Evaluation of Dynamic Speed Display Signs

2005 ◽  
Vol 1918 (1) ◽  
pp. 92-97 ◽  
Author(s):  
Gerald L. Ullman ◽  
Elisabeth R. Rose
Keyword(s):  
Least Squares ◽  
High Speed ◽  
Signalized Intersections ◽  
Speed Limit ◽  
Site Conditions ◽  
Initial Speed ◽  
Least Squares Regression ◽  
Regression Analyses ◽  
Horizontal Curves ◽  
Transition Speed

This paper describes an analysis of the effectiveness of dynamic speed display signs (DSDSs) installed in several permanent locations. Sites evaluated included a school speed zone, two transition speed zones in advance of a school speed zone, two sharp horizontal curves, and two approaches to signalized intersections on high-speed roadways. Data were collected before the DSDSs were installed, about one week after installation to determine initial effects of the signs upon vehicle speeds, and again about four months after installation to determine how well the initial speed reductions were maintained. Researchers analyzed average speeds, 85th percentile speeds, and the percentage of the sample exceeding the speed limit. In addition, least-squares regression analyses between the speed of a vehicle upstream of the DSDS and that vehicle's speed measured again at the DSDS were performed to determine whether the sign affected higher-speed vehicles more substantially than lower-speed vehicles. Overall, average speeds were reduced by 9 mph at the school speed zone. Elsewhere, the effect of the DSDS was less dramatic, with average speeds reduced by 5 mph or less depending on the location tested. As expected, those motorists traveling faster than the posted speed limit did appear to reduce their speed more significantly in response to the DSDS than did motorists traveling at or below the posted speed limit. The results of this project suggest that DSDSs can be effective at reducing speeds in permanent applications if appropriate site conditions apply.

scholarly journals A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Sheng Dong ◽  
Jibiao Zhou
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
High Speed ◽  
Signalized Intersections ◽  
Speed Limit ◽  
Vehicle Type ◽  
Decision Tree Classification ◽  
Signal Change ◽  
Signal Period ◽  
Instantaneous Speed

The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.

Partial Least Squares Regression Analysis of Thermal Distortion Behavior for Linear Motor Drive Feed System

2013 ◽  
Vol 690-693 ◽  
pp. 3001-3006
Author(s):  
Xian Kun Lin ◽  
Qian Qian Wu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Speed ◽  
Identification Accuracy ◽  
Machining Process ◽  
Thermal Distortion ◽  
Feeding System ◽  
Least Squares Regression ◽  
Test Bed ◽  
Feed System

In order to explore the effect factors of thermal distortion behavior of the high speed feeding system, a methodology with partial least squares linear regression (PLSR) was brought forward to analyze the thermal process on the basis of experiments. The feeding system test-bed and the thermal behavior measurement system were established for the experiments. The experiments were conducted under both the natural heating and force cooling condition to show the feasibility of the method. The experimental scheme and procedure were also introduced in detail. On the basis of the experiments and the regression calculation, the distribution characteristics of temperature for the high speed feeding drive system and the rule of the thermal distortion behavior were explored with further discussion. The results show that the proposed method can effectively recognize the experimental data and reach high distortion identification accuracy. The resultant factors and their proportions of thermal deformation were presented through regression calculation. The prediction of the distortion at some random axis positions was done with the regression model. The result shows good performance for recognition of the axis deformation. As a result, the study effort provides a theoretical foundation for optimum design and error compensation in machining process.

scholarly journals Education, Service Quality, Accountability, Awareness, and Taxpayer Compliance: Individual Taxpayer Perception

2019 ◽  
Vol 10 (5) ◽  
pp. 420
Author(s):  
Vince Ratnawati ◽  
Ria Nelly Sari ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Service Quality ◽  
Least Squares ◽  
Least Squares Regression ◽  
Regression Analyses ◽  
Education Service ◽  
Directorate General

The aim of this study is to investigate how education, service quality, and accountability affect taxpayer compliance and awareness. A model was developed and tested by using a sample of 253 taxpayers listed on the Directorate General of Taxation in Riau, Indonesia. Data were collected and analyzed by using least squares regression and moderated regression analyses. Results show that education, service quality, and accountability affect taxpayer compliance. The results also indicate that tax awareness strengthen the effects of education, service quality, and accountability on taxpayer compliance.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

An Approach of the Madeira Wine Chemistry

Beverages ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 12 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Carolina Gonçalves ◽  
Mariangie Castillo ◽  
José S. Câmara
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Least Squares ◽  
Partial Least Squares ◽  
Chemical Reactions ◽  
Ageing Process ◽  
Gas Chromatography Mass Spectrometry ◽  
Wine Aroma ◽  
Least Squares Regression ◽  
Volatile Composition

Madeira wine is a fortified Portuguese wine, which has a crucial impact on the Madeira Island economy. The particular properties of Madeira wine result from the unique and specific winemaking and ageing processes that promote the occurrence of chemical reactions among acids, sugars, alcohols, and polyphenols, which are important to the extraordinary quality of the wine. These chemical reactions contribute to the appearance of novel compounds and/or the transformation of others, consequently promoting changes in qualitative and quantitative volatile and non-volatile composition. The current review comprises an overview of Madeira wines related to volatile (e.g., terpenes, norisoprenoids, alcohols, esters, fatty acids) and non-volatile composition (e.g., polyphenols, organic acids, amino acids, biogenic amines, and metals). Moreover, types of aroma compounds, the contribution of volatile organic compounds (VOCs) to the overall Madeira wine aroma, the change of their content during the ageing process, as well as the establishment of the potential ageing markers will also be reviewed. The viability of several analytical methods (e.g., gas chromatography-mass spectrometry (GC-MS), two-dimensional gas chromatography and time-of-flight mass spectrometry (GC×GC-ToFMS)) combined with chemometrics tools (e.g., partial least squares regression (PLS-R), partial least squares discriminant analysis (PLS-DA) was investigated to establish potential ageing markers to guarantee the Madeira wine authenticity. Acetals, furanic compounds, and lactones are the chemical families most commonly related with the ageing process.

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