A NEW HYBRID APPROACH FOR PREDICTION OF MOVING VEHICLE LOCATION USING PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORK

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1 A NEW HYBRID APPROACH FOR PREDICTION OF MOVING VEHICLE LOCATION USING PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORK 1 BABY ANITHA, E. AND 2 DR.K.DURAISWAMY 1 Assst.Prof.,Department of CSE, K.S.R College of Engneerng 2 Dean(academc), Department of CSE, K.S. Rangasamy College of Tecnology Trucengode, Namakkal Dstrct, Tamlnadu, Inda. E-mal: 1 e_babyanta@yaoo.com, 2 drkduraswamy@yaoo.co.n. ABSTRACT Te modelng of movng objects can attract te lots of researc nterests. Te movng objects ave been developed as a specfc researc area of Geograpc Informaton Systems (GIS). Te Vecle movement locaton predcton based on ter spatal and temporal mnng s an mportant task n many applcatons. Several types of tecnque ave been used for performng te vecle movement predcton process. In suc a works, tere s a lack of analyss n predctng te vecles locaton n current as well as n future. Accordngly we present an algortm prevously for fndng optmal pat n movng vecle usng Genetc Algortm (GA). In te prevous tecnque tere s no complete assurance tat a genetc algortm wll fnd an optmum pat. Ts metod also stll now needs mprovement for optmal pat selecton due to ftness functon restrcted to predcton of comple pat. To avod ts problem, n ts paper a new movng vecle locaton predcton algortm s proposed. Te proposed algortm manly comprses two tecnques namely, Partcle Swarm optmzaton Algortm (PSO) and Feed Forward Back Propagaton Neural Network (FFBNN). In ts proposed tecnque, te vecles frequent pats are collected by watcng all te vecles movement n a specfc tme perod. Among te frequent pats, te vecles optmal pats are calculated by te PSO algortm. Te selected optmal pats for eac vecle are used to tran te FFBNN. Te well traned FFBNN s ten utlzed to fnd te vecle movement from te current locaton. By combnng te PSO and FFBNN, te vecles locaton s predcted more effcently. Te mplementaton result sows te strengt of te proposed algortm n predctng te vecle s future locaton from te current locaton. Te performance of te new algortm s evaluated by comparng te result wt te GA and FFBNN. Te comparson result demonstrates te proposed tecnque acqures more accurate vecle locaton predcton rato tan te GA wt FFBNN predcton rato, n terms of accuracy. Keywords: Movng Vecle Locaton Predcton, Partcle Swarm Optmzaton Algortm Feed Forward Back Propagaton Neural Network, Frequent Pats, Genetc Algortm, Optmal pat. 1. INTRODUCTION Data mnng and knowledge dscovery became more popular n te researc feld. Data mnng s defned as dscovery of nterestng, mplct knowledge, and prevously unknown data from large dataset. Spatal data mnng s one of te requrements of data mnng process spatal data. Spatal data mnng s te process of etracton of mplct nformaton, economcal, socal and scentfc problems, spatal relatons, or oter patterns not eplctly stored n spatal databases [5]. Tere s an ncreasng nterest n spatal data mnng n many areas suc as transportaton, ecology, epdemology, traffc and fleet management, envronmental robotcs, computer vsualzaton, bology and moble computng etc. In spatal databases can store te data n dfferent relatons suc as ctes, rvers, roads wc need to be spatally togeter n order to fnd orgnal and useful patterns n data mnng [6]. In spatal mnng movng objects ave been collected n vast amounts due to te growt of moble devces, wc capture te locaton of objects over tme [8]. Wle researc on movng objects s nvolved n a varety of dfferent applcaton areas, models and metods for movement predcton are often adapted to te 791

2 specfc type of movng objects [7]. Te modelng of movng objects can attract te lots of researc nterests. Te movng objects ave been developed as a specfc researc area of Geograpc Informaton Systems (GIS). Several types of models and algortms ave been proposed to old te contnuously cangng postons of movng objects. Tere are manly two dfferent perspectves wen dealng wt movng objects databases.frst one s te locaton management perspectve and te second one s te spato-temporal data perspectve. A movng object can be defned as an object tat canges ts geograpcal poston. Suc object ntegrates spatal and temporal caracterstcs [9]. In sensor and moble computng tecnology, enormous amounts of data about movng objects are beng collected for trackng of moble objects, weter t s a small cell pone or a gant ocean lner s accessble wt embedded GPS devces and oter sensors. Suc enormous amount of data on movng objects poses great callenges one for analyss of suc data and eploraton of ts applcatons [10]. Trajectory data are ubqutous n te real world. Currently growt n RFID, vdeo, satellte, sensor, and wreless tecnologes as made t possble to scentfcally track object movements and collect uge amounts of trajectory data, e.g., anmal movement data, vessel postonng data, and urrcane trackng data[11]. In Swarm Intellgence based algortms are attractng more researcers. Tese algortms more robustness and fleblty for tose problems under dynamc atmospere. Partcle swarm optmzaton (PSO) s one of te popular swarm ntellgence based metods [22]. In ts paper, we propose a movng vecle locaton predcton algortm wc s to predct te vecle s future locaton from te current locaton. By combnng te proposed algortm wt partcle swarm optmzaton (PSO) and Feed Forward Back propagaton Neural Network (FFBNN), te vecle s locaton s predcted effectvely. Te PSO based algortm s to adaptvely dentfy te optmal pat n a dynamc envronment. Te optmal pats are ten used to tran te FFBNN. Te well traned FFBNN are used to predct te vecle s future locaton from te current locaton. Te remanng of ts paper s structured as follows. Secton two brefly revews te related work n predcton of movng object. Secton tree dscusses te proposed algortm, wc s detaled n ts subsecton. Epermental results are sown n secton four; Te Secton fve concludes te paper. 2. RELATED WORKS In te lterature works deals about te movng objects or movng vecles locaton predcton tecnques. Ivana Nzetc et al. [12] ave dscussed a conceptual model for predctng movng object s future locatons can be very useful n many applcaton areas and data model of movng objects consderng varous object s caracterstcs. Te locaton of te vecle equpped wt a GPS devce can be receved almost contnually. Te proposed approac as a great ssue to predct movng object s net poston. Jorge Huere Pena et al. [13] as proposed tecnque to store data about movng objects. For te analyss of movement data an overvew of te estng data mnng tecnques and some future gudelnes are also presented. Te proposed approac also summarzed te man developments n systems or prototypes lke Hermes, SECONDO, Move Mne and Swarm. Jdong Cen et al. [15] frst proposed model for te road network and movng objects n a grap of cellular automata, wc makes full use of te constrants of te network and te stocastc beavor of te traffc. Te combned model to predct te future trajectores of movng objects. Gyozo Gdofalv et al. [16] ave developed a novel approac for contnuously streamng movng object trajectores for traffc predcton and management. Te proposed metods, data structures, and a prototype mplementaton n a DSMS for managng, mnng, and predctng te ncrementally evolvng trajectores of movng objects n road networks. Te approac can be measured on a large real-world data set of movng object trajectores, startng from a fleet of tas and frequent routes can be effcently dscovered and used for predcton. T Hong Nan Vu et al. [19] ave talked about novel tecnque to dscover frequent spatotemporal patterns from a movng object database. Te effcency of te approac was aceved by two algortms: (1) AllMOP and (2) MaMOP, te algortm was to fnd all frequent patterns and mamal patterns, respectvely. In addton tat to support te servce provder n sendng nformaton to a user n a pus-drven manner, te proposed tecnque as a rule-based locaton predcton. Snce t predct te future locaton of te user. Te proposed algortm as to predct te locaton of a movng object based on movement rules. Many applcatons track te trend of moble objects, 792

3 utlzng tecnologes suc as Global Postonng System (GPS), Global System for Moble Communcatons (GSM) etc.,. Ajaya Kumar Akasapu et al. [17] ave proposed a model for analyzng te trajectores of movng vecles and develop te algortm for mnng te frequency patterns of Trajectory data.te trajectory data are normally obtaned from locatonaware devces tat capture te poston of an object at a specfc tme nterval. Yng-yuan Xao1et al. [20] ntroduced a locaton predcton model of movng objects wt uncertan movement patterns based on grey teory. Te presented locaton predcton model s appled to predct te future locaton of uncertan movng objects. Te proposed model Comparng wt lnear predcton model, tese locaton predcton models not only relaes te lmtaton to moton pattern of movng objects and te requrement for accuracy of samplng data, but also mprove accuracy clearly. Roayant Hassan et al. [18] as proposed pat optmzaton algortm to determne te effcent optmal route tours n Vrtual Envronment. Tese optmal route tours manly based on tree crtera. 1. Sortest dstance, 2. Crowd avodance and 3. Small scale searc area. Prm s algortm s appled n order to generate te optmal pat. Te epermental result sows tat algortm capable to mnmze travel cost altoug mamze te vstng place n vrtual envronments. Antony J.T. Lee et al. [21] as drawn a grapbased mnng (GBM) algortm for mnng te frequent trajectory patterns n a spatal temporal database. Te algortm conssts of two pases. Te frst pase, transform all trajectores n te database nto a mappng grap. And second pase, a TI-lst for eac verte to record all trajectores tat pass troug te verte usng te nformaton recorded n TI-lsts created, te proposed algortm usng dept-frst searc manner to mne all frequent Step Descrpton Vecle vstng pats n dfferent tme Perods are collected. Frequent pats for eac vecle are computed After tat, te frequent pats are gven to te PSO optmzaton process to coose optmal pats for eac vecle Te Optmal pat s traned by FFBNN. Fnally vecle s future locaton s 5. predcted. trajectory patterns. Te epermental results llustrate tat te GBM algortm outperforms te Apror-based and Pref-Span-based algortms by more tan one order of magntude. Wereas te GBM algortm can mne frequent trajectory patterns effcently, tere are stll some ssues to be addressed n future researc. Yanfang Deng [4] proposed PSO algortm wt prorty based encodng sceme based on a flud neural network (FNN) to searc sortest pat n traffc network. Ts algortm overcomes te wegt coeffcent symmetry restrcton of FNN. In many applcatons, te movng vecles locaton predcton plays an mportant task. A lot of metods were developed to fnd te vecle locaton. In our prevous work we proposed eurstc movng vecle locaton predcton algortm. Predcton of te vecle s future locaton by fndng a vecle optmal pat s determned by one of te optmzaton tecnques called genetc algortm (GA). Te selected optmal pats for eac vecle are used to tran te FFBNN. Te traned FFBNN s ten used to determne te vecle s future locaton from te current locaton [23] [24]. Tere s no complete assurance tat a genetc algortm wll fnd an optmum pat. Ts metod also stll now needs mprovement for optmal pat selecton due to te ftness functon restrcted to predcton of comple pat. Te above mentoned n te lterary works are resolvable, after tat te movng vecle s locaton predcton performance s mproved wt ger accuracy. 3. MOVING VEHICLE S LOCATION PREDICTION Te proposed Movng Vecle s Locaton predcton tecnque conssts of four pases, frequent pat selecton, optmal pat predcton usng PSO, optmal pat tranng n FFBNN, and vecle s future locaton predcton by FFBNN. Ts proposed metod predcts te vecle s locaton by fndng te vecles frequent pats and allocatng wegts to eac of te nodes (junctons). Eac pat s frequent values and node s wegt values are utlzed to fnd te vecle s optmal pats va te optmzaton algortm for PSO. Te optmal pats from te PSO are ten utlzed n te FFBNN tranng and testng process. Te steps of proposed tecnque s as followed, 793

4 3.1 Frequent pat selecton usng a grap In ts metod, to construct an undrected grap G and Locatons are consdered as nodes (or) junctons. Edges connectng te nodes denote as a pat p. Te number of vecles n te desgned grap s represented as v= (v 1, v 2, v 3 v c ), were c s value for number of vecles and te pats are represented as p= (p 1, p 2, p 3..p z ). Te wegt wn s assgned to te nodes wt random values [1, w]. Te vecle vsted pat s collected n varous tme perod T= (t 1, t 2.t p ).Te undrected grap s sown n Fgure. 1 Fgure 1: Constructon of Grap Node n= (node1, node2, node3, node4, node5) Vecles V= (v 1, v 2, v 3, v 4, v 5 ) Number of avalable Pats P=7 Te vecle v 1 ntally traverse at te node 1 n a tme perod t 1 and t s net to vst te node 2 by usng te pat 1->2 and ten t vst te nodes 3 and node 4 va te pats 2->3 and 3->4 respectvely. In same te tme perod t 1 te oter vecles traversng pats are also to be calculated. Te same process s repeated for all vecles n dfferent tme perods. In Table 1 lst out te collected pats from te above mentoned grap n tree tme perods. Table 1: Frequent Pat n Tree Tme Perods frequency value, te vecle v c frequently vsted pats p s computed and an nde value s allocated for eac computed frequent pat. Te number of frequent pats for te vecle v c s denoted as f p vc = (p 1, p 2, p j) and te correspondng nde value for te frequent pat s represented as I v c = ( 1, 2.. ) were s a value for number of frequent pats selecton n te vecle v c. 3.2 Partcle Swarm Optmzaton (PSO) PSO s a stocastc optmzaton tecnque nspred by socal beavor of brd flockng or fs scoolng. It was developed by Jm Kennedy (socal psycologst), Bureau of Labor Statstcs and Russ Eberart (electrcal engneer), Purdue Unversty. A concept for optmzng nonlnear functons usng partcle swarm metodology [1]. PSO as been used by many applcatons of several problems. Suc as mage retreval, uman movement bo mecansm optmzaton, cancer classfcaton, gene clusterng, object trackng etc., PSO s a populaton based searc approac tat determnes te optmal soluton utlzng a set of flyng partcle wt veloctes tat are dynamcally adjusted accordng to performance as well as negbors n te searc space [2]. Eac sngle soluton s a brd called as a partcle n te searc space. Te entre partcles ave ftness values wc are evaluated usng ftness functon and ave veloctes wc drect te flyng of te partcles. In all teraton, every partcle s updated by two best values. Frst, pbest wc s te best soluton aceved so far by tat partcle. Second, gbest wc s te best value obtaned so far by any partcle n te populaton. PSO can accelerate eac partcle toward ts prevous best poston, and global best poston locatons, wt a random wegted acceleraton at eac tme [3]. 3.3 Optmal Pat Predcton Usng PSO In Table 1, te vecles are usng dfferent pats to reac at te same target node n dfferent tme perods. Lkewse n dfferent tme perods, te vecles v c vsted pats are collected and te frequency value of pat vsted more tan once by te partcular vecle v c s calculated. Based on te In Partcle swarm optmzaton to establs te optmal pat for all vecles from te number of frequent pats fp. Te selected optmal pat for eac vecle s utlzed to dentfy te vecle future locaton. Te PSO metod ntalzed wt populaton of N ndvduals and partcle are generated randomly tat s represented as X = ( 1, 2, 3... n ), and eac partcle poston s denoted as nde. Te best prevous poston of partcle represented as P = (p 1, p 2. P n ), Te partcle velocty s represented as V= (v 1, v 2.. v n ).Now [-v ma, v ma ] s te range of velocty and [- ma, 794

5 ma ] s te range of poston. Partcle updates ter velocty and postons based on two best values. Frst one s te Personal best, wc s te locaton of ts gest ftness value. Second s te Global best wc s te locaton of overall best value (ftness) and t can be obtaned by any partcle Ftness Functon At ts stage, te randomly generated partcles are evaluated by te ftness functon. Te Ftness functon s appled to eac partcle to fnd te best value. Te correspondng pat s frequency and node s wegt values of partcles nde values are utlzed n te ftness functon calculaton. Te ftness functon s f = v ( p f ) + ( w * n 1 w n ) (1) (2) Were, F vc s te ftness functon of te t partcle generated n te vecle v c, and f s te ndvdual ftness value of te nde. In equaton (2), v (p f ) s te frequency value of te frequent pat p f, wc s presented n te nde and w & F vc = 1 X f = 0 n 1 w n are te wegt value of te nodes, and te pat p f s presented between tese two nodes. Te ndvdual partcle best ftness value s calculated by equaton (2) and set of partcle ftness functon s also calculated usng equaton (1). Ten, te ftness value s calculated for all te partcles and overall best value (ftness) s also calculated for te partcles tat ave te gest ftness value are selected. In all teraton, partcle updates ter postons and velocty accordng to equatons. Among te best value partcle, some partcle contans same nde values. Ts duplcaton of nde values creates dffculty n te furter process. So, te repeated nde values are taken at one tme and te remanng repeated values are elmnated. V n = w*v n +c 1 * r 1 * (p bn - n ) + c 2 * r 2 * (p gn - kn ) (3) n = n + v n (4) Were, V n s te current speed (velocty) of partcle n te n dmensons, n s te current poston of t partcle s te nerta wegt n te range of [0-1], c 1 and c 2 are te accelerate parameters and currently set to 2.0 respectvely, r 1 and r 2 s te random number between [0, 1], p bn and p gn are te correspondng personal best and global best. In equ (4) can update te locaton of new partcle. Te nerta wegt w s to control te force of te partcle by consderng te contrbuton of te prevous velocty. It s bascally controllng ow muc memory of te prevous flgt drecton wll nfluence te new velocty. A smlar cange s made from te PSO. If w > 1, ten te velocty wll reduce te tme, te partcle wll ncrease speed to mamum velocty and te swarm wll be dvergent. If w < 1, ten te velocty of partcle wll decrease untl t reaces zero. (5) Were, wma and wmn s te ntal and fnal wegt, terma s te mamum teraton number, ter s te recent teraton number. In equaton (5), dversfcaton attrbutes s gradually decreased and a certan velocty and moves te current poston close to pbest and gbest can be calculated [14] PSO algortm Step 1: Intalze poston and velocty of all partcles (vecle) randomly generated n te N Dmensonal space. Step 2: For eac partcle (vecle) evaluates te ftness functon and update te global optmum poston. Step 3: Compare partcles ftness value wt te pbest. If current value s better tan pbest ten Rest pbest value equal to te current value and pbest locaton equal to current locaton n N-dmensonal. Step 4: Compare ftness evaluaton wt partcles overall pbest. If current value s better tan gbest, ten reset gbest to current partcles array nde and value. Step 5: Update partcle velocty and poston usng equaton (3), (4) respectvely. Step 6: Repeat 2 to 5 untl step crteron s met; mamum number of teratons s computed. 3.4 To Tran te Optmal Pat Usng FFBNN For above process, we ave number of optmal pat nde values for eac vecle and tese optmal 795

6 pats are used to obtan te vecle future locaton by FFBNN. To aceve te locaton predcton process, te optmal pat nde values are selected by te vecle s best (ftness) value and gven to te FFBNN. Te Feed Forward Back Propagaton Neural Network (FFBNN) s red to perform te tranng and testng process. On tranng stage, eac nde value of vecle s best partcle value and te correspondng pat s nodes are gven as nput to te FFBNN. For eample, select one nde value from te best partcle vecle v c. Now, we ave taken te FFBNN nput as nde correspondng pat node values n 1and n 2. In FFBNN, tere s H d number of dden layers and one output layer, wc s an ndvdual ftness value of te nde as f. Te FFBNN s well traned by ts optmal pat and provded an accurate ftness value for te nput node values. Te proposed vecle future locaton predcton FFBNN structure s sown n Fgure. 2. Fgure 2: Constructon of Optmal Pats Tranng In FFBNN In Fgure 2 contans two nput unts, one output N unt, and d dden unts. At frst, te nput data are transmtted to te dden layer and ten, to te output layer. Ts progresson s called te forward pass of te back propagaton algortm. Every node n te dden layer gets nput from te nput layer and ten multpleed wt approprate wegts and summed. Te dden layer nput value calculaton functon s sad to be bas functon. H d Y = β ) 1 ( w n + w n = (6) In Equaton (6), n1 and n 2 are te nput node values of te nde. Te output of te dden node s te nonlnear transformaton of te resultng sum. Te same process s followed n te output layer. Te followng equaton (7) denotes te actvaton functon of te output layer. Te output values from te output layer are compared wt target values and te learnng error rate for te neural network s computed, wc s gven n equaton (8). α γ = = 1+ H (7) (8) In equaton (8), γ s te learnng error rate of te FFBNN, D s te desred output, and f s te actual output. Te error between te nodes s transmtted back to te dden layer and ts process s called te backward pass of te back propagaton algortm. Te reducton of error by back propagaton algortm s descrbed n te subsequent steps. Step 1: At frst, te wegts are allocated to te dden layer. Te nput layer can old a constant wegt, wereas te wegts for output layer are selected at random. After tat te bas functon and output layer actvaton functon are calculated by usng te equaton. (6) and (7). Step 2: Net, te back propagaton error s computed for eac node and ten wegts are updated by usng te equaton (9). w w Were, te wegt gven as, 1 y p v c e 1 d d w = H 1 = 0 = w + φ. Y (9) s canged, wc s (10) Were, φ s te learnng rate tat usally ranges (ε ) from 0.2 to 0.5, and E s te Back propagaton error. Te bas functon, actvaton functon, and back propagaton error calculaton process are contnued tll te back propagaton error gets D w ( ε ). E f 796

7 ( ε ) reduced.e. E < If te back propagaton error reaces a mnmum value, ten te FFBNN s well traned by te pat node values for performng te vecle locaton predcton. Te well traned FFBNN provde a proper ftness value for te respectve nput pat values. 3.5 Vecle Future Locaton Predcton n FFBNN Te well traned FFBNN s used to predct te vecle s future locaton. Let us assume tat te vecle v c ntal startng node n s gven by te user. After, all te possble pats p va nodes vsted by te vecle v c s gven to te well traned FFBNN and we got te ftness values for all te nput pats p. Among te pats, te vecle vc vstng net pat s predcted by ter ftness value output from te FFBNN, wc are gven as, predcted. Fve vecles frequent movng pats are composed at a certan tme perod, wc s lsted n te below table 2. Table 2: Sample Frequent Pats of Fve Vecles Vecles A B C D E Frequent Pats Te Table 2 sows te vecles movng pats wt dfferent startng nodes. Tese pats are not te most frequent pat of te vecles. Terefore we ave to fnd most frequent pats for eac vecle. By usng tese frequent pats, te optmal frequent pats are computed and gven to te FFBNN. Te sample movng pats of vecle A s llustrated n Fvc = ma{ f p1, f p 2, L f p z } (11) In Equaton (11), f p s te ftness value of z te followng Fgure 3. te pat p z and F v c ave te pat value, wc as te ger ftness value tan te oters. Suppose we get te pat p avng a gest ftness value tan z te oter pats, and ten ts gest ftness value pat s consdered as a net vstng pat of a vecle v from te current node n c. In ts way, te vecle s future locaton s predcted from te current poston. Te well traned FFBNN reduces te tme complety as well as gvng te optmal future vecles pats, because te tranng process n FFBNN s carred out by te Partcle swarm algortm. Te smlar procedure s used for all vecles to predct te future locaton effcently. 4. EXPERIMENTAL RESULT In our mplementaton we use MATLAB verson Te proposed tecnque accurately fnds te movng vecle s locaton by fndng ter frequent pats. Here, all te vecles frequent movng pats are collected and ten optmal frequent pats of eac vecle are computed by Partcle swarm optmzaton tecnque and eac vecle frequent pat s traned n te FFBNN and afterward n performance testng, te vecles movng locaton s Fgure 3: Sample Movng Pats of Vecle A (10-4) Now, to fnd all te vecle s optmal frequent pats and tese optmal pats of eac vecle are ten traned n FFBNN. Ts well traned FFBNN s utlzed n te testng process. Te proposed tecnque s tested wt fve numbers of vecles and te predcted results are gven n te followng Table

8 Table 3: Optmal Frequent Pats dscovered for Eac Vecle by PSO and FFBNN Vecles Optmal Frequent Pats A B Accuracy Te comparson result of bot metods performance measures are gven n te Fgure 4. C D E Te proposed tecnque s predcton accuracy s sown n te below Table 4. Te vecle predcton accuracy s calculated by utlzng te formula, A C - Accuracy C p - Correctly predcted pats N p - Total number of optmal frequent pats (12) Table 4: Dfferent Vecles Performance Accuracy Results by PSO and FFBNN Vecles A B C D E Accuracy Also n prevous tecnque, te optmal frequent pats are computed by usng Genetc Algortm (GA). Tese frequent pats are gven to te FFBNN for performng te tranng process and te traned FFBNN s tested wt movng vecles. Te fve vecles predcted pats by FFBNN s gven n te below Table 5. Moreover, te predcton accuracy of FFBNN for fve vecles s gven n Table 6. Table 5: Optmal Frequent Pats dscovered for Eac Vecle by GA and FFBNN Vecles Optmal Frequent Pats A B C D E Table 6: Dfferent Vecles Performance Accuracy Results by FFBNN and GA Vecles A B C D E Fgure 4: Predcton Accuracy for Proposed and Estng Metods Usng Grap. As can be seen from Tables 4, 6 and Fgure 4, te proposed tecnque as offered 95% mean accuracy but te prevous work tecnque as gven only 88% accuracy. Here all fve vecles predcton accuracy results are ger tan te prevously used metod and te predcton accuracy of vecle B s same for bot metods. Te grapcal representaton of te accuracy performance results sows tat movng vecle locaton predcton tecnque more accurately determnes te future movng locaton tan prevous work metod. 5. CONCLUSION In ts paper, t s proposed tat movng vecle locaton predcton algortm was llustrated n detal wt ts eecuton ssues. In te proposed metod, te vecle s future locaton predcton was done usng PSO and FFBNN. Te optmal frequent pats were computed for all vecles troug PSO and te selected optmal frequent pats were utlzed n te FFBNN tranng and testng process. All tese processes ave mproved te performance of te proposed movng vecle locaton predcton algortm. Te epermental results ave ndcated tat te proposed algortm more accurately found te vecle s future locaton wt ger accuracy tan te estng locaton predcton tecnque. Tus proposed algortm as offered better performance n predctng te vecle s future locaton wt ger accuracy. 798

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