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In urban environments, efficiently decrypting CP-ABE in VANETs is a significant challenge due to the dynamic and resource-constrained nature of these networks. VANETs are critical for ITS that improve traffic management, safety, and infotainment through V2V and V2I communication. However, managing computational resources for CP-ABE decryption remains difficult. To address this, we propose a hybrid RL-DE algorithm. The RL agent dynamically adjusts the DE parameters using real-time vehicular data, employing Q-learning and policy gradient methods to learn optimal policies. This integration improves task distribution and decryption efficiency. The DE algorithm, enhanced with RL-adjusted parameters, performs mutation, crossover, and fitness evaluation, ensuring continuous adaptation and optimization. Experiments in a simulated urban VANET environment show that our algorithm significantly reduces decryption time, improves resource utilization, and enhances overall efficiency compared to traditional methods, providing a robust solution for dynamic urban settings.

This study addresses the methodological gap in tourism research regarding the long-term monitoring of tourism activities in urban settings. We propose an analytical framework that uses data from location-based social networks (LBSN) to derive tourists' digital footprints resulting in a sustained, yet partial, overview of tourist activity and mobility in urban destinations. Significantly, we found that LBSN data might signal changes in the geography of city tourism over time. This study pioneers the use of LBSN data to gain knowledge about city tourism in the longer run, thereby providing a means to review the development of tourism cities. The proposed framework abstracts the geographic dimension of tourism cities and extends spatial analysis to the study of tourism destinations. Moreover, the materials and methods used can be reproduced in other case studies, offering spatial measurements for comparative study, and potentially informing urban planning and design in tourism destinations. [ABSTRACT FROM AUTHOR]

The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, urban informatics, as a new discipline in the field of urban planning, has gradually attracted academic attention. The rise of urban informatics puts new pressures on urban planning, but it also provides a new perspective of analysis. This paper is a summary of a panel discussion among scholars in urban informatics held at the 2020 International Association for China Planning(IACP).In this context, the panel outlines the definition of urban informatics, and the difference between urban informatics and urban analytic and computing, and found that urban informatics pays more attention to end user. This indicates that urban informatics has been more than a supporting role in urban planning or design, and is increasingly integrated with urban planning. The panel also discusses the connotation of urban informatics and its wide application in practice, and illustrates with examples. At the same time, the team identifies the difficulties of its development mainly reflected in the two aspects of resources and talents, and the learners of urban planning discipline have natural advantage in learning urban informatics. Finally, the panel discusses how to improve teaching, and concludes that the promotion of good cases, discipline integration, training data thinking rather than focusing too much on methods and other concepts. All in all, this panel's report contributes to the wider discussion about the role of urban informatics plays in urban planning. [ABSTRACT FROM AUTHOR]

With the rapid pace of urbanization, enhancing the quality of life has become an urgent demand for the general public in both developed and developing countries. This study addresses the pressing need to understand the spatial distribution and underlying mechanisms of urban leisure activity participation. To achieve this, we propose a novel methodological framework that integrates diverse big data sources, including mobile phone signaling data, urban geospatial data, and web-crawled data. By applying this framework to the urban area of Nanjing, our study reveals both the temporal and spatial patterns of urban leisure activity participation in the city. Notably, leisure activity participation is significantly higher on weekends, with distinctive daily peaks. Moreover, we identify spatial heterogeneity in leisure activity participation across the study area. Leveraging the OLS regression model, we design and quantify a comprehensive set of 12 internal and external indicators to explore the formation mechanisms of leisure participation for different leisure activity types. Our findings offer valuable guidance for urban planners and policymakers to optimize the allocation of resources, enhance urban street environments, and develop leisure resources in a rational and inclusive manner. Ultimately, this study contributes to the ongoing efforts to improve the quality of urban life and foster vibrant and sustainable cities. [ABSTRACT FROM AUTHOR]

Introduction: Mental disorders are considered to be the most significant threat to public health. Mitigation effects of urban green spaces have been widely documented. However, the conclusions are inconsistent because of the representativeness of the original data.Method: We measured the mental perception of urban green spaces using geospatial big data instead of field observation or questionnaire data. Street view data were applied to calculate urban green space characteristics in three dimensions.Results: The positive mental perception percentage around the chosen urban parks increased as the buffer diameter decreased. The temporal variations of positive mental perceptions around the selected urban parks exhibited an obvious peak-trough shape. The spatial associations between the positive mental perception percentage and urban green space characteristics varied geographically. The spatial associations became less similar as the spatial buffer diameter decreased. At the same spatial scale level, the green view played a dominant role in the spatial distribution of positive mental perceptions.Discussion: Shrinking the deviations of urban green space characteristics and increasing the mean and maximum values of urban green space characteristics will favor the improvement of public mental health. This study provides a reference for explaining ecological scientific questions using spatiotemporal big data. It also provides insights into the mechanisms underlying the relationship between ecological processes and public health.

Smart mobility initiatives encompass innovative methods to support traffic management experts in decisions for how to improve urban infrastructures and reduce carbon footprint. Accurate and continuous information about traffic is necessary to implement effectively such decisions. This is not always possible because of the cost of the information: it is not possible to install sensor devices at large scale because of financial costs and privacy; employing a plethora of sensors requires significant computational capabilities to process the generated data. A centralized data analysis can hinder real-time applications, and limit their practical deployment in traffic management systems. This paper introduces a novel privacy-aware method for estimating traffic density using edge computing and without over-deploying privacy-intrusive surveillance technologies such as cameras. The objective is to reduce the cost of collecting data while providing accurate information to support traffic operators in decision making. We evaluate the proposed solution using a realistic traffic data of Bologna in Italy. Results shows that it yields a 45% lower average estimation error compared to standard prediction methods. Virtual traffic monitoring devices are associated with software agents that collect data from simulated traffic and estimate traffic density measurements when this information is not available. In our experiments, when we replace 50% of camera devices with cooperative low-cost edge devices, we obtain an average percentage error of just 22%. This result indicates that the cooperation between virtual traffic monitoring devices offers a means to avoid massive deployment of camera surveillance devices using low-cost information provided by connected vehicles. We also compared the results to those obtained by standard regression techniques.

In order to mitigate gas leakage damage, it is important to assess systematic risk of gas leakage in utility networks. In practice, leakage detection systems heavily rely on tedious and expensive human efforts, such as manually checking and assessing the risk, and thus only a very limited portion of the gas pipeline network can usually be assessed during a period of time. Therefore, it is critical to develop tools for automatically and systematically evaluating the whole gas pipeline network and revealing high-risk sites for checking in a timely fashion. To this end, in this paper, we develop an intelligent gas leakage risk assessment system based on the analysis of large-scale multi-source data, such as gas pipeline data, Point of Interests (POIs) data, and human mobility data. However, it is a non-trivial endeavor to design such a risk assessment system due to the unclear leakage mechanism, complex environmental conditions, and large size of the pipeline network. To address these challenges, both internal features of gas pipelines and external environmental features should be exploited. Specifically, we design a novel urban sensing technique to extract environmental features by analyzing human mobility data. Then, a joint learning neural network is developed to assess the leakage risk by integrating both internal and external features. Moreover, an intelligent risk assessment system is implemented and deployed for experiments in real-world scenarios. The results show the effectiveness of our system for citywide gas leakage risk assessment. Indeed, the proposed system could help to locate the potential leakage sites in a timely way.

Increasing levels of noise pollution in urban environments are a primary cause of various physical and psychological health issues. There is an urgent requirement to manage environmental noise by assessing the current levels of noise pollution by gathering real-world data and building a fine-granularity real-time noise map. Traditionally, simulation-based, small-scale sensor-network-based, and participatory sensing-based approaches have been used to estimate noise levels in urban areas. These techniques are inadequate to gauge the prevalence of noise pollution in urban areas and have been shown to leak private user data. This paper proposes a novel federated learning-based urban noise mapping system, FL-NoiseMap, that significantly enhances the privacy of participating users without adversely affecting the application performance. We list several state-of-the-art urban noise monitoring systems that can be seamlessly ported to the federated learning-based paradigm and show that the existing privacy-preserving approaches can be used as an add-on to enhance participants’ privacy. Moreover, we design an “m-hop” application model modification approach for privacy preservation, unique to FL-NoiseMap. We also describe techniques to maintain data reliability for the proposed application. Numerical experiments on simulated datasets showcase the superiority of the proposed scheme in terms of users’ privacy preservation and noise map reliability. The proposed scheme achieves the lowest average normalized root mean square error in the range of 4% to 7% as the number of participants varies between 500 and 5000 while providing maximum coverage of over 95% among various competing algorithms. The proposed malicious contribution removal framework can decrease the average normalizedroot mean square error by more than 50% for simulations having up to 20% malicious users.

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates. [ABSTRACT FROM AUTHOR]

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives. [ABSTRACT FROM AUTHOR]

Street View Imagery (SVI) is crucial in estimating indicators such as Sky View Factor (SVF) and Green View Index (GVI), but (1) approaches and terminology differ across fields such as planning, transportation and climate, potentially causing inconsistencies; (2) it is unknown whether the regularly used panoramic imagery is actually essential for such tasks, or we can use only a portion of the imagery, simplifying the process; and (3) we do not know if non-panoramic (single-frame) photos typical in crowdsourced platforms can serve the same purposes as panoramic ones from services such as Google Street View and Baidu Maps for their limited perspectives. This study is the first to examine comprehensively the built form metrics, the influence of different practices on computing them across multiple fields, and the usability of normal photos (from consumer cameras). We overview approaches and run experiments on 70 million images in 5 cities to analyse the impact of a multitude of variants of SVI on characterising the physical environment and mapping street canyons: a few panoramic approaches (e.g. fisheye) and 96 scenarios of perspective imagery with variable directions, fields of view, and aspect ratios mirroring diverse photos from smartphones and dashcams. We demonstrate that (1) disparate panoramic approaches give different but mostly comparable results in computing the same metric (e.g. from R=0.82 for Green View to R=0.98 for Sky View metrics); and (2) often (e.g. when using a front-facing ultrawide camera), single-frame images can derive results comparable to commercial panoramic counterparts. This finding may simplify typical processes of using panoramic data and also unlock the value of billions of crowdsourced images, which are often overlooked, and can benefit scores of locations worldwide not yet covered by commercial services. Further, when aggregated for city-scale analyses, the results correspond closely.

The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.

With the rapid pace of urbanization, enhancing the quality of life has become an urgent demand for the general public in both developed and developing countries. This study addresses the pressing need to understand the spatial distribution and underlying mechanisms of urban leisure activity participation. To achieve this, we propose a novel methodological framework that integrates diverse big data sources, including mobile phone signaling data, urban geospatial data, and web-crawled data. By applying this framework to the urban area of Nanjing, our study reveals both the temporal and spatial patterns of urban leisure activity participation in the city. Notably, leisure activity participation is significantly higher on weekends, with distinctive daily peaks. Moreover, we identify spatial heterogeneity in leisure activity participation across the study area. Leveraging the OLS regression model, we design and quantify a comprehensive set of 12 internal and external indicators to explore the formation mechanisms of leisure participation for different leisure activity types. Our findings offer valuable guidance for urban planners and policymakers to optimize the allocation of resources, enhance urban street environments, and develop leisure resources in a rational and inclusive manner. Ultimately, this study contributes to the ongoing efforts to improve the quality of urban life and foster vibrant and sustainable cities.

Gaining a complete picture of the activity in a city using vast data sources is challenging yet potentially very valuable. One such source of data is Twitter which generates millions of short spatio-temporally localized messages that, as a collection, have information on city regions and many forms of city activity. The quantity of data, however, necessitates summarization in a way that makes consumption by an observer efficient, accurate, and comprehensive. We present a two-step process for analyzing geotagged twitter data within a localized urban environment. The first step involves an efficient form of latent Dirichlet allocation, using an expectation maximization, for topic content summarization of the text information in the tweets. The second step involves spatial and temporal analysis of information within each topic using two complimentary metrics. These proposed metrics characterize the distributional properties of tweets in time and space for all topics. We integrate the second step into a graphical user interface that enables the user to adeptly navigate through the space of hundreds of topics. We present results of a case study of the city of Madrid, Spain, for the year 2011 in which both large-scale protests and elections occurred. Our data analysis methods identify these important events, as well as other classes of more mundane routine activity and their associated locations in Madrid.

The specialization of different urban sectors, theories, and technologies and their confluence in city development have led to a greatly accelerated growth in urban informatics, the transdisciplinary field for understanding and developing the city through new information technologies. While this young and highly promising field has attracted multiple reviews of its advances and outlook for its future, it would be instructive to probe further into the research initiatives of this rapidly evolving field, to provide reference to the development of not only urban informatics, but moreover the future of cities as a whole. This article thus presents a collection of research initiatives for urban informatics, based on the reviews of the state of the art in this field. The initiatives cover three levels, namely the future of urban science; core enabling technologies including geospatial artificial intelligence, high-definition mapping, quantum computing, artificial intelligence and the internet of things (AIoT), digital twins, explainable artificial intelligence, distributed machine learning, privacy-preserving deep learning, and applications in urban design and planning, transport, location-based services, and the metaverse, together with a discussion of algorithmic and data-driven approaches. The article concludes with hopes for the future development of urban informatics and focusses on the balance between our ever-increasing reliance on technology and important societal concerns. [ABSTRACT FROM AUTHOR]

With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes. [ABSTRACT FROM AUTHOR]

Identifying spatial communities in vehicle movements is vital for sensing human mobility patterns and urban structures. Spatial community detection has been proven to be an NP-Hard problem. Heuristic algorithms were widely used for detecting spatial communities. However, the spatial communities identified by existing heuristic algorithms are usually locally optimal and unstable. To alleviate these limitations, this study developed a hybrid heuristic algorithm by combining multi-level merging and consensus clustering. We first constructed a weighted spatially embedded network with road segments as vertices and the numbers of vehicle trips between the road segments as weights. Then, to jump out of the local optimum trap, a new multi-level merging approach, i.e., iterative local moving and global perturbation, was proposed to optimize the objective function (i.e., modularity) until a maximum of modularity was obtained. Finally, to obtain a representative and reliable spatial community structure, consensus clustering was performed to generate a more stable spatial community structure out of a set of community detection results. Experiments on Beijing taxi trajectory data show that the proposed method outperforms a state-of-the-art method, spatially constrained Leiden (Scleiden), because the proposed method can escape from the local optimum solutions and improve the stability of the identified spatial community structure. The spatial communities identified by the proposed method can reveal the polycentric structure and human mobility patterns in Beijing, which may provide useful references for human-centric urban planning. [ABSTRACT FROM AUTHOR]

The rapid proliferation of mobile devices with various sensors has enabled participatory mobile sensing (PMS). Several PMS platforms suffer from open issues including the limited use of their functions to a specific scenario/case and the necessity of technical knowledge for organizers. This paper proposes a novel PMS platform named ParmoSense for easy and flexible data collection. To reduce the burden on both organizers and participants, we employ two novel features. First, essential PMS functions implemented as modules can be easily chosen and combined for sensing in different scenarios. Second, the scenario-based description feature allows organizers to easily and quickly prepare a new instance of PMS and enable people to easily participate in the sensing. It also provides multiple functions to motivate participants for sustainable operation. Through a performance comparison with existing PMS platforms, we confirmed that ParmoSense shows the best cost performance in terms of the workload for preparation and the variety of functions. In addition, to evaluate the availability and usability of ParmoSense, we conducted 19 case studies over four years with ordinary citizens. As the result of a questionnaire survey carried out during the case studies, we confirmed that ParmoSense can be easily operated by ordinary citizens without technical skills. [ABSTRACT FROM AUTHOR]

This article introduces a methodology to implement Data-driven Thinking in the context of urban design. We present the results of a case study based on a 7-day workshop with 10 participants with landscape design and architecture background. The goal of the workshop was to expose participants to Data-driven Thinking through experimental design, multi-sensor data collection, data analysis, visualization, and insight generation. We evaluate their learning experience in designing an experimental setup, collecting real-time immediate environmental and physiological body reactions data. Our results from the workshop show that participants increased their knowledge about measuring, visualizing and understanding data of the surrounding built environment. [ABSTRACT FROM AUTHOR]

Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsensed or less-sensed. In addition, due to the natural changes in a city and the movements of the vehicles, the sensed and unsensed areas change over time. Consequently, challenges of air pollution estimation with data collected by non-dedicated mobile platforms are twofold: i. data coverage is sparse; ii. data coverage changes over time. Therefore, the major research question is: how can we derive accurate and robust fine-grained field (e.g., air pollution) estimation given dynamic and sparse data collected from uncontrollable mobile sensing platforms? This paper presents adaptive HMSS, an adaptive hybrid model-enabled sensing system for fine-grained air pollution estimation with dynamic and sparse data collected from uncontrollable mobile sensing platforms, which is achieved by combining the advantages of a physics guided model and a data driven model. To address the challenge of sparse coverage, the physical understanding of the spatiotemporal correlation for air pollution distribution in the physics guided model is utilized to infer values at unsensed sparse areas. Meanwhile, the data driven model is adopted to estimate the air pollution influential factors (e.g., buildings) not included in the physics guided model. To address the challenge of time-varying coverage, an adaptive model combination algorithm is designed to enable the system bias to either of the two models according to the amount of data collection and uncertainty of the model. To evaluate the system performance, we deployed 47 air pollution sensing devices on taxis and fixed locations in 2 cities for both controlled and uncontrolled experiments for over two weeks. The results show that with a resolution of $500 \;\mathrm m$ 500 m by $500\;\mathrm m$ 500 m by $1\;\mathrm {hour}$ 1 hour , our system achieves up to $3.2\times$ 3. 2 × error reduction when compared to the baseline approaches. [ABSTRACT FROM AUTHOR]

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.

The smart city term has been widely used for a number of years and many pilot projects and limited scale, sector independent initiatives have been progressed, but comprehensive, long-term, city wide, multi-sector systems are much less evident. This paper examines one such case study in Newcastle, UK highlighting the challenges and opportunities that realizing “smart city” concepts at scale present. The paper provides the background to the Newcastle Urban Observatory project and discusses the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city. We discuss the organizational requirements, governance, data quality and volume issues, big data management and discuss the current and future needs of decision makers and other city stakeholders. Finally, we propose areas where smart cities can have a positive impact on public outcomes through the discussion of two case studies related to COVID-19 and pedestrianization initiatives.

We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.

Vehicle speed is a key variable for the calibration, validation, and improvement of traffic emission and air quality models. Lidar technologies have significant potential in vehicle tracking by scanning the surroundings in 3-D frequently, hence can be used as traffic flow monitoring sensors for accurate vehicle counting and speed estimation. However, the characteristics of lidar-based vehicle tracking and speed estimation, such as attainable accuracy, remain as open questions. This research therefore proposes a tracking framework from roadside lidar to detect and track vehicles with the aim of accurate vehicle speed estimation. Within this framework, on-road vehicles are first detected from the observed point clouds, after which a centroid-based tracking flow is implemented to obtain initial vehicle transformations. A tracker, utilizing the unscented Kalman Filter and joint probabilistic data association filter, is adopted in the tracking flow. Finally, vehicle tracking is refined through an image matching process to improve the accuracy of estimated vehicle speeds. The effectiveness of the proposed approach has been evaluated using lidar data obtained from two different panoramic 3-D lidar sensors, a RoboSense RS-LiDAR-32 and a Velodyne VLP-16, at a traffic light and a road intersection, respectively, in order to account for real-world scenarios. Validation against reference data obtained by a test vehicle equipped with accurate positioning systems shows that more than 94% of vehicles could be detected and tracked, with a mean speed accuracy of 0.22 m/s.

With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes.

Zoning is an important tool to regulate the use of land and to characterize built form over land, and thus to facilitate urban sustainability. Availability of reliable data is crucial for monitoring land use zoning, which contributes directly to the success of the Sustainable Development Goals (SDGs) in general, and SDG Goal 11 for sustainable cities and communities in particular. However, obtaining this valuable information using traditional survey methods is both costly and time-consuming. Remote sensing technology overcomes these challenges and supports urban policymaking and planning processes. This study unveils a novel approach to developing a cost-effective method for identifying building types using Sentinel-2A, Visible Infrared Imaging Radiometer Suite (VIIRS)–based nighttime light (NTL) data, and TanDEM-X–based Digital Surface Model (DSM) data. A newly developed index for this study, the Normalized Difference Steel Structure Index (NDSSI), is useful for rapidly mapping industrial buildings with steel structures. The implementation status of Dhaka's existing land use plan was evaluated by analyzing the spatial distribution of different types of building uses. This study classifies residential, commercial, and industrial buildings within Dhaka using building height, and nighttime light emission. The experimental results reveal that about 67% of commercial and 51% of industrial buildings within the Dhaka Metropolitan Area (DMA) do not comply with the land use zoning by the Detailed Area Plan (DAP). It also reveals that approximately 10% of commercial buildings, 9% of industrial buildings, and 6% of residential buildings have encroached upon conservation zones (such as open space, flood-prone zones, water bodies, and proposed areas for future road extension). A major constraint in the study was the low spatial resolution of the nighttime light dataset, which made it difficult to distinguish individual sources of light. Still, the methodological approaches proposed in this study are expected to promote reduced costs and efficacious decision-making in urban transformation and to help achieve SDG 11, especially in developing countries. [ABSTRACT FROM AUTHOR]

Urban sensor networks often consist of a large number of low-cost sensor nodes. Due to the constrained resource devices and hazardous deployment, urban sensing is vulnerable to interference and destruction of external factors or the impact of external environmental emergencies. Abnormal data, outliers, or anomalies have affected the utility in various domains seriously. Timely and accurate detection of unexpected events, monitoring of network performance, and anomaly detection of data flow are of great significance to improve the decision-making ability of the system. In this paper, we propose an anomaly detection method for urban sensing based on sequential data and credibility. First, based on Bayesian methods, a reputation model is established for the selection of credible sample points. Second, aiming at the problem that the threshold range is difficult to determine in the traditional method, the pivot quantity is defined by using the median of the credible sample, and the confidence interval can be estimated to quantify the deviation degree of the sensor data. Finally, an anomaly data identification and source verification approach is proposed to distinguish errors and events accurately. The evaluation results on both the detection rate and the false positive rate demonstrate a better performance of our approach than the other existing methods.

Sparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data inference). It can significantly reduce the sensing cost while ensuring high data quality. Recently, large urban sensing systems often require multiple types of sensing data (e.g., publish two tasks on temperature and humidity respectively) to form a multi-dimensional urban sensing map. These multiple types of sensing data hold some inherent correlations, which can be leveraged to further reduce the sensing cost and improve the accuracy of the inferred results. In this paper, we study the multi-dimensional urban sensing in sparse MCS to jointly address the data inference and cell selection for multi-task scenarios. We exploit the intra- and inter-task correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective 〈cell, task〉 pairs by using reinforcement learning. To effectively capture the intra- and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms.

Identifying spatial communities in vehicle movements is vital for sensing human mobility patterns and urban structures. Spatial community detection has been proven to be an NP-Hard problem. Heuristic algorithms were widely used for detecting spatial communities. However, the spatial communities identified by existing heuristic algorithms are usually locally optimal and unstable. To alleviate these limitations, this study developed a hybrid heuristic algorithm by combining multi-level merging and consensus clustering. We first constructed a weighted spatially embedded network with road segments as vertices and the numbers of vehicle trips between the road segments as weights. Then, to jump out of the local optimum trap, a new multi-level merging approach, i.e., iterative local moving and global perturbation, was proposed to optimize the objective function (i.e., modularity) until a maximum of modularity was obtained. Finally, to obtain a representative and reliable spatial community structure, consensus clustering was performed to generate a more stable spatial community structure out of a set of community detection results. Experiments on Beijing taxi trajectory data show that the proposed method outperforms a state-of-the-art method, spatially constrained Leiden (Scleiden), because the proposed method can escape from the local optimum solutions and improve the stability of the identified spatial community structure. The spatial communities identified by the proposed method can reveal the polycentric structure and human mobility patterns in Beijing, which may provide useful references for human-centric urban planning.

The global outbreak of the SARS-COVID-19 pandemic has changed our lives, driving an unprecedented transformation of our habits. In response, the authorities have enforced several measures, including social distancing and travel restrictions that lead to the temporary closure of activities centered around schools, companies, local businesses to those pertaining to the recreation category. As such, with a mobility reduction, the life of our cities during the outbreak changed significantly. In this paper, we aim at drawing attention to this problem and perform an analysis for multiple cities through crowdsensed information available from datasets such as Apple Maps, to shed light on the changes undergone during both the outbreak and the recovery. Specifically, we exploit data characterizing many mobility modes like driving, walking, and transit. With the use of Gaussian Processes and clustering techniques, we uncover patterns of similarity between the major European cities. Further, we perform a prediction analysis that permits forecasting the trend of the recovery process and exposes the deviation of each city from the trend of the cluster. Our results unveil that clusters are not typically formed by cities with geographical ties, but rather on the spread of the infection, lockdown measures, and citizens’ reactions.

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer transit riders' activities from their smart card transaction records. Using Singapore as an example, activity type classification models were built using household travel survey and a rich set of urban built environment measures to reveal the spatial and temporal correspondences that indicate the activity participation of transit riders. The calibrated model is then applied to the transit smart card dataset to extract the embedded activity information. The proposed approach enables to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new "big data" such as transit smart card data and "small data" such as traditional travel surveys to create better insights of urban travel demand and activity dynamics. [ABSTRACT FROM AUTHOR]

We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques. [ABSTRACT FROM AUTHOR]

Advances in technology and infrastructure have positioned mobile phones as a convenient platform for real-time assessment of an individuals health and behavior, while offering unprecedented accessibility and affordability to both the producers and the consumers of the data. In this paper we address several of the key challenges that arise in leveraging smartphones for health: designing the complex set of building blocks required for an end-to-end system, motivating participants to sustain engagement in long-lived data collection, and interpreting both the data and the quality of the data collected.We present AndWellness, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. In order to address the system design and participation motivation challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. AndWellness additionally includes rich system and user analytics to instrument the act of participation itself and ultimately to contextualize and better understand the factors affecting the quality of collected data over time. We evaluate the usability and feasibility of AndWellness using data from 3 studies with a variety of populations including young moms and recent breast cancer survivors. More than 85% of the diverse set of participants who responded to exit surveys claim they would use AndWellness for further personal behavior discovery.

Participatory Sensing is a process whereby individuals and communities use mobile phones and web services to observe, analyze, and present personal and environmental artifacts, events and experiences. In this technical report we describe a community data campaign that made use of smartphone based participatory sensing for environmental needs assessment. Community organizers defined the content of the participatory sensing campaign. 68 individuals participated over the course of 6 weeks, uploading over 450 mini-surveys, including over 700 images.