Average Daily Volume Research Articles

Analysis of hydraulic load of a wastewater treatment plant in Jasło

Abstract The paper presents an analysis of hydraulic load in a wastewater treatment plant (WTP) in Jasło. The study was based on the records of daily sewage volume entering the treatment plant within a multi-year period of 2010–2014. The analysis took into account the average daily amount of incoming sewage, the maximum daily peaking factor for the incoming sewage, changes in the sewage volume depending on specific month and day of a week, and class intervals with the greatest frequency of occurrence. The analysis revealed that the average daily volume of the sewage entering the WTP in Jasło in the investigated multi-year period was 13 045 m3·d−1. The amount of incoming sewage was variable, as evidenced by the maximum peaking factors of daily sewage inflow that ranged from 1.07 to 2.78, depending on a specific month. The sewage admission was the largest in March, May and June and on Saturdays. Sewage volume interval most often occurring at the WTP in Jasło was the one between 8 000 and 10 000 m3·d−1. The study results indicated that the facility was hydraulically underloaded.

Benchmarking laboratory observation uncertainty for in-pipe storm sewer discharge measurements

Summary The uncertainty associated with discharge measurement in storm sewer systems is of fundamental importance for hydrologic/hydraulic model calibration and pollutant load estimation, although it is difficult to determine as field benchmarks are generally impractical. This study benchmarks discharge uncertainty in several commonly used sensors by laboratory flume testing with and without a woody debris model. The sensors are then installed in a field location where laboratory benchmarked uncertainty is applied to field measurements. Combined depth and velocity uncertainty from the laboratory ranged from ±0.207–0.710 in., and ±0.176–0.631 fps respectively, and when propagated and applied to discharge estimation in the field, resulted in field discharge uncertainties of between 13% and 256% of the observation. Average daily volume calculation based on these observations had uncertainties of between 58% and 99% of the estimated value, and the uncertainty bounds of storm flow volume and peak flow for nine storm events constituted between 31–84%, and 13–48% of the estimated value respectively. Subsequently, the implications of these observational uncertainties for stormwater best-management practice evaluation, hydrologic modeling, and Total Maximum Daily Load development are considered.

Toward a Better Estimation of Annual Average Daily Bicycle Traffic: Comparison of Methods for Calculating Daily Adjustment Factors

Several methods exist to calculate and apply adjustment factors for bicycle traffic. The reported accuracy of these factors differs from one study to another, making it difficult for transportation professionals to decide which method is preferable. The objective of this study is to compare the estimation accuracy of methods used to calculate daily adjustment factors and to quantify the performance of these methods relative to their mathematical intensity. Three methods of calculating daily adjustment factors were studied: the AASHTO method, the monthly and weather-specific method, and the day-of-year method. The estimation accuracy of annual average daily bicycle traffic volumes was assessed. The results supported the superiority of day-of-year factors, for which the mean absolute percent error (MAPE) was about 17.5%. Second was the monthly and weather-specific factors (MAPE ≈ 24.5%), and third was the AASHTO factors (MAPE ≈ 30.0%). Detailed error analysis was carried out to select the best days for bicycle volume data collection. The daily factor was modeled against day-specific attributes, such as weather conditions and day type, in an attempt to compute generic factors that are applicable to other locations. The model showed very good fit to the data with a coefficient of determination of about .87. The model was further used to develop daily factors for the validation data set and hence calculate annual average daily bicycle volumes. The MAPE was found to be 26.4% for all days and 20.5% for weekday data. The paper provides insights on the advantages and disadvantages of each calculation method and shows the priority preference of application.

Evaluation of chemical residues originated from automatic hematological analyzers in the Municipality of Guarulhos [State of São Paulo, Brazil

Introduction: If incorrectly managed, dangerous chemical residues produced by clinical analysis laboratories are worrisome. These residues can represent serious environmental and human health hazards if disposed improperly in the sewage system. Currently, the laboratories take over strategies with the potential to minimize the negative environmental impacts and public health risks. To demonstrate the current scenario and contribute to the sustainable development of these laboratories, it is necessary to evaluate the toxicity of hematological equipment´s chemical residue effluents. Methods: A questionnaire was addressed to employees of 15 clinical analysis laboratories of the Municipality of Guarulhos, Sao Paulo / Brazil. All these laboratories dispose the hematological equipment’s residues into the sewage system. The objective of the questionnaire was to appraise the daily average hematological exams performed in 2012. Through the data collected with equipment suppliers and analysis of the concentration of dangerous chemical residues diluted in the municipal sewage network, it was possible to calculate the volume of dangerous chemical residues generated by this equipment. Results: The data showed a daily average of 2,800 examinations per laboratory, generating a daily disposal of 131,600 mL of chemical residues. Only formic acid was identified as dangerous chemical residue. About 1,680 mg per day of this residue is disposed. Diluted in the average daily sewage volume of 32,500 m 3 , it represents a concentration of 0.052 mg/m 3 or 5.2 x 10 -5 mg / L. Conclusion: Despite the large volume of chemical residues generated by hematological analyzers and disposed into the sewage system of Guarulhos, the data indicates low toxicity concentration in the sewage system. Therefore, there is no evidence of enough toxic residues to be responsible for creating environmental risks.

Verification of Methodical Procedure for Determining the Traffic Volumes Using Short-term Traffic Surveys

The traffic volume is frequently used measure of road capacity. Traffic volumes are suitable for urban planning, construction and upgrading of roadways or land-use planning decisions, while it is necessary to plan the construction of road infrastructure in the next few decades ahead on the basis of prognosis of road traffic.This paper outlines the problems of calculating the traffic volumes in Czech Republic. Especially the authors focus on the verification of methodical procedure for the determination of resulting traffic volumes using the short-term traffic surveys. The methodology TP 189 for determining the Annual Average Daily Traffic volumes (AADT) reflects the conversion coefficients which were statistically derived from long-term measurements of traffic intensities. It can be assumed that different traffic characters exist on different types of roads, for which no coefficients are set yet. Authors of the paper use data obtained from real long-term measurements of traffic volumes on the various categories of roads and try to determine categories of roads for which current conversion coefficients to determine AADT cannot be applied

Understanding ETFs: Trading and Valuation

Examining the trading characteristics of the exchange-traded fund (ETF) product set enables investors to under-stand how growth is spreading out among a wider variety of products. Investors have learned more about the mechanisms underlying ETFs and are now able to use more of the ETF product set. Useful metrics such as implied liquidity enable investors to see potential ETF liquidity in a quantitative manner and expand the investment options in their portfolios. Moreover, understanding the nuances of executing ETFs efficiently is critical as investors move beyond the small number of ETFs that present high average daily trading volumes and utilize products with high implied liquidity but lower volumes. <b>TOPICS:</b>Exchange-traded funds and applications, statistical methods

Friksi Perdagangan dan Korelasinya Terhadap Karakteristik Perdagangan Pada Data Keuangan Berfrekuensi Tinggi Di Bursa Efek Indonesia

The sources of trading friction are studied, robust empirical measures of friction are provided. Four distinct measures of trading friction are computed from transactions data for 200 IDX stocks. Each observation for a company is an average over the 245 daily observations.All friction measures are expressed as a percentage of the average price (mentioned as the proportional half spread). The degree to which the various measures of trading frictions are associated with each other and with trading characteristics of stocks is examined. All the total friction measures are strongly related to the same variables in the same way. For association of trading friction with trading characteristics, the independent variables are the log of the average daily dollar volume, the stock's, the log of the average closing price and the log of the average number of trades per day. Quoted, effective and traded half spread measures decrease in number of trades, average daily dollar volume and stock price, and these variables are statistically significant in Indonesian Stock Exchange.

An inventory-based simulation model for annual-to-daily temporal freight assignment

In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF3 data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.

Impact of Trading Volume on Prediction of Stock Market Development

The paper focuses on the influence of trading volume on quality of prediction of stock market development. The main objective of this article is to assess the influence of stock trading volume level on quality of prediction with use of technical analysis. The research was applied on stocks included in the S &amp; P 500 index. Based on average daily trading volume, three aggregate indexes were constructed. The dynamics of index return volatility was modeled by GARCH-class models. GARCH(1,1), GJR and EGARCH models were estimated for each time series. The in-sample evidence indicated that the return volatility of the indexes can be characterized by significant persistence and asymmetric effects. The best estimate of each model was produced for the index of stocks with the highest average trading volume.However the result could differ based on the observed period, the volatility structure of the examined data supports the idea that influential investors respond to various shocks in the market primarily by closing or opening their largest position.The importance of the level of trading volume for the prediction of financial time series development was shown in the paper. This finding could help generate such volatility structure of time series which would allow to explain development of the time series by various models with better results.

Determination and Application of Standard K Factors for Bicycle Traffic

This research is concerned with the calculation and application of the design hour factor, also known as the K factor, for bicycle traffic. Two variations of the K factor were developed. The first variation was referred to as Kp/d and was calculated as the ratio between the peak hour volume and the total daily volume. The second variation was referred to as Kp/AADB and was computed as the ratio of the daily peak hour volume to the annual average daily bicycle volume (AADB). An application was presented on how to use the developed K factors to calculate the AADB, and the estimation accuracy of using the two variations of the K factor was assessed. Five groups of the K factors were calculated according to different criteria. The results showed that the Kp/d factors classified by groups of similar links, weekdays and weekends, and year led to the best results where the average estimation error of the daily bicycle volume was about 16.6%. Furthermore, the use of the best set of K p/d factors along with daily and monthly adjustment factors for the estimation of the AADB value led to an average estimation error of 28.3%. Conversely, the set of Kp/AADB factors classified by weekdays and weekends, month, and year led to the lowest estimation error with an average of 28.9%. The paper provides insights on the selection of data collection days and months, which could improve the design of data collection programs of bicycle traffic.

Nonmotorized Site Selection Methods for Continuous and Short-Duration Volume Counting

This paper outlines the process for selecting sites to perform counts on transportation facilities in the context of establishing a nonmotorized traffic volume counting program. Many agencies are well practiced in collecting motor vehicle traffic counts and estimating annual average daily traffic volumes for motor vehicles on the basis of knowledge founded on years of research and experience. Selecting sites is one component of developing a consistent system for quantifying nonmotorized travel so bicycle and pedestrian volumes can ultimately feed into tools to measure existing trends and model future increases in nonmotorized trips at site, corridor, and regional levels. The outcome of collecting count data can ultimately assist in evaluating facility usage over time, better inform the project prioritization process, and provide evidence to support nonmotorized facility inclusion through Complete Streets planning and improve planning for active transportation. This paper outlines the goals of a nonmotorized traffic monitoring program and objectives of the site selection process. From the experience of a pilot project conducted by the North Carolina Department of Transportation, the site selection method details the following components: gathering potential sites, conducting a site visit, gathering additional data needed to inform the decision-making process, and ultimately selecting sites and developing an equipment inventory.

Level-of-Service Model for Protected Bike Lanes

Several methods exist for quantifying the quality of service provided by a roadway from a bicyclist's perspective; however, many of these models do not consider physically protected bike lanes. Of those that do, none was developed on the basis of empirical data from the United States. This gap is problematic because engineers, planners, and elected officials are increasingly looking to objective performance measures to help guide both transportation project design and funding prioritization decisions. This paper addresses this gap by presenting a cumulative logistic model to predict user comfort on protected bike lanes that was developed from data collected during in-person video surveys. The surveys were conducted in Portland, Oregon, with video footage gathered in Portland; Chicago, Illinois; and San Francisco, California. The model is for road segments only and not signalized intersections. It complements the Highway Capacity Manual 2010 level-of-service methods by providing an analysis procedure for a facility type that is not currently included in the manual. The model indicates that the type of buffer, direction of travel (one-way versus two-way), adjacent motor vehicle speed limit, and average daily motor vehicle volumes are all significant predictors of bicyclist comfort in protected bike lanes. The model predicts a mean value of A or B on a scale of A (most comfortable) to F (least comfortable) for all protected bike lane video clips that were used in the survey. Consistent with previous research findings, survey respondents report that protected bike lanes are generally more comfortable than other types of on-street infrastructure. The authors thank Mark Person of Mackenzie and Sam Thompson and Tara Goddard of Portland State University for their assistance in recruiting survey participants. Theo Petritsch of Sprinkle Consulting, Inc., provided the video clips from NCHRP Project 3-70, which were used for video quality comparison. The Department of Civil Engineering at Portland State University contributed funds used in this project. The authors acknowledge the U.S. Department of Transportation, the National Institute for Transportation and Communities, and People for Bikes for funding the related project.

The study of the effect of reducing input variables in the modeling process of Tehran-Saveh freeway accidents

Since Iran is among the countries where the rate of accidents caused by inattention to safety rules and factors affecting it always has been rising and according to the capabilities of neural network model in predicting accidents, the main purpose of this paper can be Using principal component analysis to identify factors influencing on modeling process Tehran-Saveh freeway accidents that is perhaps one of the country one of crowded freeway. In order to understand better the results obtained for determine the independent variables into the principal component analysis model, benchmarking KMO is used and variables such as traffic, petrol rationing and environmental variables were used as independent variables that has effect on the dependent variable accidents rate. In this study In addition to evaluation of the efficacy of artificial neural network model to model the regression of natural logarithm of the freeway traffic modeling, the accuracy of the models that built in accidents involving artificial neural network models and regression of natural logarithm in before and after the removal of less important variables studied and the results indicate that removal of minor factors of the PCA in the process of modeling, accuracy built models with fundamental changes did not go up and as a result it has been proved that the average daily traffic volume and average vehicle speed is the most important role in accidents of this freeways.

Seeking Optimal ETF Executionin Electronic Markets

Exchange-traded funds (ETF) have had a phenomenal run in recent years, with both their number and assets under management (AUM) growing dramatically. Despite this rapid growth, the average daily exchange-traded volume of these ETFs has stalled and now appears to be in decline. However, on-screen liquidity is not a true reflection of an ETF’s total implied liquidity, which is composed of liquidity of the underlying basket, related derivatives, correlated trading vehicles, and exchange-traded volume. Because of these properties, traders need to understand the uniqueness of ETF market microstructure and use the right trading solutions to achieve optimal execution efficiency. Using empirical statistical analysis, the authors show that for small orders on relatively liquid ETFs, institutional clients are better off using broker execution algorithms to extract liquidity without creating significant market impact. For large orders on relatively illiquid ETFs, institutional clients should leverage an electronic solution that can anonymously source block liquidity from market makers to achieve optimal execution.

Single versus bilateral external ventricular drainage for intraventricular fibrinolysis using urokinase in severe ventricular haemorrhage

Background: Intraventricular fibrinolysis (IVF) through bilateral external ventricular drains (EVD) may provide better access of the thrombolytic agent to the intraventricular clot, potentially leading to faster clot clearance.Objective: To compare the feasibility and safety between single and bilateral EVD groups.Methods: Patients with spontaneous intraventricular haemorrhage (IVH) (Graeb score ≥ 5) were treated with IVF. The selection for placement of one or two EVDs was randomized. The average daily CSF drainage volume, the indwelling EVD time, the time for monitoring in intensive care unit (ICU), intracranial re-haemorrhage and intracranial infection, Glasgow coma score (GCS), Graeb score and the reserved IVH volume have been analysed for patients with one (group I, n = 22) or two EVDs (group II, n = 25).Results: Significant difference was found in the average daily CSF drainage volume between the two groups (85.2 (SD = 13.7) vs. 108.5 (15.9) ml). No difference was found in the indwelling EVD time, the time for monitoring in the ICU. Through repeated measurements of the variance analysis, the test for a difference in IVH volume over time was statistically significant (F = 466.981, p = 0.000) and the test for the interaction between treatment and time was also significant (F = 5.033, p = 0.002), indicating that the IVH volume decreased over time in both groups, with a sharper decrease in Group II. Intracranial re-haemorrhage and infection was not found in this study.Conclusion: The results provide some evidence to support the use of bilateral EVDs for IVF in patients with severe IVH.

An Update on the Performance of Actively Managed Etfs

Actively managed ETFs are a relatively recent introduction to the investing landscape, and understanding their performance against passive funds is becoming increasingly important. Consistent with preliminary studies, I find that active funds are more volatile than their passive counterparts and do not provide an absolute return advantage. Thus, active ETFs are generally not good substitutes for existing passively managed funds. However, in contrast to prior studies, I find that performance metrics based on relative risk (e.g., Information and Treynor ratios) suggest that active funds may be good additions to existing portfolios for their diversification benefits. I also find that these relative benefits are primarily concentrated in the funds with the highest average daily trading volume. <b>TOPICS:</b>Exchange-traded funds and applications, portfolio construction, volatility measures

Thirty Volatility Indexes: Worldwide Tools to Gauge Sentiment and Diversify Portfolios

In the two decades since the 1993 launch of the widely followed Chicago Board Options Exchange (CBOE) Volatility Index (VIX), dozens of new volatility indexes have been introduced. This article provides an overview of 30 volatility-related indexes; 25 of the indexes are indicators that gauge investor sentiment regarding expected volatility of traded instruments such as commodities, interest rates, currencies, or worldwide stock indexes, and 5 indexes are designed to serve as benchmarks for investable performance of strategies that use VIX futures or options. Volatility indexes are used as market signals in asset allocation programs. Over the past 14 years, many investors have struggled to cope with multiple bear markets for stocks, higher correlations among many investments, and lower risk-adjusted returns for their portfolios. Investors now use long-volatility strategies with the goal of diversifying their portfolios and short-volatility strategies with the goal of enhancing risk-adjusted returns. Increased interest in volatility indexes is reflected by the fact that average daily volume for VIX options rose from 23,491 contracts in 2006 to 442,959 in 2012.

Mispricing and Trading Profits in Exchange-Traded Notes

This article investigates whether a simple long–short weekly trading strategy based on mispricing among exchange-traded notes (ETNs) generates profits in excess of the S&P 500 Index over the sample period of June 6, 2006 to January 30, 2012. Ignoring transaction costs, liquidity, and short-selling constraints, the authors find the following: 1) Mispricing is prevalent among ETNs. They enter into 90 trades over our 295-week period using a mispricing threshold of 5% and requiring a minimum average daily volume of 20,000 shares. 2) Total returns to the authors’ strategy are significantly higher than the S&P 500, which had a total return of 3.70% over the same period. The strategy generated total returns ranging from 9% to 110% over a mispricing threshold range of 8% to 14%, depending on the study’s minimum daily trading volume requirements. 3) Nearly all the trades and all the profits from this strategy come from the short side of the portfolio. This is consistent with previous empirical work demonstrating that ETNs are more likely to be overpriced than under priced.

Assessing Transferability of Highway Safety Manual Crash Prediction Models to Data from Italy

For decades, crashes have been studied as discrete events with the focus on the circumstances of the crash. This type of analysis has been used to identify the characteristics of roadway features associated with higher crash experience, but other factors, such as traffic volumes, driver characteristics, land use, and environmental conditions, are also needed to explain or describe crash events. The Highway Safety Manual (HSM) provides a predictive method to estimate the expected average crash frequency of a site in given geometric and geographic conditions over a specific period for a specific annual average daily traffic volume. The study presented here investigated whether the modeling results closely matched the crash records. The HSM algorithms were used to assess transferability as a whole. The results suggested that implementing the HSM techniques should foster the development of local safety performance functions and accident modification factors. Calibration preserved the original HSM model form and the relationship between independent variables and crashes. To adjust the base predicted crash frequency to meet the current conditions, the accident modification factor calculations for lane width, horizontal curves, and vertical grades were made. Crash types (head-on and side collisions, single-vehicle crashes, and rear-end collisions) were investigated on the basis of the vertical grade and the curvature indicator. The estimated model provides planners and designers with a tool better able to target and select countermeasures to address these specific aspects and results in improved project selection and improved safety.

Optimal Order Scheduling for Deterministic Liquidity Patterns

We consider a broker who has to place a large order which consumes a sizable part of average daily trading volume. The broker's aim is thus to minimize execution costs he incurs from the adverse impact of his trades on market prices. In contrast to the previous literature (see, e.g., Obizhaeva and Wang [A. Obizhaeva and J. Wang, J. Financial Markets, 16 (2013), pp. 1--32] and Predoiu, Shaikhet, and Shreve [SIAM J. Financial Math., 2 (2011), pp. 183--212]), we allow the liquidity parameters of market depth and resilience to vary deterministically over the course of the trading period. The resulting singular optimal control problem is shown to be tractable by methods from convex analysis, and, under minimal assumptions, we construct an explicit solution to the scheduling problem in terms of some concave envelope of the resilience-adjusted market depth.