COMPARATIVE STUDY OF CAR-FOLLOWING MODELS FOR DESCRIBING BREAKDOWN PHENOMENA AT SAGS

Size: px
Start display at page:

Download "COMPARATIVE STUDY OF CAR-FOLLOWING MODELS FOR DESCRIBING BREAKDOWN PHENOMENA AT SAGS"

Transcription

1 COMPARAIVE SUDY OF CAR-FOLLOWING MODELS FOR DESCRIBING BREAKDOWN PHENOMENA A SAGS akashi Oguchi * and Ryoichi Konuma okyo Meropolian Universiy, JAPAN, - Minamiosawa, Hachiouji, 9-97 JAPAN , oguchi@mu.ac.jp Meropolian Epressway Co., Ld., okyo, JAPAN ABSRAC he paper describes characerisics of capaciy boleneck phenomena a sag secions in JAPAN from a viewpoin of car-following behaviour. hrough observaion and evaluaion of drivers' behaviour and he parameer esimaion and comparaive evaluaion of several car-following models are inroduced. he model of gradual gradien change effecs are inroduced and compared beween driving behaviour a sag secions and ha a a consan gradien secion. he sysem design of ACC Adapive Cruise Conrol o preven breakdown a sag secions could become more realisic hrough he evaluaion using microscopic simulaion wih he beer car-following model evaluaed in his sudy. KEYWORDS boleneck a sags, car-following behaviour, effecs of gradien change, model comparison INRODUCION here are many capaciy bolenecks on Japanese Epressways where raffic queue congesion of long lengh and duraion is occurred very frequenly. Mos major boleneck sies are ordinary secions including verical alignmen sag curve secions and unnel enrances, oherwise oll gaes on he main rack and acciden or inciden sies. his paper deals wih characerisics of capaciy boleneck phenomena a ordinary secions, paricularly a sag secions, and shows analyical measures for proving he mechanism which causes he breakdown phenomena a sag secions from a viewpoin of car-following behaviour. he mechanism of he acivaion of bolenecks a sag secions proposed by Koshi e. al. [] becomes very popular, bu only in Japan. Some of he key facors are he eisence of dense plaoon of vehicles on median lane, he gradual speed decrease being sared from he beginning of a verical sag curve secion, drivers' unawareness of such gradual change of verical gradien, drivers' endency of keeping space clearances in he dense plaoon, and resuling in he oubreak of an upsream amplified propagaion of deceleraion shockwave. hese phenomena consis in each driver's car maneuvering behaviour including he car-following behaviour and he driver's response o such a gradual gradien change. Alhough several conribuions o he developmen of models o simulae such driving behaviours were carried ou, here are sill many problems.

2 Few of he eising car-following models are, firs, proved wih acual driving daa ecep for paricular sudies. here is no comprehensive comparaive sudy among many famous car-following models. he disribuional naure of he parameers of hese eising models are, hird, no eamined enough wih observaions or eperimens of drivers' behaviour. he appropriaeness of parameers is, fourh, almos always evaluaed wih he reproducibiliy correlaion coefficien or RMSE of acceleraion only, bu i should also be done wih ha of velociy and space clearance also. he effecs of gradual gradien change on drivers' behaviour are, las, no eamined wih enough care hough hese are mos imporan facors for eplaining he difference beween he sags which cause breakdown phenomena and hose which do no. By he way, here are many well-known bolenecks of sag secions in Japan bu hese are fied a some sag secions no a ohers, and here is no obvious difference of highway geomery or raffic characerisics beween hese wo groups of sag secions. here are hree objecives of he paper. he firs objecive is observaion and evaluaion of drivers' behaviour wih high accuracy a he ime of breakdown phenomena. he second one is parameer esimaion and comparaive evaluaion of several car-following models in consideraion of he appropriaeness concep. he las one is evaluaion of he gradual gradien change effecs hrough comparison beween driving behaviour a sag secions and ha a a consan gradien secion. NAURE OF CAPACIY BOLENECK PHENOMENA A SAGS Generally, raffic flow characerisics in basic segmens wih sags were already known Koshi e al. []. Before congesion occurs in a boleneck, he flow rae in he median lane is always higher han ha in he shoulder lane, and he flow rae of median lane is abou,8 o, vphpl while he maimum flow rae is from, o,5 vph/-lane. his shows ha he speed reducion causing he breakdown sars from he median lane. Afer his however, he flow raes for boh lanes become almos equal and he capaciy flow rae afer formaion of he queue is reduced o, o,7 vph/-lane. he fac ha a sag secion can be a boleneck does no necessarily mean ha his is because of a consan, seep and long up-grade secion downsream of he sag curve. Xing e al. [] observed successfully, for he firs ime in Japan, he rajecories of more han vehicles showing he amplifying shock wave propagaion o he upsream secion, only by an inernal deceleraion a a sag secion where he gradien changes from -.6 o -.%. he downsream secion is almos level, wih a sligh down-grade slope no upgrade. Unil he consrucion of an addiional lane, his par was a boleneck where breakdowns occurred mos frequenly among many bolenecks of basic secions. I is hough ha he cause of he boleneck phenomenon a sags is no a seep up-grade bu a mild gradien change Koshi e al. []. he mechanism of he boleneck acivaion is simulaed by sligh speed decrease wih he gradien change. he drivers going hrough a sag secion canno fully compensae he gradien change, bu sill hey ry o mainain heir space clearance wihin plaoon in he median lane; his naure for all drivers causes amplifying shock wave propagaion in he plaoon on he median lane. I is hough ha drivers, afer several seconds passed hrough his secion, are accusomed wih he downsream grade condiions, herefore, he effec from he grade or he gradien change around he sag secion is already vanished. Heavy vehicles do no necessarily affec on he acivaion of he

3 boleneck phenomena because of he slighness of gradien change, and he boleneck phenomena a many sags are acivaed only in weekends or holidays wih few heavy vehicles. EXPERIMEN MEHOD AND RESULS Ouline of he Eperimens An eperimenal vehicle, equipped wih several sensors o measure he behaviour of iself and surrounding vehicles Oguchi e al. [], is employed for es run in realiy. Several es runs, going hrough one of he mos popular boleneck named "Yamao sag" in Japan, were planed repeaedly. Figure shows he verical alignmen of he secion. he auhors ried o sar he vehicle seeking he good iming for he vehicle o go hrough he secion wihin a dense plaoon and o be encounered wih deceleraion shockwave. Because he breakdown phenomenon happens suddenly, i is no easy o make he vehicle mee he shockwave. One of he es runs, which relaively eperienced wih a shockwave, are seleced as a former vehicle of he firs virual es run for each virual highway condiion creaed as -D CG for a driving simulaor DS developed in Universiy of okyo. As a join research, he auhors are permied o use he DS. he DS sysem conains he 6-degree-of-freedom shaking insrumens wih urn-able, and he insrumens providing full-direcional visual image. he DS conneced wih KAKUMO, which is a microscopic raffic simulaor S as for each vehicle can move and change lanes considering spacing and/or gaps among surrounding vehicles, can provide he condiion in which subjecs can feel as o be in he virual raffic condiion. KAKUMO enables o reproduce he ime-space rajecory recorded by boh in realiy and in he DS sysem as a virual leading vehicle. Uilizing his funcion, subjecs, driving he DS, can drive in a virual siuaion wih he leading vehicle as a reproducion of cerain behaviour of oher drivers and wih he neighbour lane raffic condiion. Aliude m 9 8 Direcion oubound VLC 7 m "Yamao Sag" Posiion km Figure Verical alignmen of he "Yamao sag".9-.6kp, oubound. algebraic gradien change able Prepared highway geomeric condiions Lengh of Verical Curves 7m m wihou curve.% case case.6% case case in realiy.9% case case 5 no change case 6

4 Seven virual highways are creaed wih differen verical alignmen condiions using -D CAD sofware; combinaion of he same, a half of and one hird of he lengh of verical curve and he same, a half of and one hird of he algebraic gradien change of he "Yamao sag" secion in addiion o a condiion wih no gradien change. able summarizes hese seven cases. Oher geomeric condiions for he seven cases are always same as he "Yamao sag" such as horizonal alignmens, cross-secion specificaions, ec. Procedures and Resuls hiry-hree subjecs were asked o follow he former vehicle. he former vehicle of he firs subjec is he reproducion of he seleced rajecory obained by he eperimenal vehicle. Ecep for firs subjec, he former vehicle is he reproducion of he behaviour of he former subjec in he order of hem, for seven virual highway condiions wih he DS. Seven es runs of car-following behaviour by he hiry-hree subjecs are recorded. Figure shows one of he eamples of ime-space rajecories of hiry-hree subjecs obained by he DS. CAR-FOLLOWING MODELS A CONSAN GRADIEN CONDIION Oguchi [] already colleced many eising ypes of car-following models. he GM model [5], he eponenial epression model [6], OV model [7], polynomial epression models including he models proposed by Koshi [5] and Ozaki [8], [9] and spiral curve model [] are seleced wih small modificaions for comprehensive comparaive sudy here. Seleced Car-following Models he noaions of variables used here are as below; : elapsed ime[s],,, : posiion[m], speed[m/s], acceleraion[m/s ] of he leader, posiion[m], speed[m/s], acceleraion[m/s ] of he follower, θ n : verical grade [rad] a ime of he n-h vehicle's posiion, : reacion delay ime [s],,,,,,, β : coefficien parameers, and l, m, n : raising parameers. posiion [km] Figure Eample of ime-space rajecories of subjecs case

5 5 Model- Linear Monomial Model LM Model: his is he simples model proposed by Chandler e al. [] which consiss of linear differenial equaion. Model- Non-Linear Monomial Model NLM Model: his is he model wih small simplificaion derived from he one developed by Gazis e al. [5] as shown in equaion. Model- Monomial Elemenary Funcion Model GM Model: his is he mos famous model, so called "GM Model", developed by Gazis e al. [5]. his Model includes he ones in equaion and as specific cases. l m { { Model- Eponenial Funcion Model Newell Model: Newell [6] proposed a model which allows space headway o be given as an eponenial funcion of speed. o ake differenial of speed, he model shown equaion is inroduced. e { Model-5 Eponenial Funcion Model ype Ceder Model: Ceder [] proposed a model which allows he reacion srengh of GM Model o be an eponenial funcion of space headway. / e { { 5 Model-6 Linear Polynomial Model KS Model: Komeani and Sasaki [] proposed a model adding he effec of acceleraion of he leading vehicle o he LM Model. 6 Model-7 Hyperbolic angen Funcion Model OV Model: Bando e al. [7] proposed a model which allows he acceleraion is given by he difference beween Opimal Velociy OV and he real speed, and he OV is given by he hyperbolic angen funcion of space headway. heir model includes he consan values of "" and "anh ", hough hese consans would be arbirary. he original model does no conain he physical dimensional adjusmen facors before aking hyperbolic angen funcion and afer doing so. So he auhors add four coefficien parameers o he original one. he original model does no also consider he effec of reacion delay ime, herefore i is included in he model here. ] - [ - anh { 7 Model-8 Linear Polynomial Model ype Helley Model: Helley [] added anoher erm including he effecs of he difference beween he opimal space clearance given by linear funcion of speed and acceleraion and real clearance o he LM Model. - β 8 Model-9 Non-Linear Polynomial Model Spiral Model: Nakayama e al. [] paid aenion o he fac ha he rajecory on he plane made of space headway and relaive speed become spiral. aking approimaion of he leader's speed be consan, he model shown in he equaion 9 is derived; he consan L is se o be a consan as an average space clearance. { { Y L Y 9 where, / L Y Model- Non-Linear Polynomial Model Koshi Model: Koshi [5] proposed a model which consiss of four erms as shown below.

6 f f f sin{ θ where, f l { f g n { f { V f β β β β g In his sudy, he hird and forh erms are omied like equaion. he model is applied only a he consan gradien condiion and he effec of gradien change is separaely deal. he reacion ime delay is pu ogeher ino one and he opimal space headway is se o be consan β. { { β l n { { hough GM, Newell, KS and Koshi Models include LM, NLM and Helley Model as special cases, every model parameers are se o be non-zero o make clear he difference beween each model. Simulaion Analyses a Consan Gradien Condiion he auhors creae a micro raffic simulaor for evaluaing he en ypes of car-following models seleced above. Simulaion analyses are conduced in 6 m secion from. km poin o.8 km poin wih consan gradien condiion in he virual highway alignmen case. he simulaor makes each run hrough he secion wih a cerain se of parameers for a cerain car-following model wih he iniial speed and space clearance condiions of he saring posiion in addiion o he oal rajecory of he leading vehicle. Reproducibiliy wih each model of any rajecories can be judged by roo mean square error RMSE of he simulaed calculaion o he observed daa recorded by DS, in addiion o he correlaion coefficien CC beween hem. hough evaluaion indices may no be only for acceleraion bu also speed and space clearance, Oguchi [6] found ha he space clearance, being he mos inegraed variable, is mos appropriae as an evaluaion inde. he simulaor can also simulaneously check he calculaed vehicle condiions, which are used for judging he local sabiliies. he sabiliy judging hresholds are as follow; avoidance of rear-end collision: >, no ecess of upper/lower limis of realisic acceleraion: 9.8 [m/s ] < <. [m/s ], avoidance of no follow-he-leader driving condiion: < 5 [m], and no sopping or being backed up siuaion: >. A simulaed calculaion should break off a he ime when one of he any condiions above does no mee, wih a cerain se of parameers. I means ha his se of parameers does no conain "Local Sabiliy". Moreover, one more vehicle he second follower is added o follow he simulaed firs follower. he "Asympoic Sabiliy" can be evaluaed parially by 6

7 able Range of he se of parameers for each model LM # of division.~..5~.5 NLM # of division.~..5~5. GM l m # of division.~. 5.~. 5.~..~. Newell # of division.~. 5.~. 5 5 Ceder.~. ~ # of division 5 6 KS.~..~.5 # of division OV.~..~.7 # of division 5 8 Helly.~..~.5 # of division Spiral.~.5 5.~. # of division 5 Koshi # of division.~. 5.~..5~5..~..5~5. 6~5.5.~..~. 5.5~..5~.5 5.~.5.5~..~ l n.~. 5.~.5.~..5~.8 checking he running condiion of he second follower hrough he simulaed calculaion as same way as he firs one. he parameers for each car-following model are idenified wih RMSE value of he simulaed space clearances o he recorded ones of a cerain subjec. he search mehod adoped here is he simple repeiion from fron o back so called "area bombing mehod" for every parameer wih cerain discree valuessee able, and he parameers wih he smalles RMSE value are se o be he idenified model parameers. If he simulaed calculaion for he firs follower successfully finished wih he smalles RMSE value, ha of he second follower sars and ry o find again he anoher smalles RMSE value, which is he fied one wih he finally idenified parameers. Reproducibiliy a Consan Gradien Condiion From he view poin of reproducibiliy, a car-following model should allow smaller RMSE value. On he oher hand, from he view poin of robusness, i should provide RMSE values for more number of subjecs. For eample, even if one model can allow very small RMSE value for some paricular subjecs, and canno provide RMSE value for oher many subjecs because of insufficien naure abou any of he four sabiliy judging hresholds, he evaluaion 7

8 RMSE [m] 5 LM NLM GM Newell Ceder KS OV Helley Spiral Koshi Figure RMSE ascending order Smalles RMSE ranked ascending order for he en car-following models he second-follower simulaion resuls, oal number resul is no well. he desirable model should have small RMSE value for as many subjecs as possible and also should have as small number of subjecs as possible wihou RMSE value. Figure shows he smalles RMSE values, derived from he second-follower simulaed calculaion, ranked in ascending order for each of en car-following models. hose five models, Ceder, OV, Spiral, Newell and Helley Models, can reproduce only half of he subjecs' follow-he-leader behaviour. Koshi Model can reproduce he mos number of subjecs' behaviour, followed by GM Model, and NLM, KS, and LM Models, respecively. he ranking, sars from Koshi Model, means he order of desirable robusness. NLM, KS, and LM Models can be described by Koshi and GM Models as a paricular parameer condiion. here is no significan difference in he performance of reproducibiliy and robusness beween he Koshi Model and he GM Model, bu he GM Model can allow smaller values of RMSE han he Koshi Model can do. herefore, GM Model is found o be he bes model o reproduce he behaviour of hiry-hree subjecs wih DS in he consan gradien condiion; followed by Koshi Model, and he oher eigh models are significanly inferior o hese wo models. MODELS OF GRADIEN CHANGE EFFECS Proposed Models Because of he performance in reproducibiliy and robusness of he compared en car-following models, he effecs of he gradien change are invesigaed only combined wih GM model. In his sudy, he resuls of virual es runs on DS for si differen gradien change condiions a he 'Yamao Sag' secion shown in able are used for comparison among models of gradien change effecs proposed. he basic idea of he model of gradien change effecs is shown as below; GM Model γ g{sinθ sinθ u, where g : graviy acceleraion [m/s ], 8

9 θ : grade [radian] a elapsed ime a he posiion, θ u : grade [radian] in upsream secion of he sag secion, and γ : gradien change effec parameer. If a driver can compleely compensae he graviy change in a sag secion and he downsream consan grade secion, his or her car-following behaviour does no affec from he gradien change a all and he gradien change effec parameer should be se o zero γ. On he oher hand, if he driver never be able o noice and reac o he gradien change and canno compensae a all, he componen of graviy change oally affecs on he car-following behaviour, i.e. he gradien change effec parameer should be se o uniy γ. In general he driver's behaviour would be in he middle condiion beween hese wo γ. Because he drivers may gradually sar o noice he gradien change in a sag secion, he gradien change effec parameer migh change from zero o uniy wih he gradien condiion and elapsed ime. hrough he discussion above, five differen models are proposed as below. No Effec NE Model: I is a model wihou effec from he gradien change. γ oally Affeced A Model: Drivers never compensae he effec of gradien change. γ Consan Effec CE Model: he effec from gradien change is consan hrough oal ravel from upsream secion of a sag boleneck o downsream secion. γ consan < γ < Linear Funcion LF Model: he gradien change effec parameer is se o be a linear funcion of elapsed ime from he ime a driver sars o noice he gradien change a W o he ime he or her sars o compensae compleely. γ γ < a W W a γ a W γ < a W 5 W γ a W γ where a and W are he model parameers. he change of γ is illusraed in Figure. Non-Linear Funcion NL Model: he gradien change effec parameer is se o be a non-linear funcion of elapsed ime wih he model parameers of a and κ. he change of γ for NL Model is also illusraed in Figure. anh{ κ γ a 6 a W γ NE Model A Model CE Model LF Model NL Model a W a Figure Gradien change effec parameers of he five models a W elapsed ime 9

10 Simulaion Analyses wih Gradien Change Condiions Daa of he hree subjecs who go virual carsick and canno carry ou heir assigned ask for driving on seven differen highway alignmen condiions are eliminaed. Daa of oher hree subjecs for whom GM Model canno reproduce heir behaviour RMSE value canno be esimaed are also eliminaed. herefore, rajecories derived by weny-seven subjecs are uilized o evaluae he gradien change effecs. he evaluaion process should be done only under he condiion of he almos same parameers of GM Model can be applied for all of he si differen gradien change condiions. herefore, before analysing he gradien change effecs, one se of he parameers of GM Model is idenified checked wih he reproducibiliy and robusness for each subjecs on each of si alignmen change condiions, ecluding no gradien change case case 6, in he same secion of.~.9 km poin wih lile gradien change. As a resul, eleven subjecs' behaviour canno be reproduced by GM Model because any of he sabiliy judging condiions ~5 is no filled in some highway alignmen condiions RMSE value canno be esimaed. For oher welve subjecs, relaively differen ses of parameers of GM Model are appropriae o differen alignmen condiions. he ses of parameers for he oher only four subjecs are good for eplaining oal of si differen alignmen condiions. I is found ha he drivers, who drive differen si highways mainaining same driving behaviour wih he same se of parameers of GM Model, are very rare. Reproducibiliy in Gradien Change Condiions Among daa of weny-seven subjecs running on si alignmen condiions, he reproducibiliy and robusness of five gradien change effec models are eamined uilizing only he daa in he condiion boh wih fied ses of parameers of GM Model esimaed shown in Figure and wih he smalles RMSE value being smaller han meers. As a resul, niney runs are used in his evaluaion. hen he parameers of he five gradien change effec models are idenified wih a RMSE value of he simulaed space clearances o he recorded ones of a cerain subjec and wih a cerain alignmen condiion hrough he secion beween. km poin o. km poin. he search mehod adoped here is also "area bombing mehod", and RMSE [m] 5 NE Model A Model CE Model LF Model NL Model 6 8 RMSE accending order Figure 7 Smalles RMSE ranked ascending order for he five gradien effec models he second-follower simulaion resuls, oal number9

11 he parameers wih he smalles RMSE value are idenified. If he simulaed calculaion for he firs follower successfully finished wih he smalles RMSE value, ha of he second follower sars and he anoher smalles RMSE value, which is he fied one wih he finally idenified parameers, is ried o be found. Figure 7 shows he smalles RMSE values, derived from he second-follower simulaed calculaion, ranked in ascending order for each of he five gradien effec models combined wih GM Model. LF and NL Models, which have changing naure of he gradien effec parameer, can reproduce more number of behaviours han oher hree NE, A, and CE Models wih fied consans of he parameer. he figure shows he fac ha LF and NL Models have no only beer reproducibiliy bu also beer robusness. CONCLUDING REMARKS he idenificaion of he parameers of en car-following models, such as LM, NLM, GM, Newell, Ceder, KS, OV, Helley, Spiral and Koshi Models, are ried in he raffic condiion of boleneck acivaion around a cerain sag secion. GM Model is he bes model from he view poins boh of reproducibiliy and robusness, and Koshi Model has he bes performance on robusness only. he parameer idenificaion mehod used here is o find he smalles RMSE of simulaed space clearances o recorded daa of hiry-hree subjecs in a consan grade secion on DS. Reproducibiliy is evaluaed by he smallness of RMSE for each subjec; on he oher hand robusness is evaluaed by he sabiliy naure for all of he subjecs. he idenificaion of he parameers of five gradien change effec models, such as NE, A, CE, LF and NL Models, are ried wih combinaion of GM Model a he sag secion. LF and NL Models are he bes model from he view poins of boh reproducibiliy and robusness. Boh LF and NL Models have changing naure of he gradien change effec parameers, herefore, he gradien change effec is gradually vanishing as drivers pass hrough he sag secion. hese characerisics are inerpreed as he drivers' psychological behaviour. here are sill many issues lef o fuure sudy. he DS used in he sudy should be verified for reproducibiliy and feelings of drivers compared o hose of hem in realiy, paricularly in he driving condiion on an epressway wih many surrounding vehicles and wih gradien change. GM Model nor Koshi Model is no compleely enough for reproducibiliy and robusness for describing car-following behaviour observed by DS, because he smalles RMSE value is no sufficien enough. In addiion, he simulaed calculaion of acceleraion and speed is no fi o recorded one. he difference of driving behaviour beween each driver will be epeced o be described by he difference of parameers idenified for a proper model of car-following behaviour affeced by gradien change. From he view poins of reproducibiliy and robusness or sabiliy beween drivers he comprehensive and simulaneous mehod o idenify he model parameers should be buil up. Even if here are many issues lef o fuure sudy, he resuls can sugges he developmen of he beer car-following model o describe he breakdown phenomena a sag secions, and he sysem design of ACC Adapive Cruise Conrol o preven breakdown a sag secions could become more realisic hrough he evaluaion using microscopic simulaion wih he model.

12 ACKNOWLEDGEMEN his sudy was fully suppored by "Susainable IS Projec" in he Universiy of okyo. he auhors would like o remark special acknowledgmens o Professor M. Kuwahara and Professor Y. Suda for giving his join research opporuniies o hem. hey also would like o epress heir graiude o he Projec Members, especially o Mr. K. Honda, Mr. M. Onuki and Mr.. Shiraishi for heir useful and generous advice and suppor. REFERENCES [] Koshi, M, Kuwahara, M and Akahane, H, "Capaciy of sags and unnels on Japanese Moorways", IE Journal, 65, 99, pp.7-. [] Xing, J and Koshi, M, "A Sudy on he Boleneck Phenomena and Car-following Behaviour on Sags on Moorways", J. Infrasrucure Planning and Managemen JSCE, 5, 995, pp in Japanese. [] Oguchi,, Akahane, H, Nishikawa, H, and Kuwahara, M "Developmen of an Eperimenal Vehicle for Evaluaing Highway raffic Composed of Auomoives wih and wihou Adapive Cruise Conrol Sysems", Proc. h FISIA,, on CD-ROM. [] Oguchi,, "Needs for Developing New Car-following Model for Evaluaing Cruise-assis Highway Sysems", Proc. 7h World Congress on IS,, CD-ROM. [5] Gazis, D, Herman, R, and B. Pos, "Car-Following heory of Seady-Sae raffic Flow", Oper. Res., 7, 959, pp [6] Newell, G, "Nonlinear Effecs in he Dynamic of Car-following", Oper. Res., 9, 96, pp [7] Bando, M, Hasebe, K, Nakayama, A, Shibaa, A, and Sugiyama, Y, "Dynamical Model of raffic Congesion and Numerical Simulaion", J. of Phys. Rev. E, 5, 995, pp.5-. [8] Ozaki, H, "Reacion and Anicipaion in he Car-following Behaviour", Proc. h In'l Symp. on ransporaion and raffic heory, 99, pp [9] Ozaki, H, "Assisance of Drivers o Miigae Highway Capaciy Problem", Proc. nd World Congress on IS, 995, pp [] Nakayama, H, Wada, M, and Ichikawa, K, "Invesigaion of raffic Simulaion Model Using Spiral Curve", Proc. of raffic Eng. JSE,, 99, pp.5-8 in Japanese. [] Chandler, R, Herman, R, and Monroll, E, "raffic Dynamics: Sudies in Car Following", Oper. Res., 6, 958, pp [] Ceder, A, "Deerminisic raffic Flow Model for he wo-regime Approach", ranspn. Res. Recrd., 567, 976, pp.6-. [] Komeani, E, and Sasaki,, "On he Sabiliy of raffic Flow Repor ", J. Oper. Res. Soc. Japan,, 958, pp.-6. [] Helley, W, "Simulaion of Bolenecks in Single-lane raffic Flow", heory of raffic Flow, 959, pp.7-8. [5] Koshi, M, "Capaciy of Moorway Bolenecks", J. Infrasrucure Planning and Managemen JSCE, 7, 986, pp.-7 in Japanese. [6] Oguchi,, "Observaion and Analysis Measure of Vehicle Moion for Evaluaion of raffic Capaciy", ebook of he One-day Seminor on Infrasrucure Planning JSCE,,, pp.5-56 in Japanese.

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

A car following model for traffic flow simulation

A car following model for traffic flow simulation Inernaional Journal of Applied Mahemaical Sciences ISSN 0973-076 Volume 9, Number (206), pp. -9 Research India Publicaions hp://www.ripublicaion.com A car following model for raffic flow simulaion Doudou

More information

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still.

Lecture 2-1 Kinematics in One Dimension Displacement, Velocity and Acceleration Everything in the world is moving. Nothing stays still. Lecure - Kinemaics in One Dimension Displacemen, Velociy and Acceleraion Everyhing in he world is moving. Nohing says sill. Moion occurs a all scales of he universe, saring from he moion of elecrons in

More information

STATE-SPACE MODELLING. A mass balance across the tank gives:

STATE-SPACE MODELLING. A mass balance across the tank gives: B. Lennox and N.F. Thornhill, 9, Sae Space Modelling, IChemE Process Managemen and Conrol Subjec Group Newsleer STE-SPACE MODELLING Inroducion: Over he pas decade or so here has been an ever increasing

More information

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n

Module 2 F c i k c s la l w a s o s f dif di fusi s o i n Module Fick s laws of diffusion Fick s laws of diffusion and hin film soluion Adolf Fick (1855) proposed: d J α d d d J (mole/m s) flu (m /s) diffusion coefficien and (mole/m 3 ) concenraion of ions, aoms

More information

WEEK-3 Recitation PHYS 131. of the projectile s velocity remains constant throughout the motion, since the acceleration a x

WEEK-3 Recitation PHYS 131. of the projectile s velocity remains constant throughout the motion, since the acceleration a x WEEK-3 Reciaion PHYS 131 Ch. 3: FOC 1, 3, 4, 6, 14. Problems 9, 37, 41 & 71 and Ch. 4: FOC 1, 3, 5, 8. Problems 3, 5 & 16. Feb 8, 018 Ch. 3: FOC 1, 3, 4, 6, 14. 1. (a) The horizonal componen of he projecile

More information

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan Ground Rules PC11 Fundamenals of Physics I Lecures 3 and 4 Moion in One Dimension A/Prof Tay Seng Chuan 1 Swich off your handphone and pager Swich off your lapop compuer and keep i No alking while lecure

More information

1. VELOCITY AND ACCELERATION

1. VELOCITY AND ACCELERATION 1. VELOCITY AND ACCELERATION 1.1 Kinemaics Equaions s = u + 1 a and s = v 1 a s = 1 (u + v) v = u + as 1. Displacemen-Time Graph Gradien = speed 1.3 Velociy-Time Graph Gradien = acceleraion Area under

More information

In this chapter the model of free motion under gravity is extended to objects projected at an angle. When you have completed it, you should

In this chapter the model of free motion under gravity is extended to objects projected at an angle. When you have completed it, you should Cambridge Universiy Press 978--36-60033-7 Cambridge Inernaional AS and A Level Mahemaics: Mechanics Coursebook Excerp More Informaion Chaper The moion of projeciles In his chaper he model of free moion

More information

Morning Time: 1 hour 30 minutes Additional materials (enclosed):

Morning Time: 1 hour 30 minutes Additional materials (enclosed): ADVANCED GCE 78/0 MATHEMATICS (MEI) Differenial Equaions THURSDAY JANUARY 008 Morning Time: hour 30 minues Addiional maerials (enclosed): None Addiional maerials (required): Answer Bookle (8 pages) Graph

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

Week 1 Lecture 2 Problems 2, 5. What if something oscillates with no obvious spring? What is ω? (problem set problem)

Week 1 Lecture 2 Problems 2, 5. What if something oscillates with no obvious spring? What is ω? (problem set problem) Week 1 Lecure Problems, 5 Wha if somehing oscillaes wih no obvious spring? Wha is ω? (problem se problem) Sar wih Try and ge o SHM form E. Full beer can in lake, oscillaing F = m & = ge rearrange: F =

More information

Module 4: Time Response of discrete time systems Lecture Note 2

Module 4: Time Response of discrete time systems Lecture Note 2 Module 4: Time Response of discree ime sysems Lecure Noe 2 1 Prooype second order sysem The sudy of a second order sysem is imporan because many higher order sysem can be approimaed by a second order model

More information

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010

Simulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Simulaion-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Week Descripion Reading Maerial 2 Compuer Simulaion of Dynamic Models Finie Difference, coninuous saes, discree ime Simple Mehods Euler Trapezoid

More information

Numerical Dispersion

Numerical Dispersion eview of Linear Numerical Sabiliy Numerical Dispersion n he previous lecure, we considered he linear numerical sabiliy of boh advecion and diffusion erms when approimaed wih several spaial and emporal

More information

RC, RL and RLC circuits

RC, RL and RLC circuits Name Dae Time o Complee h m Parner Course/ Secion / Grade RC, RL and RLC circuis Inroducion In his experimen we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors.

More information

Physics for Scientists and Engineers I

Physics for Scientists and Engineers I Physics for Scieniss and Engineers I PHY 48, Secion 4 Dr. Beariz Roldán Cuenya Universiy of Cenral Florida, Physics Deparmen, Orlando, FL Chaper - Inroducion I. General II. Inernaional Sysem of Unis III.

More information

MEI STRUCTURED MATHEMATICS 4758

MEI STRUCTURED MATHEMATICS 4758 OXFORD CAMBRIDGE AND RSA EXAMINATIONS Advanced Subsidiary General Cerificae of Educaion Advanced General Cerificae of Educaion MEI STRUCTURED MATHEMATICS 4758 Differenial Equaions Thursday 5 JUNE 006 Afernoon

More information

Chapter 15: Phenomena. Chapter 15 Chemical Kinetics. Reaction Rates. Reaction Rates R P. Reaction Rates. Rate Laws

Chapter 15: Phenomena. Chapter 15 Chemical Kinetics. Reaction Rates. Reaction Rates R P. Reaction Rates. Rate Laws Chaper 5: Phenomena Phenomena: The reacion (aq) + B(aq) C(aq) was sudied a wo differen emperaures (98 K and 35 K). For each emperaure he reacion was sared by puing differen concenraions of he 3 species

More information

Some Basic Information about M-S-D Systems

Some Basic Information about M-S-D Systems Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,

More information

KINEMATICS IN ONE DIMENSION

KINEMATICS IN ONE DIMENSION KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec

More information

Development of a new metrological model for measuring of the water surface evaporation Tovmach L. Tovmach Yr. Abstract Introduction

Development of a new metrological model for measuring of the water surface evaporation Tovmach L. Tovmach Yr. Abstract Introduction Developmen of a new merological model for measuring of he waer surface evaporaion Tovmach L. Tovmach Yr. Sae Hydrological Insiue 23 Second Line, 199053 S. Peersburg, Russian Federaion Telephone (812) 323

More information

04. Kinetics of a second order reaction

04. Kinetics of a second order reaction 4. Kineics of a second order reacion Imporan conceps Reacion rae, reacion exen, reacion rae equaion, order of a reacion, firs-order reacions, second-order reacions, differenial and inegraed rae laws, Arrhenius

More information

AP Calculus BC Chapter 10 Part 1 AP Exam Problems

AP Calculus BC Chapter 10 Part 1 AP Exam Problems AP Calculus BC Chaper Par AP Eam Problems All problems are NO CALCULATOR unless oherwise indicaed Parameric Curves and Derivaives In he y plane, he graph of he parameric equaions = 5 + and y= for, is a

More information

!!"#"$%&#'()!"#&'(*%)+,&',-)./0)1-*23)

!!#$%&#'()!#&'(*%)+,&',-)./0)1-*23) "#"$%&#'()"#&'(*%)+,&',-)./)1-*) #$%&'()*+,&',-.%,/)*+,-&1*#$)()5*6$+$%*,7&*-'-&1*(,-&*6&,7.$%$+*&%'(*8$&',-,%'-&1*(,-&*6&,79*(&,%: ;..,*&1$&$.$%&'()*1$$.,'&',-9*(&,%)?%*,('&5

More information

Lab #2: Kinematics in 1-Dimension

Lab #2: Kinematics in 1-Dimension Reading Assignmen: Chaper 2, Secions 2-1 hrough 2-8 Lab #2: Kinemaics in 1-Dimension Inroducion: The sudy of moion is broken ino wo main areas of sudy kinemaics and dynamics. Kinemaics is he descripion

More information

Introduction to Numerical Analysis. In this lesson you will be taken through a pair of techniques that will be used to solve the equations of.

Introduction to Numerical Analysis. In this lesson you will be taken through a pair of techniques that will be used to solve the equations of. Inroducion o Nuerical Analysis oion In his lesson you will be aen hrough a pair of echniques ha will be used o solve he equaions of and v dx d a F d for siuaions in which F is well nown, and he iniial

More information

1. Kinematics I: Position and Velocity

1. Kinematics I: Position and Velocity 1. Kinemaics I: Posiion and Velociy Inroducion The purpose of his eperimen is o undersand and describe moion. We describe he moion of an objec by specifying is posiion, velociy, and acceleraion. In his

More information

15. Vector Valued Functions

15. Vector Valued Functions 1. Vecor Valued Funcions Up o his poin, we have presened vecors wih consan componens, for example, 1, and,,4. However, we can allow he componens of a vecor o be funcions of a common variable. For example,

More information

SPH3U: Projectiles. Recorder: Manager: Speaker:

SPH3U: Projectiles. Recorder: Manager: Speaker: SPH3U: Projeciles Now i s ime o use our new skills o analyze he moion of a golf ball ha was ossed hrough he air. Le s find ou wha is special abou he moion of a projecile. Recorder: Manager: Speaker: 0

More information

LAB 6: SIMPLE HARMONIC MOTION

LAB 6: SIMPLE HARMONIC MOTION 1 Name Dae Day/Time of Lab Parner(s) Lab TA Objecives LAB 6: SIMPLE HARMONIC MOTION To undersand oscillaion in relaion o equilibrium of conservaive forces To manipulae he independen variables of oscillaion:

More information

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon 3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of

More information

V AK (t) I T (t) I TRM. V AK( full area) (t) t t 1 Axial turn-on. Switching losses for Phase Control and Bi- Directionally Controlled Thyristors

V AK (t) I T (t) I TRM. V AK( full area) (t) t t 1 Axial turn-on. Switching losses for Phase Control and Bi- Directionally Controlled Thyristors Applicaion Noe Swiching losses for Phase Conrol and Bi- Direcionally Conrolled Thyrisors V AK () I T () Causing W on I TRM V AK( full area) () 1 Axial urn-on Plasma spread 2 Swiching losses for Phase Conrol

More information

PHYSICS 220 Lecture 02 Motion, Forces, and Newton s Laws Textbook Sections

PHYSICS 220 Lecture 02 Motion, Forces, and Newton s Laws Textbook Sections PHYSICS 220 Lecure 02 Moion, Forces, and Newon s Laws Texbook Secions 2.2-2.4 Lecure 2 Purdue Universiy, Physics 220 1 Overview Las Lecure Unis Scienific Noaion Significan Figures Moion Displacemen: Δx

More information

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese

More information

1 Differential Equation Investigations using Customizable

1 Differential Equation Investigations using Customizable Differenial Equaion Invesigaions using Cusomizable Mahles Rober Decker The Universiy of Harford Absrac. The auhor has developed some plaform independen, freely available, ineracive programs (mahles) for

More information

Sections 2.2 & 2.3 Limit of a Function and Limit Laws

Sections 2.2 & 2.3 Limit of a Function and Limit Laws Mah 80 www.imeodare.com Secions. &. Limi of a Funcion and Limi Laws In secion. we saw how is arise when we wan o find he angen o a curve or he velociy of an objec. Now we urn our aenion o is in general

More information

10. State Space Methods

10. State Space Methods . Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he

More information

Lab 10: RC, RL, and RLC Circuits

Lab 10: RC, RL, and RLC Circuits Lab 10: RC, RL, and RLC Circuis In his experimen, we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors. We will sudy he way volages and currens change in

More information

Dynamic Analysis of Damped Driven Pendulum using Laplace Transform Method

Dynamic Analysis of Damped Driven Pendulum using Laplace Transform Method , ISSN 0974-570X (Online), ISSN 0974-578 (Prin), Vol. 6; Issue No. 3; Year 05, Copyrigh 05 by CESER PUBLICATIONS Dynamic Analysis of Damped Driven Pendulum using Laplace Transform Mehod M.C. Agarana and

More information

Traveling Waves. Chapter Introduction

Traveling Waves. Chapter Introduction Chaper 4 Traveling Waves 4.1 Inroducion To dae, we have considered oscillaions, i.e., periodic, ofen harmonic, variaions of a physical characerisic of a sysem. The sysem a one ime is indisinguishable from

More information

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check

More information

Lecture 4 Kinetics of a particle Part 3: Impulse and Momentum

Lecture 4 Kinetics of a particle Part 3: Impulse and Momentum MEE Engineering Mechanics II Lecure 4 Lecure 4 Kineics of a paricle Par 3: Impulse and Momenum Linear impulse and momenum Saring from he equaion of moion for a paricle of mass m which is subjeced o an

More information

2. Nonlinear Conservation Law Equations

2. Nonlinear Conservation Law Equations . Nonlinear Conservaion Law Equaions One of he clear lessons learned over recen years in sudying nonlinear parial differenial equaions is ha i is generally no wise o ry o aack a general class of nonlinear

More information

Robust estimation based on the first- and third-moment restrictions of the power transformation model

Robust estimation based on the first- and third-moment restrictions of the power transformation model h Inernaional Congress on Modelling and Simulaion, Adelaide, Ausralia, 6 December 3 www.mssanz.org.au/modsim3 Robus esimaion based on he firs- and hird-momen resricions of he power ransformaion Nawaa,

More information

CENTRALIZED VERSUS DECENTRALIZED PRODUCTION PLANNING IN SUPPLY CHAINS

CENTRALIZED VERSUS DECENTRALIZED PRODUCTION PLANNING IN SUPPLY CHAINS CENRALIZED VERSUS DECENRALIZED PRODUCION PLANNING IN SUPPLY CHAINS Georges SAHARIDIS* a, Yves DALLERY* a, Fikri KARAESMEN* b * a Ecole Cenrale Paris Deparmen of Indusial Engineering (LGI), +3343388, saharidis,dallery@lgi.ecp.fr

More information

Navneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi

Navneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi Creep in Viscoelasic Subsances Numerical mehods o calculae he coefficiens of he Prony equaion using creep es daa and Herediary Inegrals Mehod Navnee Saini, Mayank Goyal, Vishal Bansal (23); Term Projec

More information

MATHEMATICAL DESCRIPTION OF THEORETICAL METHODS OF RESERVE ECONOMY OF CONSIGNMENT STORES

MATHEMATICAL DESCRIPTION OF THEORETICAL METHODS OF RESERVE ECONOMY OF CONSIGNMENT STORES MAHEMAICAL DESCIPION OF HEOEICAL MEHODS OF ESEVE ECONOMY OF CONSIGNMEN SOES Péer elek, József Cselényi, György Demeer Universiy of Miskolc, Deparmen of Maerials Handling and Logisics Absrac: Opimizaion

More information

Two Coupled Oscillators / Normal Modes

Two Coupled Oscillators / Normal Modes Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own

More information

Robotics I. April 11, The kinematics of a 3R spatial robot is specified by the Denavit-Hartenberg parameters in Tab. 1.

Robotics I. April 11, The kinematics of a 3R spatial robot is specified by the Denavit-Hartenberg parameters in Tab. 1. Roboics I April 11, 017 Exercise 1 he kinemaics of a 3R spaial robo is specified by he Denavi-Harenberg parameers in ab 1 i α i d i a i θ i 1 π/ L 1 0 1 0 0 L 3 0 0 L 3 3 able 1: able of DH parameers of

More information

Single and Double Pendulum Models

Single and Double Pendulum Models Single and Double Pendulum Models Mah 596 Projec Summary Spring 2016 Jarod Har 1 Overview Differen ypes of pendulums are used o model many phenomena in various disciplines. In paricular, single and double

More information

23.2. Representing Periodic Functions by Fourier Series. Introduction. Prerequisites. Learning Outcomes

23.2. Representing Periodic Functions by Fourier Series. Introduction. Prerequisites. Learning Outcomes Represening Periodic Funcions by Fourier Series 3. Inroducion In his Secion we show how a periodic funcion can be expressed as a series of sines and cosines. We begin by obaining some sandard inegrals

More information

Sub Module 2.6. Measurement of transient temperature

Sub Module 2.6. Measurement of transient temperature Mechanical Measuremens Prof. S.P.Venkaeshan Sub Module 2.6 Measuremen of ransien emperaure Many processes of engineering relevance involve variaions wih respec o ime. The sysem properies like emperaure,

More information

Kinematics Vocabulary. Kinematics and One Dimensional Motion. Position. Coordinate System in One Dimension. Kinema means movement 8.

Kinematics Vocabulary. Kinematics and One Dimensional Motion. Position. Coordinate System in One Dimension. Kinema means movement 8. Kinemaics Vocabulary Kinemaics and One Dimensional Moion 8.1 WD1 Kinema means movemen Mahemaical descripion of moion Posiion Time Inerval Displacemen Velociy; absolue value: speed Acceleraion Averages

More information

Physics 180A Fall 2008 Test points. Provide the best answer to the following questions and problems. Watch your sig figs.

Physics 180A Fall 2008 Test points. Provide the best answer to the following questions and problems. Watch your sig figs. Physics 180A Fall 2008 Tes 1-120 poins Name Provide he bes answer o he following quesions and problems. Wach your sig figs. 1) The number of meaningful digis in a number is called he number of. When numbers

More information

Unit 1 Test Review Physics Basics, Movement, and Vectors Chapters 1-3

Unit 1 Test Review Physics Basics, Movement, and Vectors Chapters 1-3 A.P. Physics B Uni 1 Tes Reiew Physics Basics, Moemen, and Vecors Chapers 1-3 * In sudying for your es, make sure o sudy his reiew shee along wih your quizzes and homework assignmens. Muliple Choice Reiew:

More information

Displacement ( x) x x x

Displacement ( x) x x x Kinemaics Kinemaics is he branch of mechanics ha describes he moion of objecs wihou necessarily discussing wha causes he moion. 1-Dimensional Kinemaics (or 1- Dimensional moion) refers o moion in a sraigh

More information

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3 and d = c b - b c c d = c b - b c c This process is coninued unil he nh row has been compleed. The complee array of coefficiens is riangular. Noe ha in developing he array an enire row may be divided or

More information

DEPARTMENT OF STATISTICS

DEPARTMENT OF STATISTICS A Tes for Mulivariae ARCH Effecs R. Sco Hacker and Abdulnasser Haemi-J 004: DEPARTMENT OF STATISTICS S-0 07 LUND SWEDEN A Tes for Mulivariae ARCH Effecs R. Sco Hacker Jönköping Inernaional Business School

More information

- If one knows that a magnetic field has a symmetry, one may calculate the magnitude of B by use of Ampere s law: The integral of scalar product

- If one knows that a magnetic field has a symmetry, one may calculate the magnitude of B by use of Ampere s law: The integral of scalar product 11.1 APPCATON OF AMPEE S AW N SYMMETC MAGNETC FEDS - f one knows ha a magneic field has a symmery, one may calculae he magniude of by use of Ampere s law: The inegral of scalar produc Closed _ pah * d

More information

Position, Velocity, and Acceleration

Position, Velocity, and Acceleration rev 06/2017 Posiion, Velociy, and Acceleraion Equipmen Qy Equipmen Par Number 1 Dynamic Track ME-9493 1 Car ME-9454 1 Fan Accessory ME-9491 1 Moion Sensor II CI-6742A 1 Track Barrier Purpose The purpose

More information

Chapter 12: Velocity, acceleration, and forces

Chapter 12: Velocity, acceleration, and forces To Feel a Force Chaper Spring, Chaper : A. Saes of moion For moion on or near he surface of he earh, i is naural o measure moion wih respec o objecs fixed o he earh. The 4 hr. roaion of he earh has a measurable

More information

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities:

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities: Mah 4 Eam Review Problems Problem. Calculae he 3rd Taylor polynomial for arcsin a =. Soluion. Le f() = arcsin. For his problem, we use he formula f() + f () + f ()! + f () 3! for he 3rd Taylor polynomial

More information

Let us start with a two dimensional case. We consider a vector ( x,

Let us start with a two dimensional case. We consider a vector ( x, Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our

More information

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size. Mehodology. Uni Roo Tess A ime series is inegraed when i has a mean revering propery and a finie variance. I is only emporarily ou of equilibrium and is called saionary in I(0). However a ime series ha

More information

not to be republished NCERT MATHEMATICAL MODELLING Appendix 2 A.2.1 Introduction A.2.2 Why Mathematical Modelling?

not to be republished NCERT MATHEMATICAL MODELLING Appendix 2 A.2.1 Introduction A.2.2 Why Mathematical Modelling? 256 MATHEMATICS A.2.1 Inroducion In class XI, we have learn abou mahemaical modelling as an aemp o sudy some par (or form) of some real-life problems in mahemaical erms, i.e., he conversion of a physical

More information

Inventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions

Inventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions Muli-Period Sochasic Models: Opimali of (s, S) Polic for -Convex Objecive Funcions Consider a seing similar o he N-sage newsvendor problem excep ha now here is a fixed re-ordering cos (> 0) for each (re-)order.

More information

Theory of! Partial Differential Equations!

Theory of! Partial Differential Equations! hp://www.nd.edu/~gryggva/cfd-course/! Ouline! Theory o! Parial Dierenial Equaions! Gréar Tryggvason! Spring 011! Basic Properies o PDE!! Quasi-linear Firs Order Equaions! - Characerisics! - Linear and

More information

Theory of! Partial Differential Equations-I!

Theory of! Partial Differential Equations-I! hp://users.wpi.edu/~grear/me61.hml! Ouline! Theory o! Parial Dierenial Equaions-I! Gréar Tryggvason! Spring 010! Basic Properies o PDE!! Quasi-linear Firs Order Equaions! - Characerisics! - Linear and

More information

Reading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4.

Reading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4. PHY1 Elecriciy Topic 7 (Lecures 1 & 11) Elecric Circuis n his opic, we will cover: 1) Elecromoive Force (EMF) ) Series and parallel resisor combinaions 3) Kirchhoff s rules for circuis 4) Time dependence

More information

= ( ) ) or a system of differential equations with continuous parametrization (T = R

= ( ) ) or a system of differential equations with continuous parametrization (T = R XIII. DIFFERENCE AND DIFFERENTIAL EQUATIONS Ofen funcions, or a sysem of funcion, are paramerized in erms of some variable, usually denoed as and inerpreed as ime. The variable is wrien as a funcion of

More information

Electrical and current self-induction

Electrical and current self-induction Elecrical and curren self-inducion F. F. Mende hp://fmnauka.narod.ru/works.hml mende_fedor@mail.ru Absrac The aricle considers he self-inducance of reacive elemens. Elecrical self-inducion To he laws of

More information

The Paradox of Twins Described in a Three-dimensional Space-time Frame

The Paradox of Twins Described in a Three-dimensional Space-time Frame The Paradox of Twins Described in a Three-dimensional Space-ime Frame Tower Chen E_mail: chen@uguam.uog.edu Division of Mahemaical Sciences Universiy of Guam, USA Zeon Chen E_mail: zeon_chen@yahoo.com

More information

Appendix to Creating Work Breaks From Available Idleness

Appendix to Creating Work Breaks From Available Idleness Appendix o Creaing Work Breaks From Available Idleness Xu Sun and Ward Whi Deparmen of Indusrial Engineering and Operaions Research, Columbia Universiy, New York, NY, 127; {xs2235,ww24}@columbia.edu Sepember

More information

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes Half-Range Series 2.5 Inroducion In his Secion we address he following problem: Can we find a Fourier series expansion of a funcion defined over a finie inerval? Of course we recognise ha such a funcion

More information

Interpretation of special relativity as applied to earth-centered locally inertial

Interpretation of special relativity as applied to earth-centered locally inertial Inerpreaion of special relaiviy as applied o earh-cenered locally inerial coordinae sysems in lobal osiioning Sysem saellie experimens Masanori Sao Honda Elecronics Co., Ld., Oyamazuka, Oiwa-cho, Toyohashi,

More information

Optima and Equilibria for Traffic Flow on a Network

Optima and Equilibria for Traffic Flow on a Network Opima and Equilibria for Traffic Flow on a Nework Albero Bressan Deparmen of Mahemaics, Penn Sae Universiy bressan@mah.psu.edu Albero Bressan (Penn Sae) Opima and equilibria for raffic flow 1 / 1 A Traffic

More information

Stability and Bifurcation in a Neural Network Model with Two Delays

Stability and Bifurcation in a Neural Network Model with Two Delays Inernaional Mahemaical Forum, Vol. 6, 11, no. 35, 175-1731 Sabiliy and Bifurcaion in a Neural Nework Model wih Two Delays GuangPing Hu and XiaoLing Li School of Mahemaics and Physics, Nanjing Universiy

More information

LAPLACE TRANSFORM AND TRANSFER FUNCTION

LAPLACE TRANSFORM AND TRANSFER FUNCTION CHBE320 LECTURE V LAPLACE TRANSFORM AND TRANSFER FUNCTION Professor Dae Ryook Yang Spring 2018 Dep. of Chemical and Biological Engineering 5-1 Road Map of he Lecure V Laplace Transform and Transfer funcions

More information

Exponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits

Exponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits DOI: 0.545/mjis.07.5009 Exponenial Weighed Moving Average (EWMA) Char Under The Assumpion of Moderaeness And Is 3 Conrol Limis KALPESH S TAILOR Assisan Professor, Deparmen of Saisics, M. K. Bhavnagar Universiy,

More information

The field of mathematics has made tremendous impact on the study of

The field of mathematics has made tremendous impact on the study of A Populaion Firing Rae Model of Reverberaory Aciviy in Neuronal Neworks Zofia Koscielniak Carnegie Mellon Universiy Menor: Dr. G. Bard Ermenrou Universiy of Pisburgh Inroducion: The field of mahemaics

More information

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8) I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression

More information

Solution: b All the terms must have the dimension of acceleration. We see that, indeed, each term has the units of acceleration

Solution: b All the terms must have the dimension of acceleration. We see that, indeed, each term has the units of acceleration PHYS 54 Tes Pracice Soluions Spring 8 Q: [4] Knowing ha in he ne epression a is acceleraion, v is speed, is posiion and is ime, from a dimensional v poin of view, he equaion a is a) incorrec b) correc

More information

( ) a system of differential equations with continuous parametrization ( T = R + These look like, respectively:

( ) a system of differential equations with continuous parametrization ( T = R + These look like, respectively: XIII. DIFFERENCE AND DIFFERENTIAL EQUATIONS Ofen funcions, or a sysem of funcion, are paramerized in erms of some variable, usually denoed as and inerpreed as ime. The variable is wrien as a funcion of

More information

3, so θ = arccos

3, so θ = arccos Mahemaics 210 Professor Alan H Sein Monday, Ocober 1, 2007 SOLUTIONS This problem se is worh 50 poins 1 Find he angle beween he vecors (2, 7, 3) and (5, 2, 4) Soluion: Le θ be he angle (2, 7, 3) (5, 2,

More information

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé Bias in Condiional and Uncondiional Fixed Effecs Logi Esimaion: a Correcion * Tom Coupé Economics Educaion and Research Consorium, Naional Universiy of Kyiv Mohyla Academy Address: Vul Voloska 10, 04070

More information

Brock University Physics 1P21/1P91 Fall 2013 Dr. D Agostino. Solutions for Tutorial 3: Chapter 2, Motion in One Dimension

Brock University Physics 1P21/1P91 Fall 2013 Dr. D Agostino. Solutions for Tutorial 3: Chapter 2, Motion in One Dimension Brock Uniersiy Physics 1P21/1P91 Fall 2013 Dr. D Agosino Soluions for Tuorial 3: Chaper 2, Moion in One Dimension The goals of his uorial are: undersand posiion-ime graphs, elociy-ime graphs, and heir

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method Journal of Applied Mahemaics & Bioinformaics, vol., no., 01, 1-14 ISSN: 179-660 (prin), 179-699 (online) Scienpress Ld, 01 Improved Approimae Soluions for Nonlinear Evoluions Equaions in Mahemaical Physics

More information

Reasonable compensation coefficient of maximum gradient in long railway tunnels

Reasonable compensation coefficient of maximum gradient in long railway tunnels Journal of Modern Transporaion Volume 9 Number March 0 Page -8 Journal homepage: jm.swju.edu.cn DOI: 0.007/BF0335735 Reasonable compensaion coefficien of maximum gradien in long railway unnels Sirong YI

More information

LAB # 2 - Equilibrium (static)

LAB # 2 - Equilibrium (static) AB # - Equilibrium (saic) Inroducion Isaac Newon's conribuion o physics was o recognize ha despie he seeming compleiy of he Unierse, he moion of is pars is guided by surprisingly simple aws. Newon's inspiraion

More information

18 Biological models with discrete time

18 Biological models with discrete time 8 Biological models wih discree ime The mos imporan applicaions, however, may be pedagogical. The elegan body of mahemaical heory peraining o linear sysems (Fourier analysis, orhogonal funcions, and so

More information

Fishing limits and the Logistic Equation. 1

Fishing limits and the Logistic Equation. 1 Fishing limis and he Logisic Equaion. 1 1. The Logisic Equaion. The logisic equaion is an equaion governing populaion growh for populaions in an environmen wih a limied amoun of resources (for insance,

More information

and v y . The changes occur, respectively, because of the acceleration components a x and a y

and v y . The changes occur, respectively, because of the acceleration components a x and a y Week 3 Reciaion: Chaper3 : Problems: 1, 16, 9, 37, 41, 71. 1. A spacecraf is raveling wih a veloci of v0 = 5480 m/s along he + direcion. Two engines are urned on for a ime of 84 s. One engine gives he

More information

Solutions from Chapter 9.1 and 9.2

Solutions from Chapter 9.1 and 9.2 Soluions from Chaper 9 and 92 Secion 9 Problem # This basically boils down o an exercise in he chain rule from calculus We are looking for soluions of he form: u( x) = f( k x c) where k x R 3 and k is

More information

k 1 k 2 x (1) x 2 = k 1 x 1 = k 2 k 1 +k 2 x (2) x k series x (3) k 2 x 2 = k 1 k 2 = k 1+k 2 = 1 k k 2 k series

k 1 k 2 x (1) x 2 = k 1 x 1 = k 2 k 1 +k 2 x (2) x k series x (3) k 2 x 2 = k 1 k 2 = k 1+k 2 = 1 k k 2 k series Final Review A Puzzle... Consider wo massless springs wih spring consans k 1 and k and he same equilibrium lengh. 1. If hese springs ac on a mass m in parallel, hey would be equivalen o a single spring

More information

Chapter 2. First Order Scalar Equations

Chapter 2. First Order Scalar Equations Chaper. Firs Order Scalar Equaions We sar our sudy of differenial equaions in he same way he pioneers in his field did. We show paricular echniques o solve paricular ypes of firs order differenial equaions.

More information

Matlab and Python programming: how to get started

Matlab and Python programming: how to get started Malab and Pyhon programming: how o ge sared Equipping readers he skills o wrie programs o explore complex sysems and discover ineresing paerns from big daa is one of he main goals of his book. In his chaper,

More information

Cumulative Damage Evaluation based on Energy Balance Equation

Cumulative Damage Evaluation based on Energy Balance Equation Cumulaive Damage Evaluaion based on Energy Balance Equaion K. Minagawa Saiama Insiue of Technology, Saiama S. Fujia Tokyo Denki Universiy, Tokyo! SUMMARY: This paper describes an evaluaion mehod for cumulaive

More information