Queue Length Estimation Algorithm for Signalized Intersection Using Sectional Travel Time Information
|
|
- Paulina Tyler
- 5 years ago
- Views:
Transcription
1 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 Queue Length Estmaton Algorthm or Sgnalzed Intersecton Usng Sectonal Travel Tme Inormaton Mnhyoung LEE Ph.D. Student Dept. o Transportaton Engneerng Unversty o Seoul 90, Jeonnong-dong, Dongdaemun-gu, Seoul Korea Fax: E-mal: mn63954@gmal.com Youngchan KIM Proessor Dept. o Transportaton Engneerng Unversty o Seoul 90, Jeonnong-dong, Dongdaemun-gu, Seoul Korea Fax: E-mal: yckmm@uos.ac.kr Abstract: The purpose o ths research s to develop algorthm calculatng the trac queue length whch s man varable or eectve operaton at sgnalzed ntersecton. For the methods to calculate the trac queue length, there are queue theory and shock wave theory. And, the estmaton o trac queue length by exstng pont detecton system utlzes trac queue theory calculatng trac queue length through the number o vehcles, ths algorthm ntended to estmate more accurate length o trac queue by calculatng the length o physcal trac queue through the shock wave theory. For the stream o algorthm, the normaton o ndvdual vehcle's travel tme s gathered by ground detector n upstream detector and downstream detector, the coordnate o ndvdual vehcle on the workng drawng s estmated and the speed o shock wave creatng trac queue through the value o coordnate estmated or one cycle s calculated. The sutablty o algorthm s evaluated by Smulaton. Key Words: Queue length, Trac Flow Model, Shock wave Theory, ITS 1. INTRODUCTION The total populaton o South Korea, exceeded 50 mllon as o June 01. Socal stablty and envronmental changes ncreased the trac demand. The total length o roads across the country has acheved 105,931Km as o December 011. however, extenson o road per 1,000 populaton are stll.1km despte contnued nvestments n the road. In 01, socal overhead captal(soc), the government's budget accounted or 7% o the total budget, but declned 5.5% year-on-year. and Prorty or road-buldng socal overhead captal (SOC) s to decrease as compared to the nancal nvestment captal needs o other. Thereore as a measure to solve Trac Congeston Cost s contnuously ncreasng, t must seek the ecency measures road operaton that ncreases the unctonalty o the exstng road and mprove ecency. The development o normaton and communcaton technque s currently promotng Intellgent transport system. Also the collecton o trac normaton, processng, provdng, and has been used as the core technology underlyng the eld o control-related system. Unlke the collecton o trac normaton n accordance wth the development o normaton and communcaton and had to collect only a pont detecton system n the past. However road nrastructure and vehcle o ndvdual through communcatons wll collect path normaton between the ndvdual vehcle. Interval detecton system wll be to enhance the qualty o naccurate trac orecasts estmated rom normaton o lmted pont detecton systems o the past. Ths means that the ntersecton operatons more ecent trac management enorcement Urban servce level o the road s determned dependng on the ecency o the control sgnal at the ntersecton s enabled. In ths study, sectonal travel tme normaton s collected va DSRC, to represent the normaton o the length o the queue n the sgnalzed ntersecton. Whch s based on trac low-densty model and shockwave theory. The study objectve s amed at the development o queue estmaton algorthm or sgnalzed ntersecton usng sectonal travel tme normaton.
2 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013. BACKGROUND ISSUES.1 Fundamental Dagram Newell s Smpled Trac Flow Theory Newell proposed model the relatonshp between the densty and trac volume by dvdng prevous parabolc type volume-densty dagram nto uncongested and congested condton. Ths s expressed smply by assgnng uncongested state to ree low speed w1 and congested state to w. Volume-densty relatonshp s dsplayed n lnear orm by dvdng t nto areas o uncongested state and congested state There s a dculty n calculaton a prevous parabolc graph s used, but ths lnear smpled trac low theory s convenent or calculaton and the soluton can be easly derved. Shock wave Theory Fgure 1 q-k dagram o Smpled Trac Flow Theory shock wave s the propagaton movement o wave n accordance wth the change o trac densty and volume. Two derent densty o the area A, B have erased the boundares o the vertcal movng wth the speed o the trac low, the shock propagaton speed can be estmated rom the slope o a straght lne connectng two ponts o trac and densty trac densty relatonshp. Fgure Shock wave n volume-densty relatonshp
3 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013. Queueng Theory Denton o Queue Accordng to the Hghway Capacty Manual 000, queue s dened as a lne o vehcles, bcycles, or persons watng to be served by the system n whch the low rate rom the ront o the queue determnes the average speed wthn the queue. Slowly movng vehcles or people jonng the rear o the queue are usually 6 consdered part o the queue. such a sentence means that not only car stopped stop lne behnd the sgnal control and ncludes all the cars that are aected to the travelng speed by the vehcle queues approachng the ntersecton. Henry X. Lu(009) was dened the length o the queue o the actual sum o both parts. "Standng Queue" s that the vehcle was stopped by a sgnal control and "Movng Queue " s that a vehcle speed o the vehcle s reduced to a certan level below. There are two knds o queue. Frst, "Maxmum queue", reers to the length o queues ormed to the rear o the stop lne when the trac lght turns green equalzaton. Second, "Maxmum back o queue", the maxmum length o the stop lne to the rear end o the queue has been created wthn the same cycle. Queue Length Estmaton Fgure 3 Denton o Queue length Cumulatve arrval and departure analyss queung theory s that nvestgate the number o vehcles that passed through the stop lne o the vehcle arrved at the stop lne to determne queue length. The number o vehcles that make up the queue at any gven tme consst o the subtracton o two parts. The two parts as ollows: Frst, accumulated the vehcle arrves sequentally at eectve red. Second, departure trac volume obtaned by accumulatng the vehcle passed through the ntersecton o tme eectve green. shock wave theory s to calculate the length o the queue unlke those usng only data pont trac o cumulatve arrval and departure analyss queung theory. Frst, shock wave theory s the bass o a correlaton between trac Volume, Densty and Speed, thereore t must determne the volume-densty relatonshp o the target area. Second, t s to collect data n real tme that t s possble to explan the speed o the shock wave. Thrd, Trac volume o exstng - the process o analyzng the shock wave ancllary densty relatonshp occurs n the parabola s mathematcally mpossble. However, trac volume parabolc volume- densty relatonshp s accessble mathematcally through the Smpled Trac Flow Theory o Newell descrbed above. Type o shock wave generated by the sgnal ntersecton may be dvded nto three categores accordng to volume-densty relatonshp.
4 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 q m q m, k ) ( m ` Flow q a, k ) ( a V 3 ` u w V km V 1 D ensty 0, k ) ( j ` k j Fgure 4 n volume-densty relatonshp at sgnalzed ntersecton There s a derence rom the calculated queue length o the queue s calculated through cumulatve arrval and departure analyss queung theory and shockwave theory to estmate. Length s determned based on the cumulatve arrval and departure analyss queung theory, be calculated by multplyng the average length o the vehcle n an amount mathematcal vehcle stored vertcally wth respect to the tme they arrved at the stop lne. thus the error due to the length o the average car length. The length o prevously dened queue ncludes not only vehcles stopped n the queue but also vehcles that decelerated speed by the queue. As an ncrease n the number o vehcles are movng the end pont o the actual queue to upstream. Thus, end ponts o the queue are derent between cumulatve arrval and departure analyss queung theory and shockwave theory to estmate. 3. DEVELOPMENT OF QUEUE LENGTH ESTIMATION ALGORITHM 3.1 Detecton system conguraton In order to calculate sectonal travel tme o the path by sectonal detecton system, t s necessary to specy the exact endng and startng pont. At ths tme, t s necessary to select the endng and startng pont whch s regarded by consderng the actors that could aect the passage tme o the vehcle. Elements that have the greatest eect on the travel tme o the vehcle at sgnalzed ntersecton consecutve s the sgnal tme. Thereore, the detector are Installed ahead o the ntersecton. and There s a sngle ntersecton between the two detectors. Fgure 4 volume-densty relatonshp at sgnalzed ntersecton
5 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, Delay calculaton o the ndvdual vehcle measurng the tme o the pass ponts and the ID o each vehcle s possble to calculates the passage tme o ndvdual vehcles. Travel tme o ndvdual vehcles s a value that ncludes a delay tme o the sgnal n the sgnalzed ntersecton. tab tb ta (1) t A = Tme the vehcle has passed through the upstream detector t = Tme the vehcle has passed through the downstream detector t B AB =Passage tme rom A to B Delay o the ndvdual vehcle s calculated rom the derence between tme at ree low speed and actually travel tme o each vehcle. Delay L tab () v L = Dstance o the segment AB v = Free low speed Delay = Delay tme o vehcle n the segment AB 3.3 Estmatng poston o ndvdual vehcles n the shock wave There are our ponts on the trajectory through travel tme o the ndvdual vehcle. Pont 1 and Pont 4 s estmated by the value o tme and the poston o upstream and downstream detector. Fgure 5 Estmatng the coordnates o Pont 1 and Pont 4
6 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 Po dtme T3 w down nt1(re, ) (3) Pont 4( t, w up) (4) w down = The dstance between the stop lne and the downstream detector w = The dstance between the stop lne and the upstream detector up T 3 = Downstream detector to detect the subtracton o tme and red tme Pont and Pont3 are Ponts o the vehcle on the shock wave. Pont3 and Pont means the part o the vehcle speed s changng by acceleraton, deceleraton and stop. Pont shows a shock wave that s generated n the rear stop lne. The speed o the shock wave generated behnd the stop lne, trac densty can be expressed n relaton to the slope o the crtcal densty and the jam densty. Po dtme T1 h Fgure 6 Estmatng the coordnates o Pont and Pont 3 nt (Re, ) (5) T 1 = The derence between the tme to move ater the start o green tme h = The dstance between the vehcle and the stop lne h can be represented by the shock waves generated behnd the stop lne and the multplcaton o tme( T 1 ). h Shockwave T (6) 1 Shockwave = The speed o the shock wave generated by the rear due to the elmnaton o the queue
7 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 The sum o h and w down representng the wdth o the downstream ntersecton s the same as the movng dstance o the vehcle durng tme( T ). 1 h wdown tre at v ( T t ) 1 (7) t re = response tme t = Acceleraton tme requred to restore the ree speed The value o T 3 can be expressed as the sum o t1 and T. Thereore, the value o T can be calculated through the ollowng. 1 T1 Shockwave wdown tre at v ( T t ) (8) 1 Shockwave ( T3 T ) wdown tre at v ( T t ) (9) T 1 Shockwave T3 at v t t Shockwave v ( ) re (10) The coordnates o the Pont3, there s acceleraton delay derence between Pont. Pont 3(Re dtme T ( Delay AcceleratonDelay), h ) (11) 3.4 Estmate queue length 1 The speed o the shock wave s estmated usng the coordnate values or the detecton o ndvdual vehcles Pont3. Durng one cycle o the coordnate values are represented by the value o the slope o the speed o the shock wave through the method o least squares. Fnd tan mnmze n t tan d (1) 1 tan 1 Dstance ( t, d ) tan Tme
8 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 Fgure 7 Least-squares method or the estmaton o the speed o the shock wave. Every cycle usng the slope calculated by the least squares method to calculate the length o the queue. Queue Length = tan t (13) max t max = Tme Shockwave 1 and Shockwave meet 4.SIMULATION ANALYSIS 4.1 Descrpton o the analyss In ths study, VISSIM model s adopted to smulate the sgnal ntersecton operaton. VISSIM s a mcroscopc, behavor-based mult-purpose trac smulaton program. A pseudo network was developed as ollowngs: Roadway Characterstcs and sgnal condtons a. 500m dstance at ntersecton b. lanes per drecton c. Cycle : 100s d. Green nterval : 50s Trac/other condtons e. 100% passenger car (No truck). Capacty :,750 vehcles per hour per lane g. Jam dendty : 450 vehcles per Km h. Average ree low speed: 50 km/h 4. Development o scenaro / Assessment tools In ths study, was perormed our scenaros dependng on the saturaton o the access. Scen 1. v/c = 0.35 Scen. v/c = 0.55 Scen 3. v/c = 0.70 Scen 4. v/c = 0.90
9 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 Mean Absolute Percent Error(MAPE) was used n order to evaluate the t o the algorthm. A F A MAPE(%) = 100 (13) n A = Measurements F = Estmates 4.3 Smulaton result Each scenaro was perormed three tmes, accordng to the seed number. Table 1 scenaro result Scen 1 Scen Scen 3 Scen 4 Seed number A (m) F (m) MAPE(%) The MAPE values decrease, ncreasng the saturaton smulaton results. The case o Scenaro 1, the value s derent or each seed. In other words, the low saturaton means that the error o the estmated speed o the shock wave. MAPE value o scenaro 3 s the best scenaro than scenaro 4 saturaton s hgh. Ths s due to long queues n Scenaro 4.In other words, the longer the length o the queue s too great error. The approach to saturaton s better the hgher the tness o the algorthm. Scenaro 1 s the derence between the value o each seed. Thus, the mnmum approach, addtonal research s needed or saturaton. 5. CONCLUSION The purpose o ths study s the development o an algorthm or estmatng the length o the queue through the sectonal travel tme normaton. Analyzed pror research on how to estmate the length o the queue and Algorthm through the nterval travel tme and the shock wave theory was developed. Fnally, evaluate the tness o the algorthm through smulaton. The hgher saturaton smulaton results showed that queue estmaton error s less. I the studes or approprate method o queue theory are contnued, ths study shows how to estmate the queue, the possblty o utlzng the sectonal travel tme normaton and t can be utlzed n advanced sgnal system. REFERENCES Adol D. May, (1990), Trac Flow Fundamentals
10 Proceedngs o the Eastern Asa Socety or Transportaton Studes, Vol.9, 013 Carlos F. DAGANZO (1997), Fundamentals o Transportaton and Trac Operatons Rahm F. Benekohal, Yoassry M. EL-Zohary,(001), Mult-regme Arrval rate unorm delay models or Sgnalzed ntersectons, Transportaton Research Part A. Alexander Skabardons, Nkolaos Gerolmns,(005), Real-TIme Estmaton o Travel tmes on Sgnalzed Arterals, 16th Internatonal Symposum on Transportaton and Trac Theory. Joonho Ko, Mchael Hunyer, and Randall Guensler,(006), Measurng Control Delay Usng Second-by-Second GPS Speed DATA, 86th Annual Meetng Transportaton Research Board. Ma Yng-Yng, Yang Xao-Guang, and Zhong Zhang-Jan,(008), Olne Oset Models or Coordnated Sgnal Control, 11th nternatonal IEEE Conerence on Intellgent Transportaton Systems. Henry X. Lu, Wenteng Ma, Xnka Wu, and Heng Hu,(009), Real-Tme Estmaton o Arteral Travel Tme under Congested Condton, Transportmetrca. James A. Bonneson, Mchael P. Pratt, and Mark A. Vandehey,(010), Predctng Arrval Flow Proles and Platoon Dsperson or Urban Street Segments, Transportaton Research Record, No Xnka Wu, Henry X. Lu, and Douglas Gettman,(010), Identcaton o oversaturated ntersectons usng hgh-resoluton trac sgnal data, Transportaton Research Part C, Volume 18(4). Xuegang(Je) Ban, Peng Hao, and Zhanbo Sun,(010), Real Tme Queue Length Estmaton or Sgnalzed Intersectons Usng Travel Tmes rom Moble Sensors, Transportaton Research Part C, Volume 19(6).
Question No. 1 (12 points)
Fnal Exam (3 rd Year Cvl) ransportaton lannng & rac Engneerng Date: uesday 4 June 20 otal Grade: 00 onts me: 09:30am 2:30pm Instructor: Dr. Mostaa Abo-Hashema, roessor Notes:. Answer all questons and assume
More informationOPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming
OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or
More informationONE-DIMENSIONAL COLLISIONS
Purpose Theory ONE-DIMENSIONAL COLLISIONS a. To very the law o conservaton o lnear momentum n one-dmensonal collsons. b. To study conservaton o energy and lnear momentum n both elastc and nelastc onedmensonal
More informationTraffic Signal Timing: Basic Principles. Development of a Traffic Signal Phasing and Timing Plan. Two Phase and Three Phase Signal Operation
Traffc Sgnal Tmng: Basc Prncples 2 types of sgnals Pre-tmed Traffc actuated Objectves of sgnal tmng Reduce average delay of all vehcles Reduce probablty of accdents by mnmzng possble conflct ponts Objectves
More informationChapter - 2. Distribution System Power Flow Analysis
Chapter - 2 Dstrbuton System Power Flow Analyss CHAPTER - 2 Radal Dstrbuton System Load Flow 2.1 Introducton Load flow s an mportant tool [66] for analyzng electrcal power system network performance. Load
More information= 1.23 m/s 2 [W] Required: t. Solution:!t = = 17 m/s [W]! m/s [W] (two extra digits carried) = 2.1 m/s [W]
Secton 1.3: Acceleraton Tutoral 1 Practce, page 24 1. Gven: 0 m/s; 15.0 m/s [S]; t 12.5 s Requred: Analyss: a av v t v f v t a v av f v t 15.0 m/s [S] 0 m/s 12.5 s 15.0 m/s [S] 12.5 s 1.20 m/s 2 [S] Statement:
More informationPhysics 2A Chapter 3 HW Solutions
Phscs A Chapter 3 HW Solutons Chapter 3 Conceptual Queston: 4, 6, 8, Problems: 5,, 8, 7, 3, 44, 46, 69, 70, 73 Q3.4. Reason: (a) C = A+ B onl A and B are n the same drecton. Sze does not matter. (b) C
More informationChapter 9: Statistical Inference and the Relationship between Two Variables
Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,
More informationWork is the change in energy of a system (neglecting heat transfer). To examine what could
Work Work s the change n energy o a system (neglectng heat transer). To eamne what could cause work, let s look at the dmensons o energy: L ML E M L F L so T T dmensonally energy s equal to a orce tmes
More informationModule 14: THE INTEGRAL Exploring Calculus
Module 14: THE INTEGRAL Explorng Calculus Part I Approxmatons and the Defnte Integral It was known n the 1600s before the calculus was developed that the area of an rregularly shaped regon could be approxmated
More informationQueueing Networks II Network Performance
Queueng Networks II Network Performance Davd Tpper Assocate Professor Graduate Telecommuncatons and Networkng Program Unversty of Pttsburgh Sldes 6 Networks of Queues Many communcaton systems must be modeled
More informationAnalysis of Queuing Delay in Multimedia Gateway Call Routing
Analyss of Queung Delay n Multmeda ateway Call Routng Qwe Huang UTtarcom Inc, 33 Wood Ave. outh Iseln, NJ 08830, U..A Errol Lloyd Computer Informaton cences Department, Unv. of Delaware, Newark, DE 976,
More informationMultiple Sound Source Location in 3D Space with a Synchronized Neural System
Multple Sound Source Locaton n D Space wth a Synchronzed Neural System Yum Takzawa and Atsush Fukasawa Insttute of Statstcal Mathematcs Research Organzaton of Informaton and Systems 0- Mdor-cho, Tachkawa,
More informationParking Demand Forecasting in Airport Ground Transportation System: Case Study in Hongqiao Airport
Internatonal Symposum on Computers & Informatcs (ISCI 25) Parkng Demand Forecastng n Arport Ground Transportaton System: Case Study n Hongqao Arport Ln Chang, a, L Wefeng, b*, Huanh Yan 2, c, Yang Ge,
More informationPop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing
Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,
More informationχ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body
Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown
More informationAP Physics 1 & 2 Summer Assignment
AP Physcs 1 & 2 Summer Assgnment AP Physcs 1 requres an exceptonal profcency n algebra, trgonometry, and geometry. It was desgned by a select group of college professors and hgh school scence teachers
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
More informationCONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION
CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING N. Phanthuna 1,2, F. Cheevasuvt 2 and S. Chtwong 2 1 Department of Electrcal Engneerng, Faculty of Engneerng Rajamangala
More informationChapter 3 Differentiation and Integration
MEE07 Computer Modelng Technques n Engneerng Chapter Derentaton and Integraton Reerence: An Introducton to Numercal Computatons, nd edton, S. yakowtz and F. zdarovsky, Mawell/Macmllan, 990. Derentaton
More informationMathematics Intersection of Lines
a place of mnd F A C U L T Y O F E D U C A T I O N Department of Currculum and Pedagog Mathematcs Intersecton of Lnes Scence and Mathematcs Educaton Research Group Supported b UBC Teachng and Learnng Enhancement
More informationSOLVING CAPACITATED VEHICLE ROUTING PROBLEMS WITH TIME WINDOWS BY GOAL PROGRAMMING APPROACH
Proceedngs of IICMA 2013 Research Topc, pp. xx-xx. SOLVIG CAPACITATED VEHICLE ROUTIG PROBLEMS WITH TIME WIDOWS BY GOAL PROGRAMMIG APPROACH ATMII DHORURI 1, EMIUGROHO RATA SARI 2, AD DWI LESTARI 3 1Department
More informationSMART-Signal Phase II: Arterial Offset Optimization Using Archived High-Resolution Traffic Signal Data
SMART-Sgnal Phase II: Arteral Offset Optmzaton Usng Archved Hgh-Resoluton Traffc Sgnal Data Fnal Report Prepared by: Henry X. Lu Heng Hu Department of Cvl Engneerng Unversty of Mnnesota CTS 13-19 Techncal
More information2016 Wiley. Study Session 2: Ethical and Professional Standards Application
6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton
More informationLinear Momentum. Equation 1
Lnear Momentum OBJECTIVE Obsere collsons between two carts, testng or the conseraton o momentum. Measure energy changes durng derent types o collsons. Classy collsons as elastc, nelastc, or completely
More informationNote 10. Modeling and Simulation of Dynamic Systems
Lecture Notes of ME 475: Introducton to Mechatroncs Note 0 Modelng and Smulaton of Dynamc Systems Department of Mechancal Engneerng, Unversty Of Saskatchewan, 57 Campus Drve, Saskatoon, SK S7N 5A9, Canada
More informationApplication of Queuing Theory to Waiting Time of Out-Patients in Hospitals.
Applcaton of Queung Theory to Watng Tme of Out-Patents n Hosptals. R.A. Adeleke *, O.D. Ogunwale, and O.Y. Hald. Department of Mathematcal Scences, Unversty of Ado-Ekt, Ado-Ekt, Ekt State, Ngera. E-mal:
More informationEn Route Traffic Optimization to Reduce Environmental Impact
En Route Traffc Optmzaton to Reduce Envronmental Impact John-Paul Clarke Assocate Professor of Aerospace Engneerng Drector of the Ar Transportaton Laboratory Georga Insttute of Technology Outlne 1. Introducton
More informationProf. Paolo Colantonio a.a
Pro. Paolo olantono a.a. 3 4 Let s consder a two ports network o Two ports Network o L For passve network (.e. wthout nternal sources or actve devces), a general representaton can be made by a sutable
More information: 5: ) A
Revew 1 004.11.11 Chapter 1: 1. Elements, Varable, and Observatons:. Type o Data: Qualtatve Data and Quanttatve Data (a) Qualtatve data may be nonnumerc or numerc. (b) Quanttatve data are always numerc.
More informationCorrelation and Regression. Correlation 9.1. Correlation. Chapter 9
Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,
More informationFreeway Traffic Shockwave Analysis: Exploring the NGSIM Trajectory Data
PAPER #07-3016 Freeway Traffc Shocwave Analyss: Explorng the NGSIM Trajectory Data by Xao-Yun Lu* PATH, Insttute of Transportaton Studes Unversty of Calforna, Bereley Rchmond Feld Staton, Bldg 452 1357
More informationPhysics 40 HW #4 Chapter 4 Key NEATNESS COUNTS! Solve but do not turn in the following problems from Chapter 4 Knight
Physcs 40 HW #4 Chapter 4 Key NEATNESS COUNTS! Solve but do not turn n the ollowng problems rom Chapter 4 Knght Conceptual Questons: 8, 0, ; 4.8. Anta s approachng ball and movng away rom where ball was
More informationAn Interactive Optimisation Tool for Allocation Problems
An Interactve Optmsaton ool for Allocaton Problems Fredr Bonäs, Joam Westerlund and apo Westerlund Process Desgn Laboratory, Faculty of echnology, Åbo Aadem Unversty, uru 20500, Fnland hs paper presents
More informationThe Study of Teaching-learning-based Optimization Algorithm
Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute
More informationSIMPLE LINEAR REGRESSION
Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two
More informationPHYS 1441 Section 002 Lecture #16
PHYS 1441 Secton 00 Lecture #16 Monday, Mar. 4, 008 Potental Energy Conservatve and Non-conservatve Forces Conservaton o Mechancal Energy Power Today s homework s homework #8, due 9pm, Monday, Mar. 31!!
More informationCOMPLETE BUFFER SHARING IN ATM NETWORKS UNDER BURSTY ARRIVALS
COMPLETE BUFFER SHARING WITH PUSHOUT THRESHOLDS IN ATM NETWORKS UNDER BURSTY ARRIVALS Ozgur Aras and Tugrul Dayar Abstract. Broadband Integrated Servces Dgtal Networks (B{ISDNs) are to support multple
More informationQueuing system theory
Elements of queung system: Queung system theory Every queung system conssts of three elements: An arrval process: s characterzed by the dstrbuton of tme between the arrval of successve customers, the mean
More informationStatistics for Economics & Business
Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable
More informationDesign and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm
Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationOutline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]
DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm
More informationLecture 3 Stat102, Spring 2007
Lecture 3 Stat0, Sprng 007 Chapter 3. 3.: Introducton to regresson analyss Lnear regresson as a descrptve technque The least-squares equatons Chapter 3.3 Samplng dstrbuton of b 0, b. Contnued n net lecture
More informationTrajectory Optimization of Flexible Mobile Manipulators Using Open-Loop Optimal Control method
rajectory Optmzaton o Flexble Moble Manpulators Usng Open-Loop Optmal Control method M.H. Korayem and H. Rahm Nohooj Robotc Lab, Mechancal Department, Iran Unversty o Scence and echnology hkorayem@ust.ac.r,
More informationSpring Force and Power
Lecture 13 Chapter 9 Sprng Force and Power Yeah, energy s better than orces. What s net? Course webste: http://aculty.uml.edu/andry_danylov/teachng/physcsi IN THIS CHAPTER, you wll learn how to solve problems
More informationInternational Journal of Mathematical Archive-3(3), 2012, Page: Available online through ISSN
Internatonal Journal of Mathematcal Archve-3(3), 2012, Page: 1136-1140 Avalable onlne through www.ma.nfo ISSN 2229 5046 ARITHMETIC OPERATIONS OF FOCAL ELEMENTS AND THEIR CORRESPONDING BASIC PROBABILITY
More informationInternational Power, Electronics and Materials Engineering Conference (IPEMEC 2015)
Internatonal Power, Electroncs and Materals Engneerng Conference (IPEMEC 2015) Dynamc Model of Wnd Speed Dstrbuton n Wnd Farm Consderng the Impact of Wnd Drecton and Interference Effects Zhe Dong 1, a,
More informationChapter Newton s Method
Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve
More informationOld Dominion University Physics 420 Spring 2010
Projects Structure o Project Reports: 1 Introducton. Brely summarze the nature o the physcal system. Theory. Descrbe equatons selected or the project. Dscuss relevance and lmtatons o the equatons. 3 Method.
More informationChapter 6. Operational Amplifier. inputs can be defined as the average of the sum of the two signals.
6 Operatonal mpler Chapter 6 Operatonal mpler CC Symbol: nput nput Output EE () Non-nvertng termnal, () nvertng termnal nput mpedance : Few mega (ery hgh), Output mpedance : Less than (ery low) Derental
More informationAnalysis of Discrete Time Queues (Section 4.6)
Analyss of Dscrete Tme Queues (Secton 4.6) Copyrght 2002, Sanjay K. Bose Tme axs dvded nto slots slot slot boundares Arrvals can only occur at slot boundares Servce to a job can only start at a slot boundary
More informationAdiabatic Sorption of Ammonia-Water System and Depicting in p-t-x Diagram
Adabatc Sorpton of Ammona-Water System and Depctng n p-t-x Dagram J. POSPISIL, Z. SKALA Faculty of Mechancal Engneerng Brno Unversty of Technology Techncka 2, Brno 61669 CZECH REPUBLIC Abstract: - Absorpton
More informationSingle Variable Optimization
8/4/07 Course Instructor Dr. Raymond C. Rump Oce: A 337 Phone: (95) 747 6958 E Mal: rcrump@utep.edu Topc 8b Sngle Varable Optmzaton EE 4386/530 Computatonal Methods n EE Outlne Mathematcal Prelmnares Sngle
More informationTHE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD
Journal of Appled Mathematcs and Computatonal Mechancs 7, 6(3), 7- www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.3. e-issn 353-588 THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS
More informationCHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER. In real environmental conditions the speech signal may be
55 CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER 4.1 Introducton In real envronmental condtons the speech sgnal may be supermposed by the envronmental nterference. In general, the spectrum
More informationIrregular vibrations in multi-mass discrete-continuous systems torsionally deformed
(2) 4 48 Irregular vbratons n mult-mass dscrete-contnuous systems torsonally deformed Abstract In the paper rregular vbratons of dscrete-contnuous systems consstng of an arbtrary number rgd bodes connected
More informationSimulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests
Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth
More informationStatistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation
Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear
More informationChapter 5 rd Law of Thermodynamics
Entropy and the nd and 3 rd Chapter 5 rd Law o hermodynamcs homas Engel, hlp Red Objectves Introduce entropy. Derve the condtons or spontanety. Show how S vares wth the macroscopc varables,, and. Chapter
More informationTurbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH
Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant
More informationSTUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS
Blucher Mechancal Engneerng Proceedngs May 0, vol., num. www.proceedngs.blucher.com.br/evento/0wccm STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS Takahko Kurahash,
More informationNON LINEAR ANALYSIS OF STRUCTURES ACCORDING TO NEW EUROPEAN DESIGN CODE
October 1-17, 008, Bejng, Chna NON LINEAR ANALYSIS OF SRUCURES ACCORDING O NEW EUROPEAN DESIGN CODE D. Mestrovc 1, D. Czmar and M. Pende 3 1 Professor, Dept. of Structural Engneerng, Faculty of Cvl Engneerng,
More informationPolynomial Regression Models
LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance
More informationAn Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors
An Algorthm to Solve the Inverse Knematcs Problem of a Robotc Manpulator Based on Rotaton Vectors Mohamad Z. Al-az*, Mazn Z. Othman**, and Baker B. Al-Bahr* *AL-Nahran Unversty, Computer Eng. Dep., Baghdad,
More informationMODELING TRAFFIC FLOW ON OVERSATURATED ARTERIALS A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA
MODELING TRAFFIC FLOW ON OVERSATURATED ARTERIALS A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY XINKAI WU IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
More informationInductance Calculation for Conductors of Arbitrary Shape
CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors
More informationEN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st
EN40: Dynamcs and bratons Homework 4: Work, Energy and Lnear Momentum Due Frday March 1 st School of Engneerng Brown Unversty 1. The fgure (from ths publcaton) shows the energy per unt area requred to
More informationTracking with Kalman Filter
Trackng wth Kalman Flter Scott T. Acton Vrgna Image and Vdeo Analyss (VIVA), Charles L. Brown Department of Electrcal and Computer Engneerng Department of Bomedcal Engneerng Unversty of Vrgna, Charlottesvlle,
More informationMeasurement Uncertainties Reference
Measurement Uncertantes Reference Introducton We all ntutvely now that no epermental measurement can be perfect. It s possble to mae ths dea quanttatve. It can be stated ths way: the result of an ndvdual
More informationDepartment of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6
Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationUniversity of Washington Department of Chemistry Chemistry 452/456 Summer Quarter 2014
Lecture 16 8/4/14 Unversty o Washngton Department o Chemstry Chemstry 452/456 Summer Quarter 214. Real Vapors and Fugacty Henry s Law accounts or the propertes o extremely dlute soluton. s shown n Fgure
More informationJournal of Universal Computer Science, vol. 1, no. 7 (1995), submitted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Springer Pub. Co.
Journal of Unversal Computer Scence, vol. 1, no. 7 (1995), 469-483 submtted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Sprnger Pub. Co. Round-o error propagaton n the soluton of the heat equaton by
More informationMarkov Chain Monte Carlo Lecture 6
where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways
More informationSee Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)
Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes
More informationMed Phys 4R06/6R03 Laboratory Experiment #6 MULTICHANNEL PULSE SPECTROMETRY
Med Phys 4R06/6R0 Laboratory Experment #6 MULICHANNEL PULSE SPECROMERY INRODUCION: In ths experment you wll use the technque o multchannel spectrometry to perorm quanttatve analyss o radoactve partculate
More informationModal Strain Energy Decomposition Method for Damage Detection of an Offshore Structure Using Modal Testing Information
Thrd Chnese-German Jont Symposum on Coastal and Ocean Engneerng Natonal Cheng Kung Unversty, Tanan November 8-16, 2006 Modal Stran Energy Decomposton Method for Damage Detecton of an Offshore Structure
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationStatistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA
14 th Internatonal Users Conference Sesson: ALE-FSI Statstcal Energy Analyss for Hgh Frequency Acoustc Analyss wth Zhe Cu 1, Yun Huang 1, Mhamed Soul 2, Tayeb Zeguar 3 1 Lvermore Software Technology Corporaton
More informationMinimisation of the Average Response Time in a Cluster of Servers
Mnmsaton of the Average Response Tme n a Cluster of Servers Valery Naumov Abstract: In ths paper, we consder task assgnment problem n a cluster of servers. We show that optmal statc task assgnment s tantamount
More informationSecond Order Analysis
Second Order Analyss In the prevous classes we looked at a method that determnes the load correspondng to a state of bfurcaton equlbrum of a perfect frame by egenvalye analyss The system was assumed to
More informationChapter 4. Velocity analysis
1 Chapter 4 Velocty analyss Introducton The objectve of velocty analyss s to determne the sesmc veloctes of layers n the subsurface. Sesmc veloctes are used n many processng and nterpretaton stages such
More information/ n ) are compared. The logic is: if the two
STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence
More informationDesigning of Combined Continuous Lot By Lot Acceptance Sampling Plan
Internatonal Journal o Scentc Research Engneerng & Technology (IJSRET), ISSN 78 02 709 Desgnng o Combned Contnuous Lot By Lot Acceptance Samplng Plan S. Subhalakshm 1 Dr. S. Muthulakshm 2 1 Research Scholar,
More informationLecture 2 Solution of Nonlinear Equations ( Root Finding Problems )
Lecture Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods Numercal Methods Bracketng Methods Open Methods Convergence Notatons Root Fndng
More informationOperating conditions of a mine fan under conditions of variable resistance
Paper No. 11 ISMS 216 Operatng condtons of a mne fan under condtons of varable resstance Zhang Ynghua a, Chen L a, b, Huang Zhan a, *, Gao Yukun a a State Key Laboratory of Hgh-Effcent Mnng and Safety
More informationParameter Estimation for Dynamic System using Unscented Kalman filter
Parameter Estmaton for Dynamc System usng Unscented Kalman flter Jhoon Seung 1,a, Amr Atya F. 2,b, Alexander G.Parlos 3,c, and Klto Chong 1,4,d* 1 Dvson of Electroncs Engneerng, Chonbuk Natonal Unversty,
More informationReal-Time Systems. Multiprocessor scheduling. Multiprocessor scheduling. Multiprocessor scheduling
Real-Tme Systems Multprocessor schedulng Specfcaton Implementaton Verfcaton Multprocessor schedulng -- -- Global schedulng How are tasks assgned to processors? Statc assgnment The processor(s) used for
More informationEstimating the Fundamental Matrix by Transforming Image Points in Projective Space 1
Estmatng the Fundamental Matrx by Transformng Image Ponts n Projectve Space 1 Zhengyou Zhang and Charles Loop Mcrosoft Research, One Mcrosoft Way, Redmond, WA 98052, USA E-mal: fzhang,cloopg@mcrosoft.com
More informationSuppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl
RECURSIVE SPLINE INTERPOLATION METHOD FOR REAL TIME ENGINE CONTROL APPLICATIONS A. Stotsky Volvo Car Corporaton Engne Desgn and Development Dept. 97542, HA1N, SE- 405 31 Gothenburg Sweden. Emal: astotsky@volvocars.com
More informationEndogenous timing in a mixed oligopoly consisting of a single public firm and foreign competitors. Abstract
Endogenous tmng n a mxed olgopoly consstng o a sngle publc rm and oregn compettors Yuanzhu Lu Chna Economcs and Management Academy, Central Unversty o Fnance and Economcs Abstract We nvestgate endogenous
More informationGravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11)
Gravtatonal Acceleraton: A case of constant acceleraton (approx. hr.) (6/7/11) Introducton The gravtatonal force s one of the fundamental forces of nature. Under the nfluence of ths force all objects havng
More informationAS-Level Maths: Statistics 1 for Edexcel
1 of 6 AS-Level Maths: Statstcs 1 for Edecel S1. Calculatng means and standard devatons Ths con ndcates the slde contans actvtes created n Flash. These actvtes are not edtable. For more detaled nstructons,
More informationAssessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion
Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,
More informationLow Complexity Soft-Input Soft-Output Hamming Decoder
Low Complexty Soft-Input Soft-Output Hammng Der Benjamn Müller, Martn Holters, Udo Zölzer Helmut Schmdt Unversty Unversty of the Federal Armed Forces Department of Sgnal Processng and Communcatons Holstenhofweg
More informationNegative Binomial Regression
STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...
More informationAdaptive Dynamical Polling in Wireless Networks
BULGARIA ACADEMY OF SCIECES CYBERETICS AD IFORMATIO TECHOLOGIES Volume 8, o Sofa 28 Adaptve Dynamcal Pollng n Wreless etworks Vladmr Vshnevsky, Olga Semenova Insttute for Informaton Transmsson Problems
More informationUncertainty and auto-correlation in. Measurement
Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at
More informationModule 9. Lecture 6. Duality in Assignment Problems
Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept
More information