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We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factors related to the urban environment, such as population density and accessibility are also crucial and have been considered in the literature. However, these considerations have typically been at the macro level. In this work we are interested in exploring the complementarity between specific (pairs) of venues. Simply put, venue B is complementary to venue A, if customers are more probable to visit venue B after being at venue A. This can increase the traffic for a business beyond the demand expected from the aforementioned factors, and it has been largely ignored in the literature. In this study we take a simulation-based approach to estimate this complementarity. We perform our simulations and analysis on two different spatial levels, namely, the venue level, as well as, the urban area level (e.g., zip code, neighborhood, etc.). The estimated complementarity provides insights for business owners and urban planners that can allow them to satisfy more demand, which consequently can increase the revenue for the businesses, but also can create more convenient urban navigation for city dwellers. [ABSTRACT FROM AUTHOR]

Smart transportation is an important application scenario in the field of urban computing. As the popularity of electric vehicles increases, the demand for fast charging is growing rapidly. In response to this, battery swapping stations are being proposed as a solution, but their operational efficiency is challenged by factors such as battery life, vehicle queues, and grid load management. In this paper, a mixed intelligent optimization strategy combining the proximal policy optimization (PPO) algorithm from reinforcement learning and the goat swarm optimization (GSO) algorithm is proposed. The GSO-PPO algorithm is constructed, where PPO algorithm learns the optimal scheduling strategy for the battery swapping station in a dynamic environment, and the GSO algorithm optimizes the hyperparameters of PPO and adjusts the weight of the reward function to achieve the multi-objective optimization of minimizing battery life, shortening vehicle waiting time, and efficiently managing grid load. The experimental results show that compared with random strategies and traditional PPO algorithms, GSO-PPO reduces vehicle waiting time and improves service efficiency, making the overall operation of the battery swapping station more stable. The study demonstrates the potential of combining reinforcement learning and swarm intelligence algorithms in smart energy infrastructure and solving multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Forecasting urban ride-hailing demand is the main duty in Intelligent Transportation Systems. Accurate predicting can significantly improve the efficiency of regional capacity allocation, promoting energy conversation and emission reduction. Currently, the mainstream approaches utilize advanced deep learning-based methods to model aspects of both temporal and spatial. However, these previous methods overlook the long-term temporal dependence of selection and the hierarchical nature of urban road networks. To this end, we propose a novel Spatial-Temporal Hierarchical Network (STHNet) for urban ride-hailing demand prediction. Specially, The Depthwise Separable Convolution Nerual Networks (DSCNNs) extract spatial features of the road network through channel-wise and point-wise convolution operations, while the Nested Long Short-Term Memory networks (NLSTMs) are used to capture the hierarchical temporal dependencies in sequential data. DSCNNs and NLSTMs are cascaded to form the basic module of the multi-step prediction framework, called Spatial-Temporal Hierarchical block. The block can be easily extended to other spatial-temporal modeling tasks. At the end of the network, we introduce a 3DCNN to learn Spatial-Temporal heterogeneity and integrate information. Furthermore, Teacher Forcing and secondary information are incorporated into STHNet to enhance training efficiency. Extensive experiment are conducted on a Xiamen transportation network with 64 regions shows that STHNet outperforms multiple State-Of-The-Art baselines. The qualitative results underscoring the practical engineering applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Traffic forecasting is an enduring research topic in the design of intelligent transportation systems and spatial-temporal data mining. Accurate prediction can help facilitate urban resource optimization and improve road efficiency. However, the complex spatial-temporal dependencies and dynamic urban conditions make it extremely challenging. Although many spatial-temporal modeling approaches have been proposed recently, they still suffer from the following three problems: (1) Inadequate modeling of temporal correlations; (2) Ignoring the fundamental fact that the location dependence of road networks changes dynamically over time; (3) Difficulty in extracting deeper spatial-temporal features layer by layer. In this paper, we propose a novel Dynamic Spatial-Temporal Attention-enhanced Network called DSTAN for traffic prediction. In DSTAN, we combine gated temporal units with trend-aware multi-head temporal attention to jointly capture local and long-range temporal dependencies. We also employ learnable node embeddings to extract heterogeneous information and integrate this with the spatial attention module to learn dynamic spatial correlations without any expert knowledge. Structurally, we stack multiple spatial-temporal blocks to improve the model’s capability to identify complex patterns. Extensive experiments have been conducted on four widely used datasets, demonstrating that our method surpasses all baseline methods while exhibiting strong interpretability. [ABSTRACT FROM AUTHOR]

Abstract We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factors related to the urban environment, such as population density and accessibility are also crucial and have been considered in the literature. However, these considerations have typically been at the macro level. In this work we are interested in exploring the complementarity between specific (pairs) of venues. Simply put, venue B is complementary to venue A, if customers are more probable to visit venue B after being at venue A. This can increase the traffic for a business beyond the demand expected from the aforementioned factors, and it has been largely ignored in the literature. In this study we take a simulation-based approach to estimate this complementarity. We perform our simulations and analysis on two different spatial levels, namely, the venue level, as well as, the urban area level (e.g., zip code, neighborhood, etc.). The estimated complementarity provides insights for business owners and urban planners that can allow them to satisfy more demand, which consequently can increase the revenue for the businesses, but also can create more convenient urban navigation for city dwellers.

Abstract Smart transportation is an important application scenario in the field of urban computing. As the popularity of electric vehicles increases, the demand for fast charging is growing rapidly. In response to this, battery swapping stations are being proposed as a solution, but their operational efficiency is challenged by factors such as battery life, vehicle queues, and grid load management. In this paper, a mixed intelligent optimization strategy combining the proximal policy optimization (PPO) algorithm from reinforcement learning and the goat swarm optimization (GSO) algorithm is proposed. The GSO-PPO algorithm is constructed, where PPO algorithm learns the optimal scheduling strategy for the battery swapping station in a dynamic environment, and the GSO algorithm optimizes the hyperparameters of PPO and adjusts the weight of the reward function to achieve the multi-objective optimization of minimizing battery life, shortening vehicle waiting time, and efficiently managing grid load. The experimental results show that compared with random strategies and traditional PPO algorithms, GSO-PPO reduces vehicle waiting time and improves service efficiency, making the overall operation of the battery swapping station more stable. The study demonstrates the potential of combining reinforcement learning and swarm intelligence algorithms in smart energy infrastructure and solving multi-objective optimization problems.

This survey explores the landscape of well-being research in an urban context with a focus on data. First, we conduct a review of well-being in general and efforts to measure it. Then, we perform an analysis of relevant research papers, reports, surveys, and standards published between January 2018 and September 2023 for the identification of reported factors contributing to urban well-being. These indicators were placed into distinct categories, creating a taxonomy of current efforts toward capturing urban well-being. This taxonomy, presented in a table format, details each indicator alongside its measurement method, data source, and other relevant information. Therefore, we can gain a deeper understanding of how urban well-being is assessed, what the challenges are, the current research focus, and the gaps. Our analysis reveals that urban well-being is a multifaceted construct influenced by a combination of factors. The taxonomy highlights the interconnectedness of these indicators, emphasizing that well-being results from their integration rather than isolated factors. This underscores the complexity of urban life and the need for holistic assessment methods. By providing this taxonomy, we aim to facilitate the development of more holistic approaches to measure and enhance urban well-being. This taxonomy could serve as a valuable tool for practitioners, researchers, and policymakers in the field, providing a comprehensive framework to understand and assess various aspects of capturing well-being in an urban context.

Urban sensing, as the first layer of urban computing, is the foundation of intelligent cities, generating crucial data representing city dynamics for digital applications. Aiming to solve the challenges that urban sensing is facing, this paper proposes an urban sensing system that is comprised of a theoretical framework, a technical platform and an operational model. The theoretical framework consists of six categories of content to be sensed, four sensing paradigms, and four technical challenges. The technical platform provides digital tools for managing sensors and collecting data, and supplies upper-layer applications with interfaces for using urban sensing services. Urban sensing systems can reduce redundant sensor deployment and further operational workloads. It improves the capability of urban sensing service providers in solving problems and the synergy among each other. It also generates continuous economic benefits, such as income and employment, ensuring urban sensing functions stable and sustainable. [ABSTRACT FROM AUTHOR]

Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a "one-stop shop" covering data-related issues of smart cities with references for diving deeper into particular topics of interest. [ABSTRACT FROM AUTHOR]

Data elements are the key momentum for boosting digital economy.The data generated by public services provided by governments (a.k.a.public data) is ready to be transferred into data elements, because it has been well organized in the past decade.Unfortunately, public data is strictly coupled with the systems generating them, making it difficult for different applications to share data.The process of munul data governance is lagging, heavy and inefficient, and relying on automatic extraction method can’t ensure the accuracy of data elements.To tackle these challenges, leveraging the synergy between human and machine intelligence, we propose an elementarisation method for public data based on urban knowledge system.Our method is comprised of an urban knowledge system, a set of digital controls and some machine learning algorithms.The urban knowledge system consists of entities, relationships between entities, and the properties associated with these entities and relationships, which can be used to construct different kinds of public services and form standard data representation that can be shared among different applications.Powered by the urban knowledge system, the digital controls enable governments to create different applications as public services flexibly, through a configurable way without writing any codes.Later, the information input by citizens through digital controls in these applications is transferred into data elements automatically.Finally, the machine learning algorithms assist users to use digital controls smoothly through intelligent recommendations.Our method can produce data elements automatically, efficiently and accurately, unlocking the value of data for digital economy.

Data elements are the key momentum for boosting digital economy. The data generated by public services provided by governments (a.k.a. public data) is ready to be transferred into data elements, because it has been well organized in the past decade. Unfortunately, public data is strictly coupled with the systems generating them, making it difficult for different applications to share data. The process of munul data governance is lagging, heavy and inefficient, and relying on automatic extraction method can’t ensure the accuracy of data elements. To tackle these challenges, leveraging the synergy between human and machine intelligence, we propose an elementarisation method for public data based on urban knowledge system. Our method is comprised of an urban knowledge system, a set of digital controls and some machine learning algorithms. The urban knowledge system consists of entities, relationships between entities, and the properties associated with these entities and relationships, which can be used to construct different kinds of public services and form standard data representation that can be shared among different applications. Powered by the urban knowledge system, the digital controls enable governments to create different applications as public services flexibly, through a configurable way without writing any codes. Later, the information input by citizens through digital controls in these applications is transferred into data elements automatically. Finally, the machine learning algorithms assist users to use digital controls smoothly through intelligent recommendations. Our method can produce data elements automatically, efficiently and accurately, unlocking the value of data for digital economy. [ABSTRACT FROM AUTHOR]

Urban grid prediction can be applied to many classic spatial-temporal prediction tasks such as air quality prediction, crowd density prediction, and traffic flow prediction, which is of great importance to smart city building. In light of its practical values, many methods have been developed for it and have achieved promising results. Despite their successes, two main challenges remain open: (a) how to well capture the global dependencies and (b) how to effectively model the multi-scale spatial-temporal correlations? To address these two challenges, we propose a novel method— DeepMeshCity , with a carefully-designed Self-Attention Citywide Grid Learner (SA-CGL) block comprising of a self-attention unit and a Citywide Grid Learner (CGL) unit. The self-attention block aims to capture the global spatial dependencies, and the CGL unit is responsible for learning the spatial-temporal correlations. In particular, a multi-scale memory unit is proposed to traverse all stacked SA-CGL blocks along a zigzag path to capture the multi-scale spatial-temporal correlations. In addition, we propose to initialize the single-scale memory units and the multi-scale memory units by using the corresponding ones in the previous fragment stack, so as to speed up the model training. We evaluate the performance of our proposed model by comparing with several state-of-the-art methods on four real-world datasets for two urban grid prediction applications. The experimental results verify the superiority of DeepMeshCity over the existing ones. The code is available at https://github.com/ILoveStudying/DeepMeshCity. [ABSTRACT FROM AUTHOR]

Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and temporal graph neural networks to deal with the complex dependence between the traffic in adjacent areas. However, existing graph neural network based approaches suffer from several critical drawbacks, including improper graph representation of urban traffic data, lack of semantic correlation modeling among graph nodes, and coarse-grained exploitation of external factors. To address these issues, we propose DiffUFP, a novel probabilistic graph-based framework for UFP. DiffUFP consists of two key designs: (1) a semantic region dynamic extraction method that effectively captures the underlying traffic network topology, and (2) a conditional denoising score-based adjacency matrix generator that takes spatial, temporal, and external factors into account when constructing the adjacency matrix rather than simply concatenation in existing studies. Extensive experiments conducted on real-world datasets demonstrate the superiority of DiffUFP over the state-of-the-art UFP models and the effect of the two specific modules. [ABSTRACT FROM AUTHOR]

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problems, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this article, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces a differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on two real-world nationwide city air quality datasets, including the China dataset and the US dataset, indicate that our GAGNN outperforms existing forecasting models. [ABSTRACT FROM AUTHOR]

Forecasting crowd congestion is crucial for ensuring comfortable mobility and public safety. Existing methods forecast crowding by capturing the increase in planned visits, which facilitates the methods in estimating the start of crowding. However, forecasting the change in the degree of crowding until the end is challenging owing to the lack of visitors’ return plans and the deviation of visitor movements from preannounced event schedules. To address this issue, this study developed a novel framework for forecasting the start of crowding and its change over time (termed the lifespan of crowded events (LCE)). Based on the concept that event purposes influence the crowding patterns, our framework models these patterns according to the event purposes. Inspired by the acoustic synthesis that can successfully model the change in the sound volume for each instrument, we extended a canonical long short-term memory (LSTM) model with the concept of ADSR envelope, wherein the sound (crowd) volume changes can be represented within simple state transitions. The proposed versatile acoustic tri-state envelope for segmental LSTM, namely VATES, is evaluated on two datasets: synthetic and real-world mobility datasets. The results demonstrate that VATES can forecast crowding patterns with a 24.3% performance improvement, and precisely predict the start and end times of crowding, thereby improving by 6.6% and 26.1% respectively. We believe that our method enhances urban safety and mobility in crowded events, contributing to smarter city management.

Neuro-adaptive architecture has emerged as an interdisciplinary field aiming to cultivate buildings and urban environments responsive to human emotions, cognition, and well-being. Technological advances now enable unprecedented monitoring of occupants' psychological states through unobtrusive sensors, as well as adaptive modulation of environments via “smart” architectural components. If developed responsibly, these advancements hold great potential to optimize human experience and flourishing within the built milieu. However, they also present complex ethical challenges regarding privacy, consent, data security, globalization and equitable access that require thoughtful consideration. This paper provides a comprehensive review and synthesis of the opportunities and dilemmas at the nexus of neuroscience, architecture, and urban planning. Drawing from research worldwide, it examines the multidimensional issues involved and strategies for addressing them through participatory and empathic design practices. Case studies of experimental neuro-adaptive projects are discussed and recommendations provided for longitudinal evaluation of impacts on health, social outcomes, and well-being. Concepts such as cognitive ergonomics, sensory perception and emotional design, restorative urbanism, and adaptive living interfaces are explored through diverse methodologies and design hypotheses are provided for future interdisciplinary collaboration. Overall, this paper argues that responsibly optimized neuro-adaptive architecture could enhance human thriving in complex urban environments, but precautions are necessary to avoid risks to autonomy, equity or unintended consequences. Continued rigorous interdisciplinary work is imperative to navigate these opportunities and challenges, with consideration of technical, social and ethical implications at individual and societal levels.

In an era defined by rapid urbanization and technological advancements, this article provides a comprehensive examination of the transformative influence of Federated Learning (FL) on Urban Computing (UC), addressing key advancements, challenges, and contributions to the existing literature. By integrating FL into urban environments, this study explores its potential to revolutionize data processing, enhance privacy, and optimize urban applications. We delineate the benefits and challenges of FL implementation, offering insights into its effectiveness in domains such as transportation, healthcare, and infrastructure. Additionally, we highlight persistent challenges including scalability, bias mitigation, and ethical considerations. By pointing towards promising future directions such as advancements in edge computing, ethical transparency, and continual learning models, we underscore opportunities to enhance further the positive impact of FL in shaping more adaptable urban environments. [ABSTRACT FROM AUTHOR]

This study aims to propose an approach for spatiotemporal integration of bus transit, which enables users to change bus lines by paying a single fare. This could increase bus transit efficiency and, consequently, help to make this mode of transit more attractive. Usually, this strategy is allowed for a few hours in a non-restricted area; thus, certain walking distance areas behave like "virtual terminals." For that, two data-driven algorithms are proposed in this work. First, a new algorithm for detecting itineraries based on bus GPS data and the bus stop location. The proposed algorithm's results show that 90% of the database detected valid itineraries by excluding invalid markings and adding times at missing bus stops through temporal interpolation. Second, this study proposes a bus stop clustering algorithm to define suitable areas for these virtual terminals where it would be possible to make bus transfers outside the physical terminals. Using real-world origin-destination trips, the bus network, including clusters, can reduce traveled distances by up to 50%, making twice as many connections on average. [ABSTRACT FROM AUTHOR]

The spatial structures of cities defined by population distribution, distribution of facilities, and mobility have a significant impact on lifestyles of residents and their wellbeing. In this study, we analyze millions of mobile phone data points to infer significant sequences of visited facilities by individuals, cluster people with similar patterns of life activity sequences, and define lifestyles based on the patterns in each cluster. We find that lifestyles of a large number of people can be captured using a small set of activity sequences, while a small portion of populations have lifestyles with a variety of activity sequences. Facility proximity in spatial constraints is positively correlated with the volume of human movements, and is a significant factor in formation of the majority of lifestyle patterns. Differences in facility proportions between two neighborhoods contribute to cross-neighborhood travels for life activities, but its effect could be mediated by the geographical distances between neighborhoods. Our findings demonstrate that the widely studied scaling laws in these areas are not independent but rather connected through a deeper underlying reality, which has important implications for urban planning and city management policies to enhance equal accessibility.

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The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians' impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran's I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor's influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study's findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design. [ABSTRACT FROM AUTHOR]

Road network is a critical infrastructure powering many applications including transportation, mobility and logistics in real life. To leverage the input of a road network across these different applications, it is necessary to learn the representations of the roads in the form of vectors, which is named road network representation learning (RNRL). While several models have been proposed for RNRL, they capture the pairwise relationships/connections among roads only (i.e., as a simple graph), and fail to capture among roads the high-order relationships (e.g., those roads that jointly form a local region usually have similar features such as speed limit) and long-range relationships (e.g., some roads that are far apart may have similar semantics such as being roads in residential areas). Motivated by this, we propose to construct a hypergraph, where each hyperedge corresponds to a set of multiple roads forming a region. The constructed hypergraph would naturally capture the high-order relationships among roads with hyperedges. We then allow information propagation via both the edges in the simple graph and the hyperedges in the hypergraph in a graph neural network context. In addition, we introduce different pretext tasks based on both the simple graph (i.e., graph reconstruction) and the hypergraph (including hypergraph reconstruction and hyperedge classification) for optimizing the representations of roads. The graph reconstruction and hypergraph reconstruction tasks are conventional ones and can capture structural information. The hyperedge classification task can capture long-range relationships between pairs of roads that belong to hyperedges with the same label. We call the resulting model HyperRoad. We further extend HyperRoad to problem settings when additional inputs of road attributes and/or trajectories that are generated on the roads are available. We conduct extensive experiments on two real datasets, for five downstream tasks, and under four problem settings, which demonstrate that our model achieves impressive improvements compared with existing baselines across datasets, tasks, problem settings, and performance metrics. [ABSTRACT FROM AUTHOR]

Abstract In the study of social relationship inference, social relationship link inference aims to infer whether there is a social relationship between users. Most previous works applied the unsupervised graph random walk sampling, which had sampling bias and lost much information. In this paper, a Social Relationship Link Inference Based on Graph Convolutional Networks (SLiGCN) is proposed, which learns the spatiotemporal information of check-ins and the impact of related users’ trajectories. It firstly employs the end-to-end supervised learning and applies a recurrent neural network to extract spatiotemporal sequence features from trajectories. Then the graph convolutional network fuses the features of neighboring nodes and employs the fully connected network to infer social relationship links. Finally, it is evaluated with AUC on three real-world datasets. The experiment results show that, compared with baseline models, it not only avoids hand-crafted feature construction that requires much prior knowledge, but also achieves 10% improvement on average.

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning-based algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Many countries worldwide are introducing public bicycle systems to reduce urban traffic and environmental problems. However, studies on the usage behavior of public bicycles have not considered the trip purpose of riders extensively due to data limitations. Therefore, this study defined the "extra journey time" from usage time and origin–destination (OD) expected time and clustered public bicycle usage behaviors. Subsequently, the effects of the location characteristics of the departure and arrival stations, road environmental factors, and weather conditions for each cluster were analyzed. Three clusters were obtained from the results. Riders in Cluster 1 were inferred to have used the bicycles to commute and for work purposes, and riders in Clusters 2 and 3 used the bicycles for leisure purposes. Moreover, the bike station location characteristics, road environmental factors, and weather conditions influenced the probability of classification into one cluster. In particular, bike lanes near the departure and arrival stations increased the probability of classification under Clusters 2 and 3. The trip patterns according to the extra journey time of public bicycles were classified under these clusters. Furthermore, the differences in the characteristics of the bicycle usage types were identified according to the location and meteorological factors affecting them. [ABSTRACT FROM AUTHOR]

Every day, our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the urban computing community has been dedicating in recent years. Nevertheless, existing studies are still facing the challenges of data fusion for the urban data as well as the knowledge distillation for specific applications. Moreover, there is a lack of full-featured and user-friendly platforms for both researchers and developers in the urban computing scenario. Therefore, in this article, we present UrbanKG, an urban knowledge graph system to incorporate a knowledge graph with urban computing. Specifically, the system introduces a complete scheme to construct a knowledge graph for urban data fusion. Built upon the data layer, the system further develops the multiple layers of construction, storage, algorithm, operation, and applications, which achieve knowledge distillation and support various functions to the users. We perform representative use cases and demonstrate the system capability of boosting performance in various downstream applications, indicating a promising research direction for knowledge-driven urban computing. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions.

The real estate market has a significant impact on people's daily life. Therefore, it is crucial to understand the real estate market from both spatial and temporal perspectives, while there is still a lack of research in real estate industries. In this paper, a regional house price mining and forecasting (RHPMF) framework is proposed to help people intuitively understand the spatial distribution and temporal evolution of the urban estate market based on real-world housing data and urban contexts such as demographics and criminal records. Specifically, the RHPMF framework introduces a context-aware matrix factorization to extract crucial spatial and temporal price factors for revealing the housing market. Meanwhile, the RHPMF can forecast future regional house prices by manipulating the two price factors. Consequently, this study presents extensive exploratory analysis and experiments in Virginia Beach, Philadelphia, and Los Angeles to verify the proposed RHPMF. These case studies indicate that the RHPMF framework can accurately capture the market's spatial distribution and temporal evolution and forecast future regional house prices compared with recent baselines. The experimental results suggest the great potential of the proposed RHPMF for applications in real estate industries. • Unleash a new perspective to understand the regional market of real estate. • A context-aware matrix factorization is proposed to fuse information. • The decomposed factors explain the spatial and temporal evolution. • The fused information improves the forecasting accuracy of house prices. [ABSTRACT FROM AUTHOR]

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing. Specifically, we first delve into data perspective to comprehend the role of each modality and data source. Secondly, we classify the methodology into four primary categories: feature-based , alignment-based , contrast-based , and generation-based fusion methods. Thirdly, we further categorize multi-modal urban applications into seven types: urban planning , transportation , economy , public safety , society , environment , and energy. Compared with previous surveys, we focus more on the synergy of deep learning methods with urban computing applications. Furthermore, we shed light on the interplay between Large Language Models (LLMs) and urban computing, postulating future research directions that could revolutionize the field. We firmly believe that the taxonomy, progress, and prospects delineated in our survey stand poised to significantly enrich the research community. The summary of the comprehensive and up-to-date paper list can be found at https://github.com/yoshall/Awesome-Multimodal-Urban-Computing. • Comprehensive survey of deep learning techniques for data fusion in urban computing. • Innovative taxonomy framework for data sources, fusion methods, and applications. • Detailed compilation of key datasets and fusion models for urban computing. • Insightful future research outlook for urban computing. • Exploration of LLMs for potential in enhancing data fusion in urban computing. [ABSTRACT FROM AUTHOR]

Abstract As a new form of public transportation, shared bikes have greatly facilitated people’s travel in recent years. However, in the actual operation process, the uneven distribution of bicycles at each shared bicycle station has limited the travel experience. In this paper, we propose a deep spatio-temporal residual network model based on Region-reConStruction algorithm to predict the usage of shared bikes in the bike-sharing system. We first propose an Region-reConStruction algorithm (RCS) to partition the shared bicycle sites within a city into separate areas based on their geographic location information as well as bikes’ migration trends between stations. We then combine the RCS algorithm with a deep spatio-temporal residual network to model the key factors affecting the usage of shared bicycles. RCS makes good use of the migration trend of shared bikes during user usage, thus greatly improving the accuracy of prediction. Experiments performed on New York’s bike-sharing system show that our model’s prediction accuracy is significantly better than that of previous models.

Typically, the strategy of central heating for buildings in winter is climate compensator.However, this strategy heavily relies on manual experience with a relatively simple regulation.Therefore, how to optimize the heating control strategy is very important to keep the indoor temperature stable and comfortable.For this task, this paper proposes a heating strategy optimization method based on deep learning and deep reinforcement learning, which can optimize the original control strategy based on real historical data.The paper first develops a deep MTDN (Multiple Time Difference Network) as the simulator to predict the next time slot's room temperature.By learning the thermodynamic law of indoor temperature change, the network has high accuracy and confirms the physical laws.After that, the SAC (Soft Actor-Critic) algorithm based on maximum entropy reinforcement learning is employed as the strategy optimizer to interact with the simulator.Here, we use the evaluation index of the human body's thermal response as the reward to train and optimize the heating control strategy.Based on the real data of a heat exchange station in Tianjin, we evaluate the predictive ability of the simulator and the control ability of the strategy optimizer, respectively.The results verify that, compared with other types of prediction simulators, this simulator not only has high prediction accuracy but also conforms to physical laws.At the same time, compared with the original strategy, the strategy learned by the strategy optimizer can ensure that the indoor temperature is more stable and comfortable in multiple time periods of random sampling.

The relationship between urban mobility, social networks, and socioeconomic status is complex and difficult to apprehend, notably due to the lack of data. Here we use mobile phone data to analyze the socioeconomic structure of spatial and social interaction in the Chilean urban system. Based on the concept of spatial and social events, we develop a methodology to assess the level of spatial and social interactions between locations according to their socioeconomic status. We demonstrate that people with the same socioeconomic status preferentially interact with locations and people with a similar socioeconomic status. We also show that this proximity varies similarly for both spatial and social interactions during the course of the week. Finally, we highlight that these preferential interactions appear to hold when considering city–city interactions. [ABSTRACT FROM AUTHOR]

The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly. [ABSTRACT FROM AUTHOR]

The detection of anomalies in spatiotemporal traffic data is not only critical for intelligent transportation systems and public safety but also very challenging. Anomalies in traffic data often exhibit complex forms in two aspects, (i) spatiotemporal complexity (i.e. we need to associate individual locations and time intervals formulating a panoramic view of an anomaly) and (ii) multi-source complexity (i.e. we need an algorithm that can model the anomaly degree of the multiple data sources of different densities, distributions and scales). To tackle these challenges, we proposed a three-step method that uses factor analysis to extract features, then uses the goodness-of-fit test to obtain the anomaly score of a single data point and then uses one class support vector machine to synthesize the anomaly score. Finally, we conduct extensive experiments on real-world trip data include taxi and bike data. And these extensive experiments demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

As a new form of public transportation, shared bikes have greatly facilitated people's travel in recent years. However, in the actual operation process, the uneven distribution of bicycles at each shared bicycle station has limited the travel experience. In this paper, we propose a deep spatio-temporal residual network model based on Region-reConStruction algorithm to predict the usage of shared bikes in the bike-sharing system. We first propose an Region-reConStruction algorithm (RCS) to partition the shared bicycle sites within a city into separate areas based on their geographic location information as well as bikes' migration trends between stations. We then combine the RCS algorithm with a deep spatio-temporal residual network to model the key factors affecting the usage of shared bicycles. RCS makes good use of the migration trend of shared bikes during user usage, thus greatly improving the accuracy of prediction. Experiments performed on New York's bike-sharing system show that our model's prediction accuracy is significantly better than that of previous models. [ABSTRACT FROM AUTHOR]

Data has been playing an increasingly important role in building intelligent cities in the last decade. Recently, the advances in artificial technology has boosted a rising trend of using knowledge mined from data rather than raw data to tackle urban challenges. Just like designing data management frameworks for intelligent cities in the last decade, we need to define the knowledge management frame‐ work for the coming new era, in order for that knowledge generated by different applications can be constantly accumulated and consistently shared between each other. Otherwise, new knowledge islands will be created while we are spending a lot of efforts to break down data islands. To address this issue, we propose the knowledge system, consisting of the definition of the content of knowledge in intelligent cities and the framework of knowledge representation, generation and application. The knowledge system can help professionals to acquire domain knowledge quickly, mine knowledge from data efficiently, apply knowledge to solve problems effectively, and share knowledge between each other consistently, thus leading to smarter and greener cities. [ABSTRACT FROM AUTHOR]