Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
MISS-D: A fast and scalable framework of medical image storage service based on distributed file system
MISS-D: یک چارچوب سریع و مقیاس پذیر از خدمات ذخیره سازی تصویر پزشکی بر اساس سیستم فایل توزیع شده-2020
Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging
City limits in the age of smartphones and urban scaling
محدودیت های شهر در عصر تلفن های هوشمند و مقیاس بندی شهری-2020
Urban planning still lacks appropriate standards to define city boundaries across urban systems. This issue has historically been left to administrative criteria, which can vary significantly across countries and political systems, hindering a comparative analysis across urban systems. However, the wide use of Information and Communication Technologies (ICT) has now allowed the development of new quantitative approaches to unveil how social dynamics relates to urban infrastructure. In fact, ICT provide the potential to portray more accurate descriptions of the urban systems based on the empirical analysis of millions of traces left by urbanites across the city. In this work, we apply computational techniques over a large volume of mobile phone records to define urban boundaries, through the analysis of travel patterns and the trajectory of urban dwellers in conurbations with more than 100,000 inhabitants in Chile. We created and analyzed the network of interconnected places inferred from individual travel trajectories. We then ranked each place using a spectral centrality method. This allowed to identify places of higher concurrency and functional importance for each urban environment. Urban scaling analysis is finally used as a diagnostic tool that allowed to distinguish urban from non-urban spaces. The geographic assessment of our method shows a high congruence with the current and administrative definitions of urban agglomerations in Chile. Our results can potentially be considered as a functional definition of the urban boundary. They also provide a practical implementation of urban scaling and data-driven approaches on cities as complex systems using increasingly larger non-conventional datasets.
Keywords: City boundaries definition | Spectral network analysis | Urban informatics | Social computing | Scaling laws | Complex systems | Big data
Mobile phone network data reveal nationwide economic value of coastal tourism under climate change
ارزش اقتصادی داده های شبکه تلفن همراه در سراسر جهان از گردشگری ساحلی در اثر تغییر آب و هوا-2020
The technology-driven application of big data is expected to assist policymaking towards sustainable development; however, the relevant literature has not addressed human welfare under climate change, which limits the understanding of climate change impacts on human societies. We present the first application of unique mobile phone network data to evaluate the current nation-wide human welfare of coastal tourism at Japanese beaches and project the value change using the four climate change scenarios. The results show that the projected national economic value loss rates are more significant than the projected national physical beach loss rates. Our findings demonstrate regional differences in recreational values: most southern beaches with larger current values would disappear, while the current small values of the northern beaches would remain. These changes imply that the ranks of the beaches, based on economic values, would enable policymakers to discuss management priorities under climate change.
Keywords: Adaptation | Beach recreation | Big data | Climate change | Coastal tourism | Ecosystem services | Travel cost method | Sea level rise
Efficacy and safety of oral and inhalation commercial beta-glucan products: Systematic review of randomized controlled trials
اثربخشی و ایمنی محصولات بتا گلوکان تجاری و خوراکی استنشاق: مرور سیستماتیک کارآزمایی کنترل شده تصادفی-2020
Background & aims: Beta-glucans are advertised as biologically active compounds, with various health claims.We aimed to summarize results about efficacy and safety of commercial oral and inhalation betaglucan products on human health from randomized controlled trials (RCTs). Methods: We conducted systematic review of RCTs. We searched MEDLINE, CENTRAL and ClinicalTrials. gov. Any commercial product, any types of participants and any health-related outcomes were eligible. Two authors independently screened studies and extracted data. Cochrane risk of bias tool was used. This review did not have any extramural funding. Registration: PROSPERO record no. 42016043539. Results: We included 30 RCTs that were conducted on healthy or ill participants. Most of the trials reported beneficial effect of beta-glucan, but among the 105 different outcome domains and measures that were used, only three could be considered clinically relevant, while others were various biomarkers and surrogate outcomes such as complete blood count. Included studies on average had 33 participants per study arm, high or unclear risk of bias of at least one domain, and only half of them reported data for safety. More than half of trials that reported source of funding indicated commercial sponsorship from producers of beta-glucan. Only five RCTs reported trial registration. Conclusions: Commercial beta-glucan products were studied in a number of RCTs whose results can be considered only as preliminary, as they used small number of participants and surrogate outcomes. The quality of many studies was poor and further research and trials on bigger population should be performed before a final conclusion can be made.
Keywords: Beta-glucan | Systematic review | Evidence | Randomized controlled trial | Research waste
Intelligent condition assessment of industry machinery using multiple type of signal from monitoring system
ارزیابی شرایط هوشمند ماشین آلات صنعت با استفاده از چندین نوع سیگنال از سیستم نظارت-2020
Real time condition assessment for machinery is used for avoiding catastrophic failures. A new strategy which combined data processing with data-driven method is presented for condition assessment of machinery based on multiple characteristic parameters of industrial equipment. Firstly, the data processing is carried out, including the industrial data cleaning, the correlation analysis using the Bin method and the condition division. The vibration parameters, which are sensitive to the state changes of the machine, are assumed as data binning reference. Secondly, the multi-parameter condition evaluation technique is proposed by using Hidden Markov Model. The industrial big data collected from monitoring system are analyzed and the site test is conducted finally. The results show that the provided technique can not only evaluate the running condition of the machinery, but also reflect the change of the operational condition. It can exhibit a potential capability in tracing further deterioration of the machine
Keywords: Industrial machinery | Monitoring system | Condition assessment | Correlation analysis | Hidden Markov Model
Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
آنتروپی پویای نمادین چند متغیره کامپوزیت تصفیه شده و کاربرد آن در تشخیص خطای ماشین چرخشی-2020
Accurate and efficient identification of various fault categories, especially for the big data and multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive multivariate data, extracting discriminative and stable features with high efficiency is the significant step. This paper proposes a novel feature extraction method, called Refined Composite multivariate Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic entropy can capture more identification information from multiple sensors with superior computational efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method to measure the complexity of multivariate time series and identify the signals with different components are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball bearing dataset under time-varying speed condition are applied. Compared with the existing methods, the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively. Meanwhile, the proposed method saves at least half of the computational time. The result shows that the proposed method is effective to improve the efficiency and classification accuracy dealing with the massive multivariate signals.
Keywords: Multivariate multiscale symbolic dynamic | entropy | Random forest | Time-varying speed conditions | Fault diagnosis
Functional urban area delineations of cities on the Chinese mainland using massive Didi ride-hailing records
توصیف های کاربردی منطقه شهری از شهرها در سرزمین اصلی چین با استفاده از سوابق گسترده تگرگ سوار بر دیدنی Didi-2020
The problem associated with a citys administrative boundary being “under-” or “over-bounded” has become a global phenomenon. A citys administrative boundary city does not effectively represent the actual size and impact of its labor force and economic activity. While many existing case studies have investigated the functional urban areas of single cities, the problem of how to delineate urban areas in geographic space relating to large bodies of cities or at the scale of an entire country has not been investigated. This study proposed a method for FUA identification that relies on ride-hailing big data. In this study, over 43 million anonymized 2016 car-hailing records were collected from Didi Chuxing, the largest car-hailing online platform in the world (to the best of our knowledge). A core-periphery approach is then proposed that uses nationwide and fine-grained trips to understand functional urban areas in Mainland China. This study examined 4456 out of all 39,007 townships in an attempt to provide a new method for the definition of urban functional areas in Chinese Mainland. In addition, four types of cities are identified using a comparison of functional urban areas with their administrative limits, and a further evaluation is conducted using 23 Chinese urban agglomerations. With the rapidly increasing use of internet-based ride-hailing services, such as Didi, Grab, Lyft, and Uber, globally, this study provides a practical benchmark for the delineation of functional urban areas at larger scales..
Keywords: Functional urban area | Car-hailing records | |National level | Delineating standards | City system
Understanding the spatial distribution of free-floating carsharing in cities: Analysis of the new Madrid experience through a web-based platform
درک توزیع مکانی اتومبیل های شناور آزاد در شهرها: تجزیه و تحلیل تجربه جدید مادرید از طریق یک بستر مبتنی بر وب-2020
In recent years the sharing economy has become established in different modes in the urban transport system, and claims to be reducing the number of cars and contributing to lower traffic pollution. Free-floating carsharing (FFCS) is a new and more flexible type of carsharing that is driving the growth of carsharing markets around the world. While there is a very extensive literature on traditional carsharing, more research needs to be done on the new FFCS trip profile in order to estimate its spillover effects on the urban transportation system. As FFCS systems are based on ICTs, new web-based methodologies (instead of traditional surveys) are the best approach to analyse them. This paper contributes to the existing literature with a spatial evaluation of the FFCS trip profile, obtaining a temporal distribution of the main flows throughout the FFCS service area. The added value of this research is that it provides the first spatial analysis of a FFCS system in Spain using rental data collected from the operators websites. The results clearly show the prevalence of the short-distance FFCS trip that is faster than available public transport and whose origin and destination are closely dependent on parking availability.
Land titling, land reallocation experience, and investment incentives: Evidence from rural China
عنوان بندی اراضی ، تجربه توزیع مجدد زمین و مشوق های سرمایه گذاری: شواهدی از روستای چین-2020
The impacts of land titling on investment incentives among farmers with different land reallocation experiences are studied in this work. Ordered Probit model and 2SLS are employed to estimate the survey data collected from 2704 households in rural countries in China. We find that, generally, land titling can substantially promote investment incentive among farmers. However, the impacts vary among farmers with different land reallocation experiences. Specifically, land titling positively affects farmers without land reallocation experience, but it negatively affects those farmers who experienced big reallocation. Land titling has an investment incentive effect on China’s special agricultural land system, where farmers only have contract rights of land. However, big reallocation should be heavily restricted to guarantee the investment incentive effect of land titling.
Keywords: Land titling | Land rights reallocation | Investment incentive | China