Revealing deep semantic commercial patterns: Insights from urban landscape depiction
آشکار ساختن الگوهای تجاری معنایی عمیق: بینش از منظر شهری-2020
Commercial functions play a significant role in economic growth and sustainable development. Influenced by socioeconomic changes which were investigated based on commercial behaviours using big geo-data, urban environment shows polycentric structures and further leads to more complex commercial landscapes. Thus, research considered environmental consequences of urban growth to better understand the commercialization process. However, only general commercial areas or certain commercial retail locations were investigated with the effects of urban environmental landscapes in existing studies. The decomposition of the commercial patterns such as accommodation and catering functions remains undiscussed. To fill this gap, this paper proposes a framework to decompose commercial patterns based on insight from different urban landscapes. First, we depict urban landscapes using spatial metrics, including the density of points of interest (POIs), percentage of landscape of buildings, landscape shape index of buildings and density of roads. Then, we examine the spatial distribution of the decomposed commercial patterns by delineating potential deep semantic topics. Finally, a structural equation model is utilised to determine the relations among urban landscapes and commercial patterns. The results show that four types of urban landscapes exhibit different spatial patterns, revealing various perspectives of commercial characteristics. Meanwhile, the decomposed commercial patterns, including those for catering and accommodation functions, display a heterogeneous distribution. These commercial patterns are directly affected by various configurations of buildings, POIs and roads. On this basis, suggestions are offered to improve commercial pattern development, including integrating urban landscape construction, organising the commercial pattern distribution and enhancing the harmony between the urban environment and commercial land uses.
Keywords: Urban landscape | Urban function | Commercial pattern | Structural equation model
Highway crash detection and risk estimation using deep learning
تشخیص تصادف بزرگراه و تخمین ریسک با استفاده از یادگیری عمیق-2020
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years, researchers have explored several techniques for early and precise detection of crashes to aid in traffic incident management. With recent advancements in data collection techniques, abundant real-time traffic data is available for use. Big data infrastructure and machine learning algorithms can utilize this data to provide suitable solutions for the highway traffic safety system. This paper explores the feasibility of using deep learning models to detect crash occurrence and predict crash risk. Volume, Speed and Sensor Occupancy data collected from roadside radar sensors along Interstate 235 in Des Moines, IA is used for this study. This real-world traffic data is used to design feature set for the deep learning models for crash detection and crash risk prediction. The results show that a deep model has better crash detection performance and similar crash prediction performance than state of the art shallow models. Additionally, a sensitivity analysis was conducted for crash risk prediction using data 1-minute, 5-minutes and 10-minutes prior to crash occurrence. It was observed that is hard to predict the crash risk of a traffic condition, 10 min prior to a crash.
Keywords: Crash detection | Crash prediction | Deep learning
سیستم پشتیبانی از تصمیم برای خطرات و اقدامات متقابل ایمنی جاده ای اروپا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 32
سیستم پشتیبانی از تصمیم درباره ایمنی جاده ای اروپا (roadsafety-dss.eu) یک سیستم نوآورانه است که شواهد و مدارک دسترس پذیری را درباره گستره وسیعی از خطرات جاده ای و اقدامات متقابل امکانپذیر فراهم می کند. این مقاله پایه و اساس علمی سیستم پشتیبانی از تصمیم را توصیف می کند. ساختار موجود در سیستم پشتیبانی از تصمیم شامل (1) یک طبقه بندی که به شناسایی عوامل خطر و اقدامات متقابل آن می پردازد و آنها را به همدیگر مرتبط می کند، (2) یک مجموعه ای از مطالعات، و (3) خلاصه هایی که تاثیرات تخمین زده شده در منابع علمی را برای هر عامل و سنجه خطر خلاصه بندی می کنند و (4) یک ابزار ارزیابی کارآمدی اقتصادی (محاسبه گر E3) می شود. سیستم پشتیبانی از تصمیم در یک ابزار نوین مبتنی بر وب با فصل مشترک بسیار انسانی اجرا می شود که به کاربران اجازه می دهد تا مرور اجمالی سریعی داشته باشند یا نتایج هر مطالعه را برطبق نیازهای مخصوص آنها عمیق تر بررسی کنند.
کلیدواژه ها: اقدامات متقابل ایمنی جاده | خطرات جاده ای | سودمندی | سیستم آنلاین | مرور | هزینه – سود
|مقاله ترجمه شده|
Do criminally accused politicians affect economic outcomes? Evidence from India
آیا سیاستمداران متهم به جرم و جنایت بر نتایج اقتصادی تأثیر می گذارند؟ مدارک و شواهدی از هند-2019
We study the causal impact of electing criminally accused politicians to state legislative assemblies in India on the subsequent economic performance of their constituencies. Using data on the criminal background of candidates running in state assembly elections for the period 2004 – 2008 and a constituency-level measure of economic activity proxied by the intensity of nighttime lights, we employ a regression discontinuity design and find that narrowly electing a criminally accused politician lowers the growth of the intensity of night-time lights by about 24 percentage points (approximately 2.4 percentage point lower GDP growth). The negative impact is more pronounced for legislators who are accused of serious or financial charges, have multiple accusations, are from a non-ruling party, have less than a college education, or have below median wealth. Overall, we find that the effect appears to be concentrated in the less developed and the more corrupt states. Similar findings emerge for the provision of public goods using data on India’s major rural roads construction program.
Keywords: Criminal Accusations | Politicians | Night-time Lights | Regression Discontinuity| India
Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index
پیش بینی یادگیری ماشین از نانوذرات در سمیت آزمایشگاهی: یک مطالعه مقایسه ای از طبقه بندی کننده ها و طبقه بندی کننده های گروه با استفاده از شاخص Copeland-2019
Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is increasing with their significant economic and societal potential yet to be fully realized. Inroads have been made in our understanding of the risks posed to human health and the environment by NPs but this area will require continuous research and monitoring. In recent years Machine Learning (ML) techniques have exploited large datasets and computation power to create breakthroughs in diverse fields from facial recognition to genomics. More recently, ML techniques have been applied to nanotoxicology with very encouraging results. In this study, categories of ML classifiers (rules, trees, lazy, functions and bayes) were compared using datasets from the Safe and Sustainable Nanotechnology (S2NANO) database to investigate their performance in predicting NPs in vitro toxicity. Physicochemical properties, toxicological and quantum-mechanical attributes and in vitro experimental conditions were used as input variables to predict the toxicity of NPs based on cell viability. Voting, an ensemble meta-classifier, was used to combine base models to optimize the classification prediction of toxicity. To facilitate inter-comparison, a Copeland Index was applied that ranks the classifiers according to their performance and suggested the optimal classifier. Neural Network (NN) and Random forest (RF) showed the best performance in the majority of the datasets used in this study. However, the combination of classifiers demonstrated an improved prediction resulting meta-classifier to have higher indices. This proposed Copeland Index can now be used by researchers to identify and clearly prioritize classifiers in order to achieve more accurate classification predictions for NP toxicity for a given dataset.
Keywords: Machine learning | Voting | Nanotoxicity | Nanoparticles | Copeland Index
Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model
زمانبندی ترافیک هوشمند هدایت شده تئوری صف از طریق تجزیه و تحلیل ویدئو با استفاده از مدل فرایند Dirichlet مخلوط-2019
Intelligent traffic signaling is an important part of city road traffic management systems. In many coun- tries, it is done through supervised/semi-supervised ways. With the advances in computer vision and machine learning, it is now possible to develop expert systems guided intelligent traffic signaling sys- tems that are unsupervised in nature. In order to schedule traffic signals, it is essential to learn the traf- fic characterization parameters such as the number of vehicles, their arrival and departure rates, etc. In this work, we use unsupervised machine learning with the help of a modified Dirichlet Process Mixture Model (DPMM) to measure the aforementioned traffic parameters. This has been done using a new fea- ture, named temporal clusters or tracklets extracted using DPMM. Detailed analysis on tracklet behavior during signal on/offperiod has been carried out to derive a queuing theory-based method for signal du- ration prediction. The queuing behavior at a junction is analyzed using tracklets for understanding their applicability. Queue clearance time at the junction has been used for predicting the signal duration with the help of Gaussian regression of historical data. Two publicly available video datasets, namely QMUL and MIT have been used for verification of the hypothesis. The mean absolute error (MAE) of the proposed method using tracklets has been reduced by a factor of 2.4 and 6.3 when compared with the tracks generated using Kernel Correlation Filters (KCF) and Kanade–Lucas–Tomasi (KLT), respectively. Through experiments, we are also able to establish that KCF and KLT tracks do not consider spatial occupancy of the vehicles on roads, leading to error in the estimation. The results reveal that the proposed queuing theory-based approach predicts the signal duration for the next cycle more accurately as compared to the ground truths. The method can be used for building intelligent traffic control systems for roadway junctions in cities and highways.
Keywords: Traffic intersection management | Signal duration | prediction Dirichlet process | Queuing theory | Unsupervised learning | Visual surveillance
Design and field implementation of an impact detection system using committees of neural networks
طراحی و اجرای میدانی یک سیستم تشخیص ضربه با استفاده از کمیته های شبکه های عصبی-2019
Many critical societal functions depend on uninterrupted service of civil engineering infrastructure. Rail- roads represent important infrastructure components of the transportation sector and provide both pas- senger and freight services. Railroad bridges over roadways are susceptible to impacts from overheight vehicles and equipment, which may damage bridge girders or supports and must be investigated after each event. One method of monitoring for vehicle-bridge collisions utilizes accelerometers to monitor for abnormal bridge vibrations corresponding to abnormal activity. Passing trains under normal operat- ing conditions frequently produce significant bridge responses that have similar response characteristics to bridge strikes, but do not need to be investigated. This paper presents an expert system which com- prises committees of artificial neural networks trained to interrogate data collected from accelerometers mounted on the bridge, assess the nature of the acceleration signal, and classify the event as either a passing train or a potentially damaging impact. This system is trained using acceleration time histories from accelerometers installed on 8 low-clearance rail bridges; no finite element model simulations were used for network training or data stream creation. The presented system accurately detects and classifies impacts with average impact detection performance ranging from 91–100% with average false positive rates limited to 0.00–0.75%.
Keywords: Bridge impacts Impact detection | Signal classification | Feature selection | Artificial neural networks
مدیریت ترافیک با استفاده از رگرسيون لجستيک به همراه منطق فازی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 15
تراکم ترافیک یکی از مشکلات عمده در اکثر شهرهای سراسر جهان است و منجر به مشکلاتی مانند آلودگی، اتلاف وقت، ترافیک طولانی در جاده ها و حوادث می شود. بهبود زیرساخت های جاده، راه حل عملی برای حل مشکل نیست. در سناریو زندگی واقعی، مسیر کوتاهتر تا مقصد، منجر به جذب اکثریت مردم می شود و گاهی شرایط ترافیک را تشدید می¬کند. بنابراین، اطلاعات ترافیکی در لحظه برای تصمیم گیری هوشمندانه انتخاب مسیر حرکت ضروری است. علاوه بر این، سیستمی شامل فاکتور فاصله نسبت به مقصد با در نظر گرفتن وضعیت ترافیکی آن مسیر، به راه حل مشکل افزوده شد. پارامترهای خاصی نظیر فاصله، شرایط آب و هوایی، موقعیت جغرافیایی، روز هفته و زمان برای حل مشکل در نظر گرفته شد و راه حل هایی برای مشکلات پیدا شد. در این مقاله ترکیبی از رگرسيون لجستيک با منطق فازی مثل تصمیم گیری هوشمندان در انتخاب مسیر بهتر ارائه شد. این روش برای محاسبه احتمال هر مسیر، با در نظر گرفتن اطلاعات ترافیکی لحظه ای، فاصله و جاده استفاده شد و سپس برای تصمیم گیری بروی سناریوی نامطلوب استفاده گردید. روش پیشنهادی تعداد پارامترهایی مانند فاصله، شرایط آب و هوایی، موقعیت جاده، روز از هفته و زمان را در نظر می گیرد.
کلید واژه : رگرسيون لجستيک | مدیریت ترافیک | تراکم | منطق فازی | الگوریتم بهینه سازی | کنترل فازی
|مقاله ترجمه شده|
“After all these years” – New Zealands quota management system at the crossroads
"پس از این همه سال" - نیوزلند سیستم مدیریت سهمیه در تقاطع-2018
New Zealand has rightly been admired for a new and innovative system of fishery management (the QMS) where ITQs to commercial fishers have been granted in perpetuity. By the turn of the century, the system was seen as a model for how to get the incentives right. 15 years later, it is worthwhile considering the challenges that have not been solved. The article focusses in particular on social aspects such as the labour conditions in the charter fleet, the discard problems, the relationship between the commercial and the recreational sector, the compli cated procedures involved in setting and changing TACs, the involvement of Maori in fisheries management and finally on the relationship to the aquaculture sector. The main message is that strong rights to one group (the quota owners), without sorting out the rights for the other stakeholders in the marine area, have created long term problems, which now partly paralyses the entire QMS. This also implies some serious lessons for countries who would like to copy the QMS; the system comes with a cost.
Project studies: What it is, where it is going
مطالعات پروژه: چیست، چه موقع می آید-2018
Project organising is a growing field of scholarly inquiry and management practice. In recent years, two important developments have influenced this field: (1) the study and practice of projects have extended their level of analysis from mainly focussing on individual projects to focussing on micro- as well as macro-level concerns around projects; and (2) there has been a greater interest in different kinds of scholarly inquiry. Taken together, these two developments call for closer scrutiny of how the levels of analysis and the types of inquiry are related and benefit each other, and of the explanations of project practices they could offer. To discuss avenues for future research on projects and project practice, this paper suggests the notion of project studies to better grasp the status of our field. We combine these two sets of ideas to analyse the status and future options for advancing project research: (1) levels of analysis; and (2) type of research. Analysing recent developments within project studies, we observe the emergence of what we refer to as type 3 research, which reconciles the need for theoretical development and engagement with practice. Type 3 research suggests pragmatic avenues to move away from accepted yet unhelpful assumptions about projects and project organising. The paper ends with an agenda for future research, which offers project scholars a variety of options to position themselves in the field of project studies, and to explore opportunities in the crossroads between levels of analysis and types of research.
keywords: Levels of analysis |Research |Project studies |Project organising |Scholarship |Sociology of science |Project management