Urban Computing Research Articles

Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction Using Aerial Images and Trajectories

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We design a gated fusion module to explicitly control the information flows from both modalities in a complementary-aware manner. Moreover, we propose a novel densely supervised refinement decoder to generate the prediction in a coarse-to-fine way. Our comprehensive experiments demonstrate that DeepDualMapper can fuse the information of images and trajectories much more effectively than existing approaches, and is able to generate maps with higher accuracy.

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.

Concept Lattice Method for Spatial Association Discovery in the Urban Service Industry

A relative lag in research methods, technical means and research paradigms has restricted the rapid development of geography and urban computing. Hence, there is a certain gap between urban data and industry applications. In this paper, a spatial association discovery framework for the urban service industry based on a concept lattice is proposed. First, location data are used to form the formal context expressed by 0 and 1. Frequent closed itemsets and a concept lattice are computed on the basis of the formal context of the urban service industry. Frequent closed itemsets can filter out redundant information in frequent itemsets, uniquely determine the complete set of all frequent itemsets, and be orders of magnitude smaller than the latter. Second, spatial frequent closed itemsets and association rules discovery algorithms are designed and built based on the formal context. The inputs of the frequent closed itemsets discovery algorithms include the given formal context and frequent threshold value, while the outputs are all frequent closed itemsets and the partial order relationship between them. Newly added attributes create new concepts to guarantee the uniqueness of the new spatial association concept. The inputs of spatial association rules discovery algorithms include frequent closed itemsets and confidence threshold values, and a rule is confident when and only if its confidence degree is not less than the confidence threshold value. Third, the spatial association of the urban service industry in Nanning, China is taken as a case to verify the method. The results are basically consistent with the spatial distribution of the urban service industry in Nanning City. This study enriches the theories and methods of geography as well as urban computing, and these findings can provide guidance for location-based service planning and management of urban services.

Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction

Crowd flow prediction has become a strategically important task in urban computing, which is the prerequisite for traffic management, urban planning and public safety. However, due to variousness of crowd flows, multiple hidden correlations among urban regions affect the flows. Besides, crowd flows are also influenced by the distribution of Points-of-Interests (POIs), transitional functional zones, environmental climate, and different time slots of the dynamic urban environment. Thus, we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units (MGCGRU) for capturing these multiple spatial correlations. For adapting to the dynamic mobile data, we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes. Hence, the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction. The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.

IEEE Access Special Section Editorial: Urban Computing and Well-Being in Smart Cities: Services, Applications, Policymaking Considerations

Over the past decade, the status of smart cities changed from a fancy area of research into a key policy-making consideration. The increased attention paid to smart cities by decision-makers and politicians prompted in turn a new wave of research in the domain. Urban computing is but one of the developments thus prompted. As this Special Section depicts, it is also one of the most promising domains of research likely to transform the debate on smart cities in the years to come. Urban computing offers a conceptual and methodological framework that integrates the plethora of increasingly sophisticated technologies and their applications in city/urban space. It offers a framework suitable to conceive of andbuild synergies, and to apply in the city/urban space context a variety of approaches and techniques. These include pervasive computing, big data analytics, crowdsourcing, and volunteered geographic information such as user behavior, brand popularity, recommender systems, and social media analytics. In this way, urban computing bears the promise that targeted solutions will be found for the challenges cities/urban spaces face. The objective of this Special Section in IEEE A CCESS was to explore that promise from a variety of complementary interdisciplinary perspectives, including computing/ICT, political economy, public policy, innovation, and entrepreneurship.

A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

In spatial-temporal data analysis, location data and its evolution through time are investigated with the goal of uncovering important information to provide novel insights. These insights, for example, may involve congestion identification in transportation, mobility patterns in urban computing, and storm prediction in weather forecasting. Clustering, one data analysis technique, groups spatial-temporal data based on location. Current spatial-temporal data analysis techniques fail to investigate relationships between spatial-temporal clusters, such as splitting from a cluster and merging with another one because of a change of properties over time. These relationships could hold valuable information about the existence of a cluster and its interactions with other clusters and trajectories. In this paper, we introduce a framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split). We describe its architecture and components, as well as a proposed clustering technique, the different approaches for distance calculation, and how we calculate cluster similarity of temporally separated clusters. The result of these operations are used in the identification of cluster relationships over space and time. The analysis of these relationships helps uncover hidden values that could support novel approaches to more effective decision-making. We evaluate our framework with two case studies, based on truck and human trajectories.

AttractRank: District Attraction Ranking Analysis Based on Taxi Big Data

The city's district attraction ranking plays an essential role in the city's government because it can be used to reveal the city's district attraction and, thus, help government make decisions for urban planning in terms of the smart city. The traditional methods for urban planning mainly rely on the district's GDP, employment rate, population density, information from questionnaire surveys, and so on. However, as a comparison, such information is becoming relatively less informative as the explosion of an increasing amount of urban data. What is more, there is a serious shortcoming in these methods, i.e., they are independent representations of the attraction of a district and do not take into account the interaction among districts. With the development of urban computing, it is possible to make good use of urban data for urban planning. To this end, based on taxi big data obtained from Guangzhou, China, this article proposes a district attraction ranking approach called AttractRank, which for the first time uses taxi big data for district ranking. An application system is developed for demonstration purposes. First, the entire Guangzhou city is divided into a number of districts by using constrained K-means. Second, the original PageRank algorithm is extended to integrate with the taxi's origin–destination ( $OD$ ) points to establish the $OD$ matrix, whereby the attraction ranking of each district can be calculated. Finally, by visualizing the results and case studies obtained from AttractRank, we can successfully obtain the pattern of how attractions of districts change over time and interesting discoveries on urban lives; therefore, it has wide applications in urban planning and urban data mining.

SUDIR: An Approach of Sensing Urban Text Data From Internet Resources Based on Deep Learning

Urban data is a imperative resource for urban computing, which can promote the establishment of urban knowledge, the collection of urban information and the construction of smart cities. Seen that the urban data collected through hardware sensors and crowdsourcing has the limitations of uneven information distribution, poor data comprehensiveness and high resource costs, we turn to the Internet resources of real-time updates and extensive information coverage. Therefore, we propose an approach to Sensing Urban text Data from Internet Resources (SUDIR). We put forward innovative work on two key issues: urban data recognition for Chinese context and urban data sensing for multi-source web resources. On one hand, we design a Chinese urban data recognition model based on Whole Word Masking for Bidirectional Encoder Representations from Transformers (BERT-WWM) embedding model and Bidirectional Long-Short Term Memory with a Conditional Random Field (BLSTM-CRF) sequence labeling model. We introduce Chinese Word Segmentaion (CWS) concept in BERT embedding model to make the text embedding effect better represent semantic information on Chinese context. BLSTM-CRF model based on deep learning is used to achieve high-quality coding and prediction. On the other hand, we propose a method of Extracting Urban text data based on Web page features and Clustering operation (EUWC). EUWC is used to correct the false negative samples labeled by BERT-WWM+BLSTM-CRF recognition model and enable SUDIR to sense more accurate and comprehensive city data from multi-source web resources. The experimental results show that our work outperforms the other baseline methods, and it also proves that SUDIR using Internet resources and deep learning technology has the advantages of low-cost, high-quality urban data sensing.

Temporal Impulse of Traffic Accidents in South Korea

With the emergence of urban computing technology, the development of smart cities has gained much attention as a means to improve citizens’ quality of life. As traffic accidents constitute a major problem that affects the quality of life, an effective solution to address this problem can significantly increase the level of intelligence of smart cities. This paper presents the development of a mathematical model for accurate analysis of big data to promote the effectiveness of policy decisions, thereby largely advancing the intelligent transportation systems (ITS) of smart cities. Temporal impulse was designed as a novel and measurable quantity to analyze traffic accidents by identifying the hidden patterns, such as varying causes and diverging impacts of traffic accidents. Based on the big data produced by the South Korean National Police Agency, we analyzed traffic accidents over three years by applying the temporal impulse. The research results suggested that the temporal impulse not only helped in identifying the varying influence of weather and driver conditions but also facilitated the establishment of sophisticated policies in the implementation of smart cities with the use of urban computing technology. As presented in the section VII, our simulation outputs indicated that our temporal model was predictive within the parameter space comprising driver’s dynamic behaviors, day of the week, and environmental factors including weather, road surface condition, and road type.

Batman or the joker?

The exponential growth of the urban data generated by urban sensors, government reports, and crowd-sourcing services endorses the rapid development of urban computing and spatial data mining technologies. Easier accessibility to such enormous urban data may be a double-bladed sword. On the one hand, urban data can be applied to solve a wide range of practical issues such as urban safety analysis and urban event detection. On the other hand, ethical issues such as biasedly polluted urban data, problematic algorithms, and unprotected privacy may cause moral disaster not only for the research fields but also for the society. This paper seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are addressed.

Visions and challenges in GeoAI, ethics, and spatial quantum computing

Geographic information systems are changing rapidly as new technologies, such as advances in artificial intelligence and quantum computing, proliferate. Towards gaining a glimpse of possible new directions for the next decades, it is paramount to disseminate and discuss new research directions, challenges, and visions, that may be of broad interest to the SIGSPATIAL community. In the last few years, our community has excelled at providing a forum for such visions and challenges in dedicated Vision and Challenge tracks, leading to many visionary research directions [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. A goal of this newsletter is to continue and complement these visions. Three vision and challenge topics are covered in this special issue: 1. the first article is contributed by the organizers of GeoAI, the most successful ACM SIGSPATIAL Workshop of the last years, having 40+ registrations each year, and having 50+ people in the workshop room at a time. The authors survey and summarize the directions of GeoAI publications of the last three years, and provide a list of open research directions for this rapidly advancing field; 2. in the second article, Fu et al. discuss ethical issues of mining urban spatial data. They identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events; 3. in the third article, Martin Werner introduces central aspects of quantum algorithms and explores future directions on how quantum algorithms can be used to leverage spatial and spatio-temporal algorithms. This article describes example algorithms, such as map coloring and dynamic time warping, which may benefit from advances in quantum computing.

Introduction to the Special Issue on Online Learning for Big-Data Driven Transportation and Mobility

In the last years, the arrival and progressively gained maturity of technological paradigms such as Connected Vehicles, the Internet of Things, Sensor Networks, Urban Computing, Smart Cities, Cloud Computing, Edge Computing, Big Data and others alike have ignited the role historically played by data-based learning techniques to levels never seen before. This sharp increase has been particularly noticed in the design and management of intelligent systems for transportation and mobility, as processes, services and applications deployed in these systems are fed with data substrates captured at unprecedented rates and scales. Legacy sensing equipment installed on the roads’ infrastructure (e.g., induction loops and cameras) are nowadays complemented by alternative means to sense the transportation and mobility context of interest in real time and ubiquitously, as can be exemplified by data collected in a crowd-sourced way by using ad-hoc smart applications, as well as floating car data and/or social media.

Aerial and Under-water Dronal Communication: Potentials, Issues and Vulnerabilities

The use of UAVs has been on the rise since the past decade. These UAVs are very versatile and can be applied to several fields ranging from agriculture to military reconnaissance. Due to the immense functionality of the UAVs, it has now become a very popular topic in Research. With the advent of Deep Learning and Machine Learning the autonomous navigation functionalities have been improved but it still poses a challenge as navigating a drone involves the integration of its software and hardware functionalities. This paper reviews both indoor and outdoor navigation. Topics in navigation such as Control, Hardware, Algorithms, Path-Planning, and communication are touched upon in this paper. The paper also presents a discussion on the Navigation of UAVs using vision-based technologies, this technology encompasses the use of various sensors and cameras and integrate it with the navigation system of the UAV to ensure the success of UAV’s operation. UAVs play a pivotal role in networking, many applications have been developed which have UAVs as an important component in them. Their applications include Urban computing, Internet of Things, Ubiquitous Computing to name a few. Safely operating a UAV as a network node is not a simple task, these nodes are prone to various types of attacks. A new type of attack introduced in this paper is termed as “Coagulation Attack”. This attack gets its name from the coagulation property of fluids wherein fluids clot and their particles settle down. This paper explores the concept, issues, challenges and research aspects of Coagulation attack. This paper also introduces the concept of Underwater Wireless Communication. On a planet where 70% of the surface is covered in water, UWC has a lot of potentials to be exploited. Moreover UWC is critical in many key areas of Maritime research, commerce, and surveillance. UWC is used for experimental observation, data collection and analysis, underwater navigation, disaster prevention and early detection warning of a tsunami. In addition, we summarize emerging technologies in the UWC, future research directions and recommendations using fifth-generation (5G) communication techniques.

Tracking urban geo-topics based on dynamic topic model

Modern cities are facing critical environmental and social problems that are difficult to solve using conventional planning approaches due to the cities’ magnitude and complexity. Recent developments in sensing technologies and urban computing, however, integrate new data resources and technologies to tackle these challenges. Popular social networking platforms such as Twitter provide new data sources on important events (e.g., cultural activities, political campaigns, accidents, crises) providing rich knowledge about urban systems and human dynamics. This research is intended to develop a method for effectively monitoring important information during such events and helping with planning and policymaking. We use semantically similar and geographically close geo-topics to represent important local events. This research proposes a data-driven system for detecting and tracking the semantic, spatial, and temporal dynamics of these geo-topics, specifically designed for geo-tagged tweets. The system consists of data preprocessing, geo-topic generation, and geo-topic tracking modules. The preprocessing module can remove robotic and semantically trivial texts. In the geo-topic generation module, we use spatial factors to measure the spatial impacts of geo-tagged tweets by applying an exponential decay function to the pairwise distances between tweets. We then improve the dynamic topic model (DTM) by embedding the spatial factors to enable the generation of geo-topics in semantic, spatial, and temporal dimensions simultaneously. The geo-topic tracking module monitors semantic change by detecting changes in certain keywords’ probabilities and the volumes of tweets belonging to different geo-topics. This module also uses radius of gyration and trajectory-pattern mining to track and analyze the movement patterns of geo-topics. We employed the tracking system in three disaster cases in different U.S. cities to track small-scale emergencies and crises. These implementations demonstrated the effectiveness of the system for identifying and tracking geo-topics at fine temporal and geographic scales. The system also has strong potential in creating planning-related analyses for policy makers, improving the situational awareness of the general public, and serving as a basis for urban information systems that contribute to smart, agile, and resilient city developments.

Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services

Ride-sourcing services are becoming an increasingly popular transportation mode in cities all over the world. With real-time information from both drivers and passengers, the ride-sourcing platform can reduce matching frictions and improve efficiencies by surge pricing, optimal vehicle-trip assignment, and proactive ridesplitting strategies. An important foundation of these strategies is the short-term supply-demand forecasting. In this paper, we tackle the problem of predicting the short-term supply-demand gap of ride-sourcing services. In contrast to the previous studies that partitioned a city area into numerous square lattices, we partition the city area into various regular hexagon lattices, which is motivated by the fact that hexagonal segmentation has an unambiguous neighborhood definition, smaller edge-to-area ratio, and isotropy. To capture the spatio-temporal characteristics in a hexagonal manner, we propose three hexagon-based convolutional neural networks (H-CNN), both the input and output of which are numerous local hexagon maps. Moreover, a hexagon-based ensemble mechanism is developed to enhance the prediction performance. Validated by a 3-week real-world ride-sourcing dataset in Guangzhou, China, the H-CNN models are found to significantly outperform the benchmark algorithms in terms of accuracy and robustness. Our approaches can be further extended to a broad range of spatio-temporal forecasting problems in the domain of shared mobility and urban computing.

Geospatial Big Data: New Paradigm of Remote Sensing Applications

The rapid development of information technology and location techniques not only leads to an increasing growth of massive geospatial big data but also raises the attention of using these data to complement with remote sensing images. Many efforts have been made to utilize geospatial big data to identify human activity patterns and carry out urban and environmental researches, integrating with remote sensing images. Nonetheless, there are still many issues, including the representativeness and locality of geospatial big data, as well as the fusion methods, remain to be further explored. In this article, we first reviewed the innovation and proceedings of data mining and analyzing techniques, as well as remote sensing applications driven by geospatial big data. Besides, two popular concepts, namely, “Social Sensing” and “Urban Computing,” were briefly introduced. Then, we highlighted the role of geospatial big data in mining human activity dynamics and socioeconomic characteristics, and the feasibility of combining with remote sensing data for various studies. Lastly, we presented some empirical case studies on the confluence of remote sensing and geospatial big data in land use extraction, environmental and disaster monitoring, as well as socioeconomic dynamics sensing. The provided examples and discussion demonstrated the high efficiency and complementarity of the integration of remote sensing and geospatial big data, which benefits decision making from multiple perspectives and scales.

Development of Smart Cities and Its Sustainability: A Smart City Framework

Objective of the paper is to get analyze the scientific studies by focusing on smart city concepts and sustainability to understand better. For doing such thing, the study identifies the information about the different existing and recent technologies, various research, different models and various frameworks. The methodology used for this research is qualitative by an organized review of different literatures, which examines the important terminologies like, “Smart city”, “Urban Computing” and “Sustainability”, focusing on the sustainable development of smart cities. This review gives detailed information regarding the recent scientific piece of writing focusing variety of issues related to smart cities and sustainability. This review may provide a base to the researchers looking for surrounding information to explore more. The finding gives important insights for the scholars researching in the area under discussion, and managers are trying to apply those in their respective cities. In the last section we have proposed a three layered framework of smart cities which includes the supporting technologies, pillars of smart city and the society which gets benefitted.

Vehicular Clouds Leveraging Mobile Urban Computing Through Resource Discovery

Vehicles are integral elements of urban computing and urban analytics, providing sensed data and thus being closely related to the exchange and gathering of a massive quantity of data in real-time. Vehicular Clouds consist of joining Vehicular Networks with Mobile Cloud Systems, allowing vehicles to share their resources and facilitate the transfer of information. Due to its mobility, the pool of resources in a Vehicular Cloud changes constantly. Thus, these Clouds need to adapt according to the input and output of their resources to meet the requirements of quality of service. Resource management and discovery are crucial elements for enabling and maintaining such Clouds. In this paper, we describe and discuss important issues about identifying and organizing resources in a Vehicular Cloud, as well as the specific techniques involved. Finally, we discuss challenges and issues for potential future works.

Joint hyperspectral unmixing for urban computing

Recently, many methods for hyperspectral unmixing have been proposed. These methods are often based on nonnegative matrix factorization (NMF), which naturally inherits the non-negative advantage and is in line with the common sense of physics. Although there are many ways to perform NMF-based hyperspectral unmixing, these methods can only unmix one hyperspectral image at a time. In practice, we may often collect two or more similar hyperspectral images, and the end of the hyperspectral images of the signal could be only slightly different. Traditional NMF-based hyperspectral unmixing methods cannot take advantage of the fact that different hyper-spectral images may have similar or even the same end-element signals. Accordingly, in order to improve the performance of NMF-based hyperspectral unmixing, we present an algorithm in this paper that can process two hyperspectral images, simultaneously, and makes full use of the available information when most of the signals at the two end-points are similar. This improves the effect of end-element extraction in hyperspectral unmixing evidenced by experimental results on both synthetic and real-world data.