2. RELATED WORKS AND PROBLEM STATEMENT

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 30, 1245-1260 (2014) Short Paper Traffc Congeston Evaluaton and Sgnal Tmng Optmzaton Based on Wreless Sensor Networks: Issues, Approaches and Smulaton * School of Computer Scence and Technology Dalan Unversty of Technology Dalan, 116023 P.R. Chna Ths paper proposed the model and algorthms for traffc data montorng and sgnal tmng optmzaton based on contnuum traffc model and wreless sensor networks. Gven the scenaro that sensor nodes are sparsely nstalled along the segment between sgnalzed ntersectons, an analytcal model s bult based on contnuum traffc equatons, and an adaptve nterpolaton method s proposed to estmate traffc parameters wth scattered sensor data. Based on the prncple of traffc congeston formaton, a congeston factor s ntroduced whch can be used to evaluate the real-tme status of traffc congeston along the segment, and to predct the subcrtcal state of traffc jams. The result s expected to support the sgnal tmng optmzaton of traffc lght control for the purpose to avod traffc jams before ts formaton. We smulated the traffc montorng based on Moble Century dataset, and analyzed the performance of sgnal control on VISSIM platform when congeston factor s ntroduced nto the phase optmzaton model. The smulaton result shows that ths method can mprove the spatal-temporal resoluton of traffc data montorng, and t s helpful to allevate urban traffc congeston that remarkably decreases the average delays and maxmum queue length. Keywords: ntellgent transportaton system, traffc survellance, wreless sensor networks, traffc congeston evaluaton, congeston factor, cost functon, traffc flow theory, scattered data fttng, tmng phase optmzaton, mult-objectve optmzaton 1. INTRODUCTION Traffc crowds seen n ntersecton of urban road networks are hghly nfluental n both developed and developng natons worldwde. Urban resdents are suffered poor transport facltes, and meanwhle the consderable fnancal loss caused by traffc becomes a large and growng burden on the naton s economy, ncludng costs of productvty losses from traffc delays, traffc accdents, traffc jams, envronmental polluton, and more [1, 2]. The dea that mprovements to transport nfrastructure s the effcent way has been central to transport economc analyss, but n fact ths problem can t be perfectly resolved wth better roads [3, 4]. Intellgent transportaton system (ITS) has Receved July 16, 2012; revsed November 5, 2012; accepted January 14, 2013. Communcated by Chung-Ta Kng. * Ths work was supported n part by the Natonal Hgh Technology Research and Development 863 Program of Chna under Grant No. 2012AA111902, the Natonal Key Technology R&D Program of Chna under Grant No. 2011BAK02B02, the Natonal Natural Scence Foundaton of Chna under Grant No. 60873256, and the Fundamental Research Funds for the Central Unverstes under Grant No. DUT12JS01. 1245

1246 been proven to be an effcent soluton [5]. Comprehensve utlzaton of nformaton technology, transport engneerng and behavoral scence to reveal the prncple and dynamcs of traffc flow, measurng traffc data and route vehcles to avod traffc jam before ts formaton, promotes a prospectve to resolve traffc problem from the root [6]. Nowadays, n an nformaton-rch era, the tradtonal traffc survellance and control methods are confronted wth great challenges [7]. How to obtan meanngful nformaton from large amounts of sensor data to support transportaton applcatons becomes more and more sgnfcant [8]. Modern traffc control and gudance systems are always networked n large scale whch needs real-tme traffc data wth hgher spatal-temporal resoluton, that challenges tradtonal traffc montorng technologes such as nductve loop, vdeo camera, mcrowave radar, nfrared detector, UAV, satellte mage, and GPS, etc. [9]. The state-of-the-art, ntellgent and networked sensors are emergng as a novel network paradgm, whch provdes an appealng alternatve to tradtonal traffc survellance technologes n the near future [10], especally for proactvely montorng traffc data n urban envronments under the grand prospectve of cyber physcal systems [11]. Many researchers have endeavored to traffc montorng based on novel technologes, and recent research shows that wreless sensor networks for the purpose of traffc survellance and control are wdespread applcatons [8, 12-14]. However current research stll cannot fully explan the ntrnsc prncple of traffc congeston formaton and under what condtons traffc jam may suddenly occur. M.R. Flynn et al. studed traffc congeston modelng based on macro-scope traffc flow theory and obtaned some basc results n congeston predcton [15], whch s regarded as a great step towards answerng the key queston that how can the occurrence of traffc congeston be avoded. Sgnal tmng optmzaton can be modeled as a mult-objectve optmzaton problem (MOP), and current control strateges nclude fxed-tme control, nductve control and adaptve control. In urban traffc control systems such as SCOOT, SCATS and REHODES, t always deploys sngle or double loops as vehcle detector. The tradtonal traffc detecton s Euleran sensng whch collect data at predefned locatons [16], and the loops can t be deployed n large amount as comparng to the scale of road networks, as a result the traffc data s dffcult to be acheved accurately [17]. In ths paper, we studed the ntrnsc space-tme propertes of actual traffc flow, and buld an observaton model to estmate traffc parameters based on wreless sensor networks. We are nterested n how to evaluate traffc congeston quanttatvely and what the performance of sgnal control would be f take congeston factor as one of the objectves n tmng optmzaton. The rest of the paper s organzed as follows. Current research on traffc survellance based on sensor networks s brefly revewed n secton 2. An observaton model for traffc parameters estmaton based on traffc flow theory s descrbed n secton 3. The traffc congeston evaluaton model and congeston factor based sgnal tmng optmzaton are studed n secton 4. The performance s analyzed based on smulaton n secton 5. Fnally, a concluson and future works are gven n secton 6. Symbols wll be used n ths paper are lsted n Table 1. 2. RELATED WORKS AND PROBLEM STATEMENT Several research works on traffc montorng wth wreless sensor networks have been carred out n recent years [8]. In PATH program launched by UC Berkeley and

TRAFFIC CONGESTION EVALUATION AND CONTROL BASED ON SENSOR NETWORKS 1247 Table 1. Nomenclature and symbols. x [0, L e ] Locaton n road segment t [ 0, ) Observaton tme u(x, t) Traffc flow speed (x, t) Traffc densty x (t) Vehcle trajectory p(x, t) Traffc data ˆp(x, t) Estmated traffc data M Maxmum traffc densty ũ Equlbrum speed u f Free speed on empty road p() Traffc pressure s(x, t) Flow producton rate s(k) Sensor readngs at tme k h(k) Vehcle detecton threshold t up, t down Tme sgnals exceed threshold d(k) Detecton flag t, x Temporal-spatal scales u m k Speed of vehcle m at sensor k e m k Error from sensor k of vehcle m Ê k Mean square error (MSE) = (s xt)/ Self-smlar varable C cf (t) Congeston factor of lane g l, gu Mn/max green tme G Effectve green tme J m (k) Cost functon on lane m q j n (k) Inflow n phase j q j out (k) Outflow n phase j q s (k) Arrval traffc flow at stop lne d j (k) Demand flow n phase j s j (k) Ext flow n phase j S g nj Saturaton flow for green S y nj Saturaton flow for yellow n (k) Exstng phase state l n (k) Queue length n phase Caltrans, P. Varaya et al. creatvely appled wreless sensor networks n traffc survellance and obtaned hgh precson exceedng 95% [10]. Jont wth Noka, UC Berkeley launched a plot traffc-montorng system named Moble Century to collect traffc data based on GPS sensors equpped n cellular phone [16]. M. L. L et al. studed traffc data collecton based on vehcular networks equpped wth GPS sensors [18], and Y. Zheng et al. studed traffc pattern based on hstory GPS trajectores to reveal the cause of traffc jams [3]. In TIME program, researchers developed a data compress method usng weekly spatal-temporal pattern of traffc data [19]. M. Babak et al. appled knematc wave theory n traffc model and try to reconstruct vehcle trajectores based on fxed and probe sensor data [20]. But n current research there are somethng mportant out of consderaton: (1) Few consderatons are gven to the ntrnsc space-tme propertes of traffc flow and the prncple of traffc congeston formaton. (2) How to evaluate traffc congeston quanttatvely wth suffcent precson and real-tme performance, and ntroduce t as an objectve to support sgnal tmng optmzaton? (3) How to combne sensor networks wth traffc control system to analyze future traffc state under current tmng strateges and try to avod traffc jams before ts formaton. Traffc flow theory has expanded sgnfcantly n recent decades [21]. The typcal models nclude LWR contnuum model [22] and Payne-Whtham hgher model [23]. Vast majorty of current research s focused on state-space methodology, and lmted amount of work has been performed usng space-tme model [20, 24]. Y. Sugyama et al. explaned the formaton of traffc congeston by expermental observatons [25], and based on ths, M. R. Flynn et al. bult a congeston model, Jamtons, to explan and predct traffc congeston based on travelng wave solutons of contnuum traffc models [15]. The goal of ths paper s to estmate traffc parameters based on sparsely deployed sensor networks, evaluate the degree of traffc congeston to explan the spatotemporal

1248 propertes of traffc flow, and ntroduce a congeston factor to the optmzaton model of sgnal tmng. We use Lagrangan detecton [26] to collect traffc data, n whch not only to detect pont data, but also to estmate the tme-space propertes along the road segment. The deployment of sensor networks s shown n Fg. 1, where p(x, t) denotes traffc data such as velocty and densty. p ( x, t) AP sensor k ( 1 ( k sensor measurement p x 1, t ) p x k, t ) p x n, t ) ( n sgnal controller scattered data fttng contnuum, smooth traffc flow theory p ˆ( x, t) Fg. 1. Deployment of wreless sensor networks for urban traffc survellance. The urban road network can be modeled as a drected graph consstng of vehcles v V and edges e E. Let L e be the length of edge e. The spatal and temporal varables are road segment x [0, L e ], and tme t [0, +) respectvely. On a gven road segment x e and tme t, the traffc flow speed u(x, t) and densty (x, t) are dstrbuted parameters n tme and space. Whle vehcle labeled by N travels along the road segment wth trajectory x (t), the sensor measurements u(x (t), t) and (x (t), t) are scattered values as showed n Eq. (1). Here k s the sensor node number. The problem of traffc data montorng can be transformed to estmate traffc parameters n gven spatal and temporal varables t wth these dscrete values. U t = (u 1,, u k ), P t = ( 1,, k ) T (1) 3. TRAFFIC MONITORING AND DATA ESTIMATION 3.1 The Contnuum Traffc Flow Theory and Theoretcal Models Lghthll and Whtham ntroduced the contnuum model (LWR) [22] based on flud dynamcs. Payne ntroduced dynamcs equatons and proposed the second order model (Payne-Whtham) [23]. The Payne-Whtham model s defned by Eq. (2) and the acceleraton Eq. (3), gven n non-conservatve form. ( u) s( xt, ) t x (2) u u 1 p 1 u ( u u ) (3) t x x

TRAFFIC CONGESTION EVALUATION AND CONTROL BASED ON SENSOR NETWORKS 1249 Where x and t denote space and tme, u(x, t) and (x, t) are the traffc flow speed and densty at the pont x and tme t respectvely, s traffc densty n unt of vehcles/ length, s delay, p s traffc pressure whch s nspred from gas dynamcs and typcally assumed to be a smooth ncreasng functon of the densty only,.e. p = p(). The parameter ũ denotes the equlbrum speed that drvers try to adjust under a gven traffc densty, whch s a decreasng functon of the densty ũ = ũ() wth 0 < ũ(0) = u f < and ũ( M ) = 0. Here u f s the desred speed of free flow, M s the maxmum traffc densty n congeston state at whch vehcles are bumper-to-bumper n the traffc jam. In Jamtons model [15], the relatonshp between ũ and s denoted n Eq. (4). u n f u( ) u (1 ), u( ) u 0 f (4) M M In Eq. (2), the s(x, t) s flow producton rate, and for road segment wth no ramp s(x, t) = 0, for entrance ramp s(x, t) < 0, for ext ramp s(x, t) > 0. Assumng the velocty of vehcle travelng from the gven ntersecton durng green lght nterval s v x (t), and the ntervals of green lght phase s T, thus the flow producton rate can be denoted as follows. T s( xt, ) v( tdt ) (5) 0 x The contnuum model s gven by partal dfferental equatons, and t s dffcult to obtan exact soluton n analytcal form. Based on the LWR solver developed by A. M. Bayen et al. [27], we can obtan the approxmate numercal solutons wth gven ntal parameters. That means the future state of traffc flow and congeston can be predcted and analyzed n a system scale. 3.2 Sgnal Processng for Traffc Data Estmaton Based on Sensor Networks In ths paper, we employ hgh senstve magnetc sensor to detect vehcles, as shown n Fg. 2 (a). Gven the detecton radus s R, sensor node detects travellng vehcle based on ATDA algorthm [10], whch detects vehcle based on an adaptve threshold, and estmates velocty wth tme dfference and the lateral offset, as shown n Fg. 2 (b). Where D s sensor separaton, s(t) s raw data, whch wll be sampled as sensor readngs n dscrete values s(k), and transformed to a(k) after nose flterng. a(k) A B h(k) d(k) t t A, up t B, up t A, down t B, down (a) Magnetc sensor node and gateway. (b) Presentence and velocty detecton based on ATDA. Fg. 2. Vehcle detecton va hgh senstve magnetc sensor.

1250 v D D AB AB ˆ avg (, ) mk t t t t B, up A, up B, down A, down (6) The h(k) s an adaptve threshold at detecton nterval k, and d(k) s detecton flag. The nstantaneous velocty can be estmated by Eq. (6). Here tme t up and t down are the moment when sensor sgnals exceed the threshold contnuously wth count N and M respectvely. In actual applcatons, sensor sgnals are usually error-prone, we use hstory data to elmnate errors from magnetc feld self-nterference and sgnal absence, as Eq. (7), where B(k) s baselne and [0, 1] s forgettng factor. Bk ( 1) (1 ) ak ( ), f s( ) 0 [( ks )...( k1)] buf Bk ( ) Bk ( 1), others (7) In actual applcaton, there are tght temporal correlatons among sensor readngs. Assumng the temporal-spatal scales are t and x, the vehcle trajectory r and observaton tme t are dspersed nto L and T sectons. Then the two-dmensonal x t doman can be transformed to a grd mesh, as shown n Fg. 3, whch can be denoted by Eq. (8). Where (x, t j ) s grd pont, and h and k are spatotemporal scales that can be denoted as h x and k t. x = h, t j = jk, [0, L], j [0, T] (8) t j t 0 measurement ux ˆ(, t ) x / x Fg. 3. The detecton grd n x-t space. j ( free flow u ) t u ( x, t) t k u jk ( x, t) u ( x, t) error delay x t j t j x u 0 Fg. 4. Scattered data fttng between proxmty ponts. x j u j x k u k x The total number of sensor node s K. Sensor readng u(x, t j ) n grd cell g(, j), may be consdered as a detecton unt on locaton [, + 1] x, and there s a sngle sensor node whch take effect n tme nterval [j, j + 1] t. Defne v mk the actual speed of the mth vehcle travellng from the kth sensor n grd g(, j), ˆv mk s the estmated speed calculated from sensor measures, u k s the average speed, m and m are the frst and last vehcle n detecton nterval respectvely, and u(x, t) s the theoretcal speed based on the contnuous traffc flow model. The actual and estmated traffc flow speed can be denoted by followng equatons. m m 1 1 u v, uˆ vˆ mm m mm m (9) k k k k

TRAFFIC CONGESTION EVALUATION AND CONTROL BASED ON SENSOR NETWORKS 1251 If the space-tme scale s small enough, t could be nferred that the traffc flow speed keep unchanged n the unt grd, and consequently the partal dfferental Eqs. (2)- (5) can be rewrtten n an approxmated way, as Eq. (10). Here the subscrpts and j ndcate space and tme respectvely. j j j 1 u u u x h (10) Wth the scattered measurements as boundary ntal values, the traffc data can be estmated by numercal nterpolaton, as shown n Fg. 4. For nstance of traffc flow speed dem m tecton, denote ûk and uk the estmated and actual velocty of mth (m [1, M]) vehcle on m sensor k (k [1, K]), respectvely. The estmaton error s e, whch can be formulated as: m m m e uˆ u. (11) k k k We use the same objectve functon as that n [28], whch s defned by Eq. (12). Here Ê s objectve functon, Ê k s mean square error (MSE) of traffc parameter estmaton for all M vehcles on sensor k. The purpose of optmzaton s to mnmze the total MSEs of all sensors k M K K m 2 ( ) / k k. (12) m1 k1 k1 Eˆ e M Eˆ Assume K pont data û(x, t ) obtaned n detecton area g(, j), and u(x, t ) s the correspondng value gven by traffc equatons. The nose root-mean-square error rms between model outputs and measured data can be defned as Eq. (13), whch s a twodmensonal random feld, and we assume t s unbased. 2 K uxt ˆ uxt 2 rms (13) 1 uˆ( x, t ) 1 (, ) (, ) K The velocty change n real traffc flow u(x, t) s contnuous. To elmnate nose, we ntroduce a smoothng factor based on the mnmum sum of squares of the second dervatve, as shown n Eq. (14). Where denotes two-dmensonal square detecton area. To solve the condtonal extremum problem based on Eqs. (13) and (14), we can use the smlar method n [29] based on fnte elements method. mn 2 2 uxt (, ) mn d (14) x t xt 4. CONGESTION FACTOR BASED SIGNAL OPTIMIZATION 4.1 Traffc Congeston Evaluaton and Congeston Factor There much research about traffc congeston predcton and evaluaton n last decades [30, 31]. In Jamtons model proposed by M.R. Flynn et al., the traffc congeston s

1252 modeled as travelng wave [15]. Based on LWR contnuum traffc models present n Eqs. (2)-(3), defne a self-smlar varable = (s xt)/, Eq. (15) holds. du ( u s)( u u) 2 2 d ( u s) c (15) Where s s the speed of travelng shock wave, and traffc flow densty and speed can be denoted as functon of, vz: = (), u = u(). The subcrtcal state can be predcted by Eq. (15), where c p 0 denotes the subcrtcal condton. To solve these equatons, we select the shallow water equatons [15] denoted as Eq. (16) to smplfy the problem. p = 2 /2 (16) Then Eq. (15) can be rewrtten as Eq. (17). Here m s a constant denotng the mass flux of vehcles n the wave frame of reference. du m 2 m ( u s) u (1 ) / ( ) 0 ( ) u u s d u s ( u s ) (17) M The subcrtcal condton s therefore denoted as Eq. (18). If ths equaton s satsfed, the traffc congeston s nevtable to occur. The densty wll reach M mmedately when traffc condtons exceed the subcrtcal state. u c = s + (m) 1/3 (18) The road can be regarded as share resource for vehcles and traffc flow lnks, and accordng to Jan s farness ndex for shared computer systems, the quanttatve congeston factor can be defned as Eq. (19). Here ndcates the lane number, x s the locatons coordnate wth orgn startng from stop lne, and the traffc densty s sampled from n dscrete values wth fxed frequency. The congeston factor ndcates the general congeston state on whole road segment, whch s a number between 0 and 1, and larger value means more crowded. C cf n 2 ( t) ( ( x )) / n ( ( x )) m1 m n m1 m 2 Consderng an ntersecton wth four phases numbered A, B, C and D, as shown n Fg. 5, the phase tmng can be denoted as Eq. (20). Here g l and g u represent the mnmum and maxmum green tme respectvely, and G s the effectve green tme of phase. (19) G { G A, G B, G C, G D l }, G [ g, g u ] (20) S A B C D W lne m N Fg. 5. Four phases of traffc control.

TRAFFIC CONGESTION EVALUATION AND CONTROL BASED ON SENSOR NETWORKS 1253 Under the scenaro that traffc flow stops by red sgnal, for nstance of lane m durng sgnal phase, the traffc flow from west to east wll be blocked from the begnnng of phase A, and the nterval s G A. The correspondng cost functon on lane m s denoted as Eq. (21). Here T s tmng adjustment step length, C m cf (k) and C m cf (k) represent congeston factor on lane m of traffc flow under blockng status and normal condton under green phase respectvely. The normal condton can be smulated based on Eqs. (2) and (3) wth ntal values detected by sensor networks at tme t, where s(t) 0. And traffc parameters can be predcted by resolvng traffc equatons. K m m m cf cf A 0 J ( k) C ( k) C ( k), k[0, K], K G / T (21) Wth the mplementaton of LWR solver [27], we can buld a vrtual smulator for traffc flow schedulng to analyze the traffc state, congeston factor and cost functon n a theoretcal way based on gven ntal parameters. For traffc flow of a straght lane, consder two scenaros that traffc flow run contnuously and blocked by red sgnal at tme t, the congeston factor and cost functon can be smulated. The result s shown n Fg. 6. The congeston factor can denote the congeston extent of road segment. densty factor, p/pm 1.5 1 0.5 blocked traffc flow free traffc flow 0 0 1000 2000 3000 4000 5000 locaton/m congeston factor (a) Traffc flow densty. (b) Congeston factor. Fg. 6. Traffc flow densty and congeston factor at observaton tme t. 0.8 0.6 0.4 0.2 blocked traffc flow free traffc flow 0 0 1000 2000 3000 4000 5000 locaton/m A B (k) q n q out (k) q s (k) s (k) d(k) queue l(k) Fg. 7. Urban ntersecton and road lnk model for traffc sgnal control. 4.2 The Mult-Objectve Optmzaton Model for Sgnal Control The sgnal tmng problem can be formulated as a mathematcal programmng to mnmze mult-objectve constrants [32]. Gven two sgnaled ntersectons, the varables on ntersecton and connectng lnks of phase j are shown n Fg. 7. We defne q j n (k) and q j out (k) to be the nflow and outflow respectvely, and defne d j(k) and s j (k) to be the de-

1254 mand flow and ext flow durng the phase j n an nterval [kt, (k + 1)T], where T s the tmng adjustment step, and k s a dscrete ndex. Defne S g nj and Sy nj as the saturaton flow for green and yellow tme of phase j at ntersecton n. u k n (k) ndcates the sgnal, and u k n (k) = 0 means green lght and uk n (k) = 1 means red lght. To smply the problem we just optmze the phase tme, wth assumpton that phase order s unchanged, four phases as shown n Fg. 5, transfer n the presupposed order A, B, C and D. Based on the dynamcs of traffc flow, the control objectve of the dynamc model s to mnmze the total delay and traffc congeston factor. To mnmze: N K Delay TD T l ( k) n (22) n1 In k1 M K m Congeston factor CF C ( k) (23) m1 k1 cf M K Cost factor J J ( k) (24) m1 k1 m Wth constrants subject to: g G g (25) l u ( ) 0, ; ( ) ( 1) ( l k kk l k l k q ( k) q ( k)) T (26) n n n s out j q ( k) b q ( k) (27) n j out g y g q ( k) (1 u ( k))[ S (1 ( k)) S ( k)] S ( k) u ( k) (28) out n n n n n n n n For a gven tme wndow T, based on constrants of Eq. (24), the tmng problem can be separated nto h(1 h T/g l 1) sub-problems. We can solve these h problems and obtan h non-nferorty set of optmal solutons, and then merge them to get a new non-nferorty set of optmal solutons, whch s the soluton of the orgnal problem. In ths paper we use MOPSO-CD (Mult-objectve Partcle Swarm Optmzaton Algorthm usng crowdng dstance) to fnd the optmal tmng phases. 4.3 Traffc Flow Detecton and Control Algorthms Based on above model and computatonal method, the overall block dagram of traffc data detecton and control algorthm s shown n Fg. 8. It employs magnetc sensor and detects magnetc sgnature based on ATDA. The ndvdual vehcle data s collected n tme wndow W and traffc flow speed s montored at regular ntervals. The scattered pont data U t, P t contans all sensor readngs wll be used to approxmate the traffc equaton and numercal approxmaton u(h, jk) obtaned. Fnally we can get the traffc data u(x, t) and (x, t), whch s expected to provde data to traffc control and evaluate traffc congeston.

TRAFFIC CONGESTION EVALUATION AND CONTROL BASED ON SENSOR NETWORKS 1255 r k W Band flter ATDA ak sk U / P t t LWR Numercal approxmaton Tmng optmzaton Congeston factor uxt (, ) u(h, jk) Data fttng MOP Cost Traffc schedulng functon smulator LWR solver Fg. 8. Dagram of traffc flow detecton and adaptve control model based on sensor network. The traffc congeston state can be evaluated based on Eq. (19) and we can obtan the congeston factor n every segment near the ntersecton. At the same tme, a cost functon n next control phase can be calculated wth a traffc schedulng smulator whch s based on traffc equatons and LWR solver. When we gve prorty to dfferent possble drectons and block traffc flow on other drectons, the overall delay cost from alternatve tmng strateges wll be nvolved nto a mult-objectve optmzaton model before makng the fnal sgnal. Fnally, the traffc controller wll choose the optmal tmng scheme. Ths process operates n a crculaton and n an adaptve way. 5. SIMULATION RESULT AND PERFORMANCE ANALYSIS The model and algorthms are smulated based on VISSIM platform. The traffc flow data s generated wth the Moble Century feld test dataset [16, 33] and LWR solver [27]. VISSIM s a mcroscope, tme nterval and drvng behavor based traffc smulaton toolkt. It supports external sgnal control strateges by provdng API, n whch an nterface Calculate wll be nvoked wth presupposed frequency. And user can obtan the sgnal control data from ths nterface. We desgned a software/hardware n the loop smulaton platform based on VISSIM, as shown n Fg. 9. COM API External API Data Detecton Communcaton Module Cost Functon Data Generator Feld Data LWR Solver VISSIM Sgnal Tmng Sgnal Controller Optmzaton Strateges Fg. 9. Software/hardware n the loop smulaton based on VISSIM. The traffc data for smulaton s based on Moble Century data-set. Traffc data near three ntersectons s used to smulate traffc data collecton and tmng phase opt-

1256 mzaton. The traffc network s shown n Fg. 10. We select a fxed coordnate wthout sensor, and try to estmate traffc parameters wth the method proposed n ths paper based on proxmty sensor readngs. The estmaton precson under dfferent smooth factor s shown n Fg. 11. The performance s better than traffc predcton based on BP neural network [34]. Fg. 10. Traffc networks for tmng optmzaton smulaton. 80 70 60 speed/mph 50 40 30 ground truth BP neural network 20 data fttng based on traffc equatons, w=0.8 data fttng based on traffc equatons, w=0.9 10 0 100 200 300 400 500 600 700 800 tme/sec Fg. 11. Performance of traffc data estmaton based on traffc equaton. In the sgnal tmng smulaton, we analyzed the performance by four scenaros: fxed-tme control, nductve control, adaptve control, and congeston factor constraned control whch combnng delay wth traffc congeston factor together as the optmzaton objectve, and compare the performance n average delay and queue length. On the same traffc flow dataset, the performance s llustrated n Fgs. 12 and 13. The crtera nclude average delay and the maxmum queue length. The result shows that congeston factor based control optmzaton can ncrease the performance wth lower average watng tme and shorter queue length. 35 average delay / sec 30 25 20 fxed-tme control nductve control 15 adaptve control congeston factor constraned control 10 0 200 400 600 800 1000 1200 1400 phase cycle Fg. 12. Average delay performance under dfferent traffc control strateges.

TRAFFIC CONGESTION EVALUATION AND CONTROL BASED ON SENSOR NETWORKS 1257 total queue length / veh 250 200 150 fxed-tme control nductve control adaptve control congeston factor constraned control 100 0 200 400 600 800 1000 1200 1400 tme / sec (a) Queue length under traffc volume 1000 veh/h. total queue length / veh 300 250 200 150 fxed-tme control nductve control adaptve control congeston factor constraned control 0 200 400 600 800 1000 1200 tme / sec (b) Queue length under traffc volume 2000 veh/h. Fg. 13. Total queue length performance under dfferent traffc control strateges. Fg. 13 shows mnmum total queue length under dfferent traffc volume that denotes capacty of congeston dspersng under dfferent traffc control strateges. Under peak flow 1000veh/h, comparng to fxed-tme control, nductve control and adaptve control, congeston factor constraned control decrease queue length wth 58%, 57% and 39% respectvely, and under peak flow 2000veh/h, these values are 84%, 59% and 27% respectvely. Result shows that congeston factor constraned control has obvous advantages n congeston dspersng. 6. CONCLUSION In ths paper we studed the traffc flow montorng, congeston evaluaton and congeston factor based control method based on wreless sensor networks. Takng nto consderaton the ntrnsc propertes of traffc flow and the model of traffc congeston, try to obtan optmal phase tmng wth better performance. The man dea s to study congeston state and ts nfluence on future traffc flow, and combne traffc equatons wth optmzaton functon. Based on the numercal soluton of traffc equatons va approxmate method, traffc data s refned wth data fttng and correlaton between sensor readngs. The model and algorthms are smulated based on VISSIM platform and Moble Century dataset. The result shows better performance and t s helpful to decrease average delay and maxmum queue length. Current research s lmted to smple segments wth contnuous traffc flow. Future research should focus on complex segments and even road network, such as ramp, long road wth mult-ntersectons. And the traffc control strategy, road capablty and dy-

1258 namcs caused by ncdents need to take nto consderaton n actual applcatons. Furthermore, complex traffc flow pattern smulaton and traffc control strateges on a networked scale among mult-ntersectons and arbtrary connectng segments n road network s also an mportant aspect n next step. REFERENCES 1. H. K. Wang, L. X. Fu, Y. Zhou, et al., Trends n vehcular emssons n Chna s mega ctes from 1995 to 2005, Envronmental Polluton, Vol. 158, 2010, pp. 394-400. 2. IBM, IBM 2011 commuter pan survey, http://www.bm.com, 2011. 3. M. Davd, The myth of travel tme savng, Transport Revews, Vol. 28, 2008, pp. 321-336. 4. E. Naone, GPS data on Bejng cabs reveals the cause of traffc jams, MIT Technology Revew, 2011, pp. 1-2. 5. J. P. Zhang, F. Y. Wang, K. F. Zhang, et al., Data-drven ntellgent transportaton systems: a survey, IEEE Transactons on Intellgent Transportaton Systems, Vol. 12, 2011, pp. 1624-1639. 6. A. Faro, D. Gordano, and C. Spampnato, Integratng locaton trackng, traffc montorng and semantcs n a layered ITS archtecture, IET Intellgent Transport Systems, Vol. 5, 2011, pp. 197-206. 7. R. M. Murray, K. J. Astrom, S. P. Boyd, et al., Future drectons n control n an nformaton-rch world, IEEE Control Magazne, Vol. 23, 2003, pp. 20-33. 8. J. W. C. Lnt, A. J. Valkenberg, A. J. van Bnsbergen, et al., Advanced traffc montorng for sustanable traffc management: experences and results of fve years of collaboratve research n the Netherlands, IET Intellgent Transport Systems, Vol. 4, 2010, pp. 387-400. 9. L. E. Y. Mmbela and L. A. Klen, A summary of vehcle detecton and survellance technologes used n ntellgent transportaton systems, Jont Program Offce for Intellgent Transportaton Systems, FHWA, Department of Transportaton, USA, 2007. 10. H. Amne, K. Robert, and V. Pravn, Wreless magnetc sensors for traffc survellance, Transportaton Research Part C, Vol. 16, 2008, pp. 294-306. 11. F. Z. Qu, F. Y. Wang, and L. Q. Yang, Intellgent transportaton spaces: vehcles, traffc, communcatons, and beyond, IEEE Communcatons Magazne, Vol. 48, 2010, pp. 136-142. 12. M. Welsh, Sensor networks for the scences, Communcatons of the ACM, Vol. 53, 2010, pp. 36-39. 13. M. Bottero, B. D. Chara, and F. P. Defloro, Wreless sensor networks for traffc montorng n a logstc center, Transportaton Research Part C, Vol. 26, 2013, pp. 99-124. 14. T. Davd, M. Danele, C. Iacopo, et al., Usng wreless sensor networks to support ntellgent transportaton systems, Ad Hoc Networks, Vol. 8, 2010, pp. 462-473. 15. M. R. Flynn, A. R. Kasmov, J. C. Nave, et al., Self-sustaned nonlnear waves n traffc flow, Physcal Revew E, Vol. 79, 2008, pp. 61-74.

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1260 ceedngs of the 2nd Internatonal Workshop on Intellgent Systems and Applcatons, 2010, pp. 1-4. We Zhang () receved the M.S. degree n Software Engneerng, the B.S. degree n Telecommuncaton Engneerng, from Jln Unversty, Changchun, Chna, n 2005 and 2002 respectvely. He s currently workng toward the Ph.D. degree wth the School of Computer Scence and Technology, Dalan Unversty of Technology, Dalan, Chna. Hs research nterests nclude wreless sensor networks, vehcular sensor networks, traffc survellance, traffc lght control and moble computng, etc. Guo-Zhen Tan () receved the M.S. and Ph.D. degree n Computer Engneerng from Harbn Insttute of Technology, Harbn, Chna and Dalan Unversty of Technology, Dalan, Chna, n 1998 and 2002 respectvely. He s a Professor wth the School of Computer Scence and Technology, Dalan Unversty of Technology, Dalan, Chna. He was a vstng scholar wth the Department of Electrcal and Computer Engneerng, Unversty of Illnos at Urbana-Champagn, IL, U.S.A, from Jan 2007 to Jan 2008. Hs research areas nclude cyber-physcal system, Internet of vehcles, network optmzaton, ntellgent transportaton system and wreless sensor networks. Nan Dng () receved the M.S. and Ph.D. degree n Computer Scence n 2005 and 2011 respectvely, both from School of Computer Scence and Technology, Dalan Unversty of Technology, Dalan, Chna. He s an Assstant Professor wth the Department of Computer Scence and Engneerng, Dalan Unversty of Technology, Dalan, Chna. Hs current research nterests nclude wreless sensor networks, traffc data fuson and analyss, embedded and real-tme systems. Guang-Yuan Wang () receved the B.S. degree n Computer Scence and Technology from Dalan Unversty of Technology, Dalan, P.R.C., n 2008. He s a M.S. canddate wth the Department of Computer Scence and Engneerng, Dalan Unversty of Technology, Dalan, P.R.C. Hs research nterests nclude traffc sgnal control, sgnal tmng optmzaton, etc.