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Congestion Evaluation from Traffic FlowInformation based on Fuzzy LogicJia Lu. Li Cao

AArbnd-ln this paper, we present P new method to evaluratecongestion from trafiic flow information based on fuzzy logic.Level of congution is considered as a continuous variable fromfree flow to traffic jam. After a simulation. we uscd adaptivencuro-fuzzy inference system and trained a series of fuzzy logicrules. to estimate the congestion. As a result, general perceptionofjudg ing congestion is recovered by tk fuzzy system with bnsictraflie parameters.

l n h 7"-fuzzy logic. human perception, level ofcongestion. traflic flow

1. INTRODUCTIONT RAFFICcongestion is one of the focuses of IntelligentTransportation System all the time. It results in serious

social problem and economic problem. Thus, it is imponant todetcct where the congestion occurs, a s well as o measure andevaluate how the congestion is. In traveler navigation system,publication OF congestion degree will provide drivers usefulinformation, thus, reduce traffic jam, increase efficiency of trips,and avoid waste of fuel consumption.

In general.MICan be either '' bee" or " congested . Theclassification is alternatively absolute. Morris I. Rothenbergdefines urban highway congestion as "a condition in which thenumber of vehicles attempting to use a roadway at any giventime exceeds the ability of the roadway to carry the load atgenerally acceptable service levels" [I]. Th e concept of levels ofservice (LOS) is well established in highway capacity analysisprocedures. In such a criterion, congestion occurs by judgingV/C (volume over capacity ratio) when it exceeds a certainthrcshold. Travel Time Index ('IT)sanother criterion toexpresscongestion level, which is defined as he r atio of real travel timeto free flow travel time [2]. B. S. Kemer presentedthree-phase-traac-theory, lassifying traffic pattem into freeflow, synchmnizcd flow, and wide moving j a m which is moreelaborate in traffic congestion evaluation [3].

Manuscript rewived March 13,2003.Jia Lu is now with Deparlment of Automation, Tsinghua University,Li Cso is now wvlth Department of Automation. Tringhua University,China. (ema il: luj ia97~mai ls .~inghuaedu.~n) .China. (email: caoli@inghua.edu.cn).

0-7803-8125-4/03/S17.00 0 XMl3IEEE

l t i swel l knownthatthepmcessfromfreeflowtotrafficjamiscontinuous. Therefore, we define a new index, level ofcongestion (LOC), indicating the congestion extent of trafficflow. It is a continuous number and should much fit humanperception on congestion. LOC is related to the basic trafficparameters such as velocity and density by a fuzzy inferencesystem. This paper will show the rationality of the inferencemodel.

11. APPROACHTo achieve the objective mentioned abo ve, in a first step, wesimulate the process o f traffic flow and collect concerned traffic

parameters instead of those from real road networks. Subjectivecongestion evaluations will be conducted by watching a videoofth e simulation flow, and a congestion average o f each road inevery time period will be obtained.

During the second pan, data including those collected fromsimulation andevalu ationo fcong estion will be prepro cess ed tobe normalized. The results are treated a s fuzzy logic inputs andoutputs of a training syste m in the next step.An adaptive neuro-fuzz y inference system is adopted as the

training system to train the fuzzy logic rules in order to estimateLOC. Simulation data are inputs while human evaluation data areoutputs.Based on he results of training, we will analyze th e rationality

of such a method.

In. SMULATION AND DATA ACQUIREMENTHere, we choose Paramics' as our simulation software. In

Paramics', all macros copical p aramete rs can be collected fromthe detectors ona road. Three urban highways with 2 lanes eachare selected in our experiment (Fig. 1). We collect the meanvelocity of vehicles and road density per 30 seconds. Theinterval is less than the traffic fight period. Dur ing the process ofsimulation, a screen capture will save the scene as a video file,which will be us ed in congestion eva luation later.

Firstly, we will watch the video several times; thus, we form acommon sense of the order of congestion among 3 roads. Forinstance, congestion degree of road1 is greater than that ofroad3, and road3 greater thanroad2. Afterwards, we add an idlecl ip of about 4 seconds every 3 0 seconds'in the video. We willgaze at one road, and check a n impression of road congestion in

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and mad3 are ugged bctwccn 2 detectors

TABLE ITABLEOFOA D I 'SCONCESTION EVALUATtON(O.0-10.0)

Time Period LC C LOC(Firs t time) (Second ime)-3 16:00:30

every 30 seconds' video of traffic flow. During the 4 seconds'idle clip. write down the evaluation into a table (Table I). Theevaluation is a number between 0 an d IO, n which. 0 means freeflow while 10 means serious traffic jam. Such evaluations ofeach30 seconds correspond with traffic datacollected p er3 0 secondsin the formersimulation. Each road will beevalu ated tw ice by oneperson. Final degree of congestion is mean value of all theresults.

IV. DATA PROCESSAND MODEL TRAINING.i . Doloprocess

We design a fuzzy inference system with two input variablesan d one output variable (Fig. 2) . I n the system. inputs are mean~ 1 0 c i t yfvehic les and roaddensitydetected .and theourput islwe i of congestion ( L O O In order to adapt the requirement ofhay system. data will be pre-processed firstly.

D 17E+ea "dmi l r U ICFig. . Structure o f furzy infercncc rysicm: Consisting o f 2 inpun(density. mean velocity). I output (LOC) nd a series ofruler.

where v,-, an d vlw2 are mean velocity of two lanes, d,, an dd,, are density of two lanes, vu an d d., are the maximum ofmean velocity and density in the whole simu lation.

The maximum of mean velocity vmu in the whole simulation isconsidered as the largest free flow velocity reachable on anurban highway. This demands that simulation time is longenough. Our 2-hour-simulation meets the need.

The maximum of road density d., in the whole simulation isconsidered as the largest traffic densit y that can be burdened onan urban highway.

Evaluations ofthe congestion range from 0.00 to 10.00. Theywill be divided by IO so as to be suitable for a fuzzy systembetween 0.0 an d 1.0. The results are output data of our fuzzyinference system.npd iuhll n* atalml d*

4-ig.3. 9 mles in the fuzzy infcrencc syrrcm

B. ,Model m i n i n gOur goal is to train the fuzzy inferen ce system according to the

known input and output data. Here, w e select Sugeno model asou r fuzzy model. Every input variable has 3 membershipfunctions. and the output variable is the type of constant.Therefore. there are 9 rules in this system (Fig.3). Initialmembership functions (Fig.4 (a. b)) are same o nes randomlygenerated, and the initial outputs are set to be zeros. Aftertraining, the membership function and rules will be improved.Matlab tool box" anfis" (adaptive neuro-fuzzy infere nce system )is used here for the system training.

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Fig. 4. Mcmbcrrhip functions (m.f.) before an d a f te r training: la ) initialm.f. o f m c s n velocity. (b ) initial m. f ofdcns i ty . I C ) traincd m.f. o f m c a n

w l o c i t ? . Id) m i n c d m.f. ofdcn r i ry .

\', R E S U L T SAfter 40 epochs, mean error is below 0.1. which is normally

acceptable. Fig.4 ( c) and (d ) show the trained membershipfunction results.

After training, rules in our fuzzy inference syslem are muchimproved. as shown bclow.1 . IflDenrit!. is sparse) 2nd lmmn rclocity is slon,.J hcn (L OC 1s 0.5S26)2. I C (Denrily i s rparrc) and l m c m rclociry i s modcnm) then (LOC i s0 .4508 )1. I f Dcnrity is sparse1 and lmcan veiocity i s farti then (LOC is 0.4356)2 ti tDcn\ll! 15 m n i n ~ n lnd (mcm vclncit? IS i l o \ r ) i h r n ILOC i s0.6S8215 . If(Dcnri1y is common) and (mean rc loc i ty is modcrarcl then (LOC i s0.61)6. l r (Dcnr i iy i s common) and lmcan rc loc i ty i s fast) thcn (LOC is7. f l D m r i t y i s denscl an d fmcan velocity is slou~)hc n (LOC is 0.9399)S~ r lDenr i ly i rdenrc)rnd im c~n elocity i rmodewc) lhen(LOCis0 .85)9 . lf(Dcnrit? is dcnicl an d lmcan ucloci!y is fast) thcn (LOC io 0.4)

0 .3 157)

Surface oftrained fuzzy inference system is shown in Fig. 5(a).And fuzzy relationships between LOC and density (Fig. 5(b)).LOC and mean velocity (Fig.5(c)), illustrate a rising ofLO C whendensity goes higher or mean velocity dro ps down.

Fig.6 represents the comparison between human evaluationsand results using trained fuzzy inference sy stem by the test data.The mean error within 0.1 is acceptable.

0.50.4 0 0.5 1 0 0.5 1Densily mean-velocity

(b) (C )Fig. 5. Relationship bctwcen inputs and output: (a ) 3 - D rysrem rurfacc.(b) LOC-Dcnrity cuwc whcn mcan vclociry is 0.49. (cl LOC-mcan

~ c l o c i l y WYC whcn dcnriiy is 0.52.VL ANALYSIS

The trained model of fuzzy inference system indicates such arule that the mean velocity gro ws higher, whereas the L OC dro psdown: and along with the road density increasing, LO C gains. I trepresents the supporting relationship between LOC and trafficparameters. Human sense of the congestion may reappear byinputting velocity and density variables.

W e may discover another phenomenon in FigS (c). When themeanvelocityisinalowlevel,LOC vanes slowly. From about0.4IO 0.6. LOC changes little. However, LOC will decrease fasterwhen mean velocity gr ows greater than 0.6. That's to say,congestion are not perceived sensitively in the situation of lowtravel speed. Nevertheless, along with the increase of speed,congestion will be felt reduced subtly. Similarly, Fig. 5(b) sho wsthat ifdensity is greater or lower than a certain extent, the varietyof LOC is insensitive. In other words, people will be blunttowards low or high den sty.

All the phenomena discovered from the model abo ve is highlycoherent with human perception. This can be interpreted asfollowing results: The trained fuzzy inference system mayrecover the perception of people by giving LOC index, in thecondition of inputting mean velocity and density o fo ne road...

VII. CONCLUSIONIn this paper, we define a new index named LOC (level of

congestion) to evaluate traffic congestion. It is a continuousvariable to express the situation from free flow to traffic jam, b ywhich, travelers or trafic managen will get more directinformation, which is much adapted to their sensory evaluation.LOC based on fuzzy logic can be given from a fuzzy inferencesystem by inputting mean velocity and density. We analyzed the

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system and showed that the system is of rationality due to itscoherence with human perception.

Perception of congestion may be also influenced by someother micro parameters. e.g ., the acceleration. frequency o fchanging lan e, which w ill be studied further in the future.

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