiveness of this model in reducing car usage and carbon emissions. In this study, we conduct a large-scale data-driven analysis to evaluate the impact of service proximity to homes on CO$_2$ emissions. By examining nearly 400 cities worldwide, we discover that, within the same city, areas with services located closer to residents produce less CO$_2$ emissions per capita from transportation. We establish a clear relationship between the proximity of services and CO$_2$ emissions for each city. Additionally, we quantify the potential reduction in emissions for 30 cities if they optimise the location of their services. This optimisation maintains each city's total number of services while redistributing them to ensure equal accessibility throughout the entire urban area. Our findings indicate that improving the proximity of services can significantly reduce expected urban emissions related to transportation.
Aysan Mokhtarimousavi, Michael Kleiss, Mostafa Alani
et al.
This paper presents a study of computation and morphology of Louis Kahn City Tower project. The City Tower is an unbuilt design by Louis I. Kahn and Anne Tyng that integrates form and structure using 3D space triangular geometries. Although never built, the City Tower geometrical framework anticipated later developments in design of space-frame structures. Initially envisioned in the 1950s, the City Tower project is a skyscraper structure based on a tetrahedral and octahedral space frame called Octet-Truss. The aim of this study is to analyze the geometry of the City Tower structure and how it can be used to develop modular and adaptable architectural forms. The study is based on an analytical shape grammar that is used to recreate the original structure, and later to generate new structural configurations based on the City Tower's morphology. This study also investigates the potential applications of these findings in architecture and reveals the possibilities of using tetrahedrons and octahedrons as fundamental geometries for creating scalable and modular designs and presents initial findings.
This book examines the economic reasons why people choose to live where they live and develops, through analysis of the bid rent function, a unified theory of urban land use and city size. The first part of the book explicates the basic theory of urban land use and optimal city size. Residential location behavior of households is examined in a microeconomic framework and equilibrium and optimal patterns of residential land use are discussed. The corresponding equilibrium and optimal city sizes are studied in a variety of contexts. Part Two extends the classical theories of von Thunen and Alonso with the addition of externality factors such as local public goods, crowding and congestion, and racial prejudice. The rigorous mathematical approach and theoretical treatment of the material make Urban Economic Theory of interest to researchers in urban economics, location theory, urban geography, and urban planning.
Luis Sanchez, Luis Muñoz, Jose Antonio Galache
et al.
This paper describes the deployment and experimentation architecture of the Internet of Things experimentation facility being deployed at Santander city. The facility is implemented within the SmartSantander project, one of the projects of the Future Internet Research and Experimentation initiative of the European Commission and represents a unique in the world city-scale experimental research facility. Additionally, this facility supports typical applications and services of a smart city. Tangible results are expected to influence the definition and specification of Future Internet architecture design from viewpoints of Internet of Things and Internet of Services. The facility comprises a large number of Internet of Things devices deployed in several urban scenarios which will be federated into a single testbed. In this paper the deployment being carried out at the main location, namely Santander city, is described. Besides presenting the current deployment, in this article the main insights in terms of the architectural design of a large-scale IoT testbed are presented as well. Furthermore, solutions adopted for implementation of the different components addressing the required testbed functionalities are also sketched out. The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions.
With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) Application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.
Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions. Although existing approaches typically focus on top-down optimization or bottom-up community planning, only some frameworks successfully integrate both perspectives. Our methodology employs a two-tier approach: First, a deterministic solver optimizes basic infrastructure requirements in the city region. Second, four specialized planning agents, each representing distinct sub-regions, propose demographic-specific modifications to a master planner. The master planner then evaluates and integrates these suggestions to ensure cohesive urban development. We validate our framework using a newly created dataset comprising detailed region and sub-region maps from three developing cities in India, focusing on areas undergoing rapid urbanization. The results demonstrate that this hybrid approach enables more nuanced urban development while maintaining overall city functionality.
Ashland City, Tennessee, located within the Lower Cumberland Sycamore watershed, is highly susceptible to flooding due to increased upstream water levels. This study aimed to develop a robust flood prediction model for the city, utilizing water level data at 30-minute intervals from ten USGS gauge stations within the watershed. A Gated Recurrent Unit (GRU) network, known for its ability to effectively process sequential time-series data, was used. The model was trained, validated, and tested using a year-long dataset (January 2021-January 2022), and its performance was evaluated using statistical metrics including Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Percent Bias (PBIAS), Mean Absolute Error (MAE), and Coefficient of Determination (R^2). The results demonstrated a high level of accuracy, with the model explaining 98.2% of the variance in the data. Despite minor discrepancies between predicted and observed values, the GRU model proved to be an effective tool for flood prediction in Ashland City, with potential applications for enhancing disaster preparedness and response efforts in Ashland City.
As the United States is witnessing elevated racial differences pertaining to economic disparities, we have found a unique example contrary to the traditional narrative. Idaho is the only US state where Blacks earn more than Whites and all other races. In this paper, we examine how Idaho Blacks might have achieved economic success and, more importantly, what factors might have led to this achievement in reducing racial and economic disparities. Preliminary research suggests that fewer barriers to land ownership, smaller populations, well-knit communities, men's involvement in the family, and a relatively less hostile environment have played a significant role. Further research by historians can help the nation uncover the underlying factors to see if some factors are transportable to other parts of the country.
This study aims to find out the current status and prospects of using a bicycle as a mode for commuting within Dhaka city. Bicycling is a very sustainable mode of transport but unfortunately is used very less by the commuters of Dhaka. There has been a lot of factors affecting the choice of bicycle to commute. This study was aimed to find out what factors could motivate the commuters of Dhaka to use a bicycle as a mode of transport. For determining the motivators, a survey was administered among the commuters of Dhaka city in which the respondents were asked to answer how certain factors would affect their choice to use a bicycle to commute. A Likert scale was used in the survey and the responses were analyzed, from which the top motivators were found. The Motivators were then grouped together using exploratory factor analysis to support possible policy making. Four factors were extracted using the method. The factors were named Additional Perks, General benefits, Personal Benefits, and Infrastructural benefits
Tomoya Mori, Takashi Akamatsu, Yuki Takayama
et al.
City-size distributions follow an approximate power law in various countries despite high volatility in relative city sizes over time. Our empirical evidence for the United States and Japan indicates that the scaling law stems from a spatial fractal structure owing to the coordination of industrial locations. While the locations of individual industries change considerably over time, there is a persistent pattern in that more localized industries at a given time are found only in larger cities. The spatial organization of cities exhibits a hierarchical structure in which larger cities are spaced apart to serve as centers for surrounding smaller cities, generating a recursive pattern across different spatial scales. In our theoretical replication of the observed regularities, diversity in scale economy among industries induces diversity in their location pattern, which translates into diversity in city size via spatial coordination of industries and population. The city-size power law is a generic feature of Monte-Carlo samples of stationary states resulting from the spontaneous spatial fractal structure in the hypothetical economy. The identified regularities reveal constraints on feasible urban planning at each regional scale. The success or failure of place-based policies designed to take advantage of individual cities' characteristics should depend on their spatial relationships with other cities, subject to the nationwide spatial fractal structure.
This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that modeled the city of Marysville, Ohio as an example of such a city, with realistic traffic behavior, and trip requests. A simple SAV dispatcher is implemented to model the behavior of the proposed on-demand mobility service. The goal of the service is to optimally distribute SAVs along the network to allocate passengers and shared rides. The pickup and drop-off locations are strategically placed along the network to provide mobility from affordable housing, which are also transit deserts, to locations corresponding to jobs and other opportunities. The study is carried out by varying the behaviors of the SAV driving system from cautious to aggressive along with the size of the SAV fleet and analyzing their corresponding performance. It is found that the size of the network and behavior of AV driving system behavior results in an optimal number of SAVs after which increasing the number of SAVs does not improve overall mobility. For the Marysville network, which is a 9 mile by 8 mile network, this happens at the mark of a fleet of 8 deployed SAVs. The results show that the introduction of the proposed SAV service with a simple optimal shared scheme can provide access to services and jobs to hundreds of people in a small sized city.
The purpose of the present paper is to show how blockchain and IoT technologies can benefit smart city projects, which tend to spread in the context of the sharing economy. The article also aims to describe the challenges and potentialities of smart city projects. It was found that technology platforms can serve as a strategy to build the basis for product development (goods and services) and technology-based innovation.
Vehicle-to-grid (V2G) is a promising approach to solve the problem of grid-level intermittent supply and demand mismatch, caused due to renewable energy resources, because it uses the existing resource of electric vehicle (EV) batteries as the energy storage medium. EV battery design together with an impetus on profitability for participating EV owners is pivotal for V2G success. To better understand what battery device parameters are most important for V2G adoption, we model the economics of V2G process under realistic conditions. Most previous studies that perform V2G economic analysis, assume ideal driving conditions, use linear battery degradation models, or only consider V2G for ancillary services. Our model accounts realistic battery degradation, empirical charging efficiencies, for randomness in commute behavior, and historic hourly electricity prices in six cities in the United States. We model user behavior with Bayesian optimization to provide a best-case scenario for V2G. Across all cities, we find that charging rate and efficiency are the most important factors that determine EV users' profits. Surprisingly, EV battery cost and thus degradation due to cycling has little effect. These findings should help focus research on figures of merit that better reflect real usage of batteries in a V2G economy.
Vicente Acuña, María Paz Cortés, Andrew Hart
et al.
We consider an epidemic spreading in a city which is divided geographically into different districts. We introduce the reproduction matrix $\R=\bigl(R(i,j)\bigr)$ between districts, where $R(i,j)$ is the mean number of individuals in district $j$ infected by an individual from district $i$. We analyse policies of partial lockdowns of the city, that is of a set of districts, based on the study of matrix $\R$, where rows and columns corresponding to districts in lockdown are set to zero. This schema can also be applied to a country divided into regions or other appropriate units, provided the relevant information is available. We conclude by analyzing a matrix~$\R$ which was constructed for the spread of COVID-19 in Santiago, Chile, with the aid of an agent-based simulator for generating surrogate district data.
We propose ParkSage, a set of spatially-explicit algorithms for establishing parking prices that guarantee a predetermined occupancy rate over a city, and for evaluating the achieved reduction in parking search time. We apply ParkSage for establishing overnight parking prices that guarantee 85% occupation in the Israeli city of Bat Yam. Pricing by street links ensures high parking availability and close to zero cruising everywhere in the city, but is inconvenient for drivers. Establishing prices by the large and heterogeneous city quarters results in local mismatch between demand and supply, the emergence of areas with fully occupied on-street parking and a long search time for the drivers whose destinations are in these areas. We demonstrate that pricing by the medium sized Transportation Analysis Zones, which is easy enough for drivers to comprehend and abide by, is sufficient for eliminating cruising. The software for establishing and assessing performance parking prices is based on the standard municipal GIS layers of streets and parking lots and is available for free download from https://www.researchgame.net/profile/Nir_Fulman
Felipe G. Operti, Erneson A. Oliveira, Humberto A. Carmona
et al.
We show that the definition of the city boundaries can have a dramatic influence on the scaling behavior of the night-time light (NTL) as a function of population (POP) in the US. Precisely, our results show that the arbitrary geopolitical definition based on the Metropolitan/Consolidated Metropolitan Statistical Areas (MSA/CMSA) leads to a sublinear power-law growth of NTL with POP. On the other hand, when cities are defined according to a more natural agglomeration criteria, namely, the City Clustering Algorithm (CCA), an isometric relation emerges between NTL and population. This discrepancy is compatible with results from previous works showing that the scaling behaviors of various urban indicators with population can be substantially different for distinct definitions of city boundaries. Moreover, considering the CCA definition as more adequate than the MSA/CMSA one because the former does not violate the expected extensivity between land population and area of their generated clusters, we conclude that, without loss of generality, the CCA measures of light pollution and population could be interchangeably utilized in future studies.
Privacy has become a major concern in Online Social Networks (OSNs) due to threats such as advertising spam, online stalking and identity theft. Although many users hide or do not fill out their private attributes in OSNs, prior studies point out that the hidden attributes may be inferred from some other public information. Thus, users' private information could still be at stake to be exposed. Hitherto, little work helps users to assess the exposure probability/risk that the hidden attributes can be correctly predicted, let alone provides them with pointed countermeasures. In this article, we focus our study on the exposure risk assessment by a particular privacy-sensitive attribute - current city - in Facebook. Specifically, we first design a novel current city prediction approach that discloses users' hidden `current city' from their self-exposed information. Based on 371,913 Facebook users' data, we verify that our proposed prediction approach can predict users' current city more accurately than state-of-the-art approaches. Furthermore, we inspect the prediction results and model the current city exposure probability via some measurable characteristics of the self-exposed information. Finally, we construct an exposure estimator to assess the current city exposure risk for individual users, given their self-exposed information. Several case studies are presented to illustrate how to use our proposed estimator for privacy protection.
Zipf's law of city-size distributions can be expressed by three types of mathematical models: one-parameter form, two-parameter form, and three-parameter form. The one-parameter and one of the two-parameter models are familiar to urban scientists. However, the three-parameter model and another type of two-parameter model have not attracted attention. This paper is devoted to exploring the conditions and scopes of application of this Zipf models. By mathematical reasoning and empirical analysis, new discoveries are made as follows. First, if the size distribution of cities in a geographical region cannot be described with the one- or two-parameter model, maybe it can be characterized by the three-parameter model with a scaling factor and a scale-translational factor. Second, all these Zipf models can be unified by hierarchical scaling laws based on cascade structure. Third, the patterns of city-size distributions seems to evolve from three-parameter mode to two-parameter mode, and then to one-parameter mode. Four-year census data of China's cities are employed to verify the three-parameter Zipf's law and the corresponding hierarchical structure of rank-size distributions. This study is revealing for people to understand the scientific laws of social systems and the property of urban development.
The relationships between urban area and population size have been empirically demonstrated to follow the scaling law of allometric growth. This allometric scaling is based on exponential growth of city size and can be termed "exponential allometry", which is associated with the concepts of fractals. However, both city population and urban area comply with the course of logistic growth rather than exponential growth. In this paper, I will present a new allometric scaling based on logistic growth to solve the abovementioned problem. The logistic growth is a process of replacement dynamics. Defining a pair of replacement quotients as new measurements, which are functions of urban area and population, we can derive an allometric scaling relation from the logistic processes of urban growth, which can be termed "logistic allometry". The exponential allometric relation between urban area and population is the approximate expression of the logistic allometric equation when the city size is not large enough. The proper range of the allometric scaling exponent value is reconsidered through the logistic process. Then, a medium-sized city of Henan Province, China, is employed as an example to validate the new allometric relation. The logistic allometry is helpful for further understanding the fractal property and self-organized process of urban evolution in the right perspective.