Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks

Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power, motivates us to improve the prediction accuracy via deep-learning approaches. Recurrent neural networks have become one of the most popular methods for time series forecasting, however, due to the variety of these networks, the question that which type is the most appropriate one for this task remains unsolved. In this paper, we use three kinds of recurrent neural networks including simple RNN units, GRU and LSTM neural network to predict short-term traffic flow. The dataset from TAP30 Corporation is used for building the models and comparing RNNs with several well-known models, such as DEMA, LASSO and XGBoost. The results show that all three types of RNNs outperform the others, however, more simple RNNs such as simple recurrent units and GRU perform work better than LSTM in terms of accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279, arXiv:1804.04176 by other author

On particles in the Arctic stratosphere

Soon after the discovery of the Antarctic ozone hole it became clear that particles in the polar stratosphere had an infl uence on the destruction of the ozone layer. Two major types of particles, sulphate aerosols and Polar Stratospheric Clouds (PSCs), provide the surfaces where fast heterogeneous chemical reactions convert inactive halogen reservoir species into potentially ozone-destroying radicals. Lidar measurements have been used to classify the PSCs. Following the Mt. Pinatubo eruption in June 1991 it was found that the Arctic stratosphere was loaded with aerosols, and that aerosols observed with lidar and ozone observed with ozone sondes displayed a layered structure, and that the aerosol and ozone contents in the layers frequently appeared to be negatively correlated. The layered structure was probably due to modulation induced by the dynamics at the edge of the polar vortex. Lidar observations of the Mt. Pinatubo aerosols were in several cases accompanied by balloon-borne backscatter soundings, whereby backscatter measurements in three different wavelengths made it possible to obtain information about the particle sizes. An investigation of the infl uence of synoptic temperature histories on the physical properties of PSC particles has shown that most of the liquid type 1b particles were observed in the process of an ongoing, relatively fast, and continuous cooling from temperatures clearly above the nitric acid trihydrate condensation temperature (TNAT). On the other hand, it appeared that a relatively long period, with a duration of at least 1-2 days, at temperatures below TNAT provide the conditions which may lead to the production of solid type 1a PSCs

Predicting passenger origin-destination in online taxi-hailing systems

Because of transportation planning, traffic management, and dispatch optimization importance, passenger origin-destination prediction has become one of the most important requirements for intelligent transportation systems management. In this paper, we propose a model to predict the next specified time window travels' origin and destination. To extract meaningful travel flows, we use K-means clustering in four-dimensional space with maximum cluster size limitation for origin and destination zones. Because of the large number of clusters, we use non-negative matrix factorization to decrease the number of travel clusters. Also, we use a stacked recurrent neural network model to predict travel count in each cluster. Comparing our results with other existing models shows that our proposed model has 5-7% lower mean absolute percentage error (MAPE) for 1-hour time windows, and 14% lower MAPE for 30-minute time windows.Comment: 25 pages, 20 figure

Wild Goats Optimization Approach for Capacitor Placement Problem

This paper deals with Capacitor Placement (CP) issue. The topic is an optimization problem including two types of variables: capacitor location as an integer variable, capacitor size as a continuous one. To cope with this problem, a new approach entitled Wild Goats Algorithm (WGA) is used. WGA is a new heuristic approach which has been proved recently. In this paper, WGA is successfully implemented to the CP problem with the objective of total loss reduction. Power flow criteria as well as operation constraints are all together accommodated in the process of optimization. Two various scenarios at three load levels are also recognized to cover all possible conditions. The validity of the WGA approach in handling CP problem is assured by testifying it on IEEE 33-bus and 69-bus test systems

Investigating the Effect of Geocell Changes on Slope Stability in Unsaturated Soil

The purpose of this research is to investigate the performance and efficiency of reinforced slope in the stability of geocell layers in unsaturated soil conditions. Slope reinforced with geocell acts like a beam in the soil due to the geocell having a height (three-dimensional). Due to its flexural properties, it has moment of inertia as well as bending strength, which reduces the displacement and increases the safety factor of the slope. Taking into consideration unsaturated conditions of soil contributes a lot to making results close to reality. One of the well-known models among elastoplastic models for modeling unsaturated soils is Barcelona Basic Model, which has been added to the FLAC2D software by codification. Changes in thickness, length and number of geocell layers are remarkably effective on slope stability. The results show that the geocell\u27s reinforcing efficiency depends on the number of layers and depth of its placement. As the depth of the geocell\u27s first layer increases, the lateral and vertical side elevation of the upper part of the slope increases with respect to the elevation. Load capacity increases with increasing geocell length. By increasing the length of the geocell layer, the joint strength, the mobilized tensile strength, and the bending moment are increased. At u/H = 0.2, an increase in the bending momentum of about 20% occurs with increasing geocell thickness. In u/H = 1, the increase in bending momentum is 10.4%. In addition, by increasing the thickness of the geocell, the Value of moment of the inertia increases and, as a result, the amount of geocell reinforcement bending moment increases

Independent Predictors of One-Month Mortality in Patients with Intracranial Hemorrhage; a Cohort Study

Introduction: Predicting the outcome is one of the most frequent and important issues when approaching patients with intracranial hemorrhage (ICH). Objective: This study aimed to evaluate the correlation of SUSPEKT score variables plus electrocardiogram (ECG) abnormalities with one-month mortality of patients with ICH presenting to emergency department (ED). Methods: In this cohort study, adult patients presenting to the EDs of three educational hospitals, during one year, were followed and their one-month mortality rate as well as independent predictors of outcome among the variables of SUSPEKT score plus electrocardiography findings were evaluated. Results: One hundred seventy-seven patients with the mean age of 63.07±14.89 years were studied (59.9%). The most common locations of intra-parenchymal hemorrhage were basal ganglia (53.7%) and cortex (36.2%). Ninety-two (52.0%) of cases had at least one ECG abnormality. The most frequent ECG abnormalities were ST segment depression (20.3%), T wave inversion (16.4%), and left ventricular hypertrophy (14.7%). Thirty (16.9%) cases died during the 30-day follow-up. Survived and non-survived cases were significantly different regarding the location of intra-parenchymal hemorrhage (p < 0.0001), presence of intraventricular hemorrhage (IVH) (p = 0.007), ST segment elevation (p < 0.0001), bradycardia (p < 0.0001), tachycardia (p < 0.0001), arterial fibrillation (p < 0.0001), blood sugar (p = 0.044), and serum level of potassium (p = 0.022). Conclusions: The location of hemorrhage (basal ganglia), higher blood sugar, and presence of ECG abnormalities (ST segment elevation, tachycardia, bradycardia, atrial fibrillation) were among the independent predictors of one-month mortality of ICH patients in this study

Evaluation process in viewpoints of academic staff and students in Shahrekord University of Medical Sciences

زمینه و هدف: ارزشیابی از جمله فعالیت های کیفی است که با وجود لزوم انجام، روند اجرای آن همواره در بین اعضای هیئت علمی بحث انگیز بوده است. این مطالعه با هدف بررسی نظر اعضای هیئت علمی و دانشجویان در مورد فرآیند ارزشیابی ،معیارهای آن و اولویت بندی شاخص های مندرج در فرم دانشجویان، در جهت بهبود روند ارزشیابی انجام گرفت. روش بررسی: در این مطالعه توصیفی- تحلیلی 60 نفر از اعضای هیئت علمی دانشگاه و 370 دانشجو با روش طبقه بندی بر اساس دانشکده و رشته به صورت تصادفی ساده وارد مطالعه شدند. ابزار جمع آوری اطلاعات دو پرسشنامه پژوهشگر ساخته جهت مدرسین و دانشجویان با روایی و پایایی مناسب بود. داده ها به کمک آزمون های آماری tو کای دو و تحلیل واریانس یک طرفه تجزیه و تحلیل گردید. یافته ها: بر اساس نتایج این مطالعه کلیه مدرسین با ارزشیابی توسط دانشجو و مسئولین موافق بوده ولی از نظر آنان ارزشیابی توسط دانشجو اولویت داشت. مسئولین ارزیابی کننده از نظر آنان به ترتیب اولویت مدیر گروه، معاون آموزشی پایه و بالینی و ریاست دانشکده بودند. 63 از مدرسین با ارزشیابی توسط همکار و 37 با روش خودسنجی موافق بودند. 89 دانشجویان با ارزشیابی مدرس موافق بوده 53 آن را دارای تاثیر مثبت عنوان نمودند. زمان ارزشیابی در موقع امتحان از نظر 62 آنان نامناسب بود. معیارهای یک مدرس خوب از نظـــر دانشجویان و مدرسین مشابه و اولویت عمده، توانایی علمی مدرس و روش تدریس بود. مهمترین شاخص ها از نظر هر دو گروه تسلط به محتوای درس و قدرت بیان و تفهیم مطالب درسی بود. هر دو گروه استفاده از وسایل سمعی و بصری مناسب را از شاخص های کم اهمیت عنوان نمودند ولی ارایه طرح درس و پذیرش انتقادات و پیشنهادات دانشجویان از نظر دانشجویان اهمیت بیشتری داشت. نتیجه گیری: نتایج این مطالعه بر اهمیت ارزشیابی تاکید نموده و ارزشیابی مدرس توسط دانشجو را علیرغم وجود بعضی از مشکلات اولویت اول می داند. استفاده از روش های مکمل جهت افزایش اعتبار ارزشیابی و در نظر گرفتن اهمیت بیشتر جهت معیارها و شاخص های دارای اولویت در بهبود فرآیند ارزشیابی و افزایش رضایت مدرسین تاثیرگذار خواهد بود

Studying the Impact of Firm Characteristics on the Relationship between Product Market Competition and the Cost of Capital (Case study: Companies Listed in Tehran Stock Exchange)

The main objective of this study was to investigate the characteristics of companies on the relationship between product market competition and capital costs of the companies listed in Tehran Stock Exchange. Therefore, the Herfindahl Hirschman index was used to measure competition in the product market. Furthermore, the characteristics of the companies were examined from three perspectives capital structure, disclosure quality and profitability. The population of this research was all companies listed in Tehran Stock Exchange and the sample consists of 81 companies listed in Tehran Stock Exchange which were studied from 2005 to 2012. Multivariate linear regression analysis within the framework of Baron and Kenny (1986) was used to test the hypothesis. The results indicated a significant and inverse relationship between the competition in the product market, capital structure and profitability of companies operating in the Tehran Stock Exchange and a significant and positive relationship between product market competition and quality of information disclosure. Moreover, the results showed a significant and negative correlation between the competition in the product market and the cost of equity. Also, the results showed that variable quality of information disclosure has minor role in mediating the relationship between product market competition and the cost of equity, but the company does not have a variable capital structure and profitability

Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model

The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017-2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively