Real-Time Traffic Signal Intelligent Control with Transit-Priority

Transcription

1 738 JOURNAL OF SOFTWARE, VOL. 7, NO. 8, AUGUST 202 Real-Tme Traffc Sgnal Intellgent ontrol wth Transt-Prorty Xanyan Kuang School of vl Engneerng and Transortaton, South hna Unversty of Technology, GuangZhou, Guangdong, 50640, hna 2 School of Electrc Engneerng and Automaton, Jangx Unversty of Scence and Technology, Ganzhou, Jangx, 34000, hna Emal: xanyankuang@63.com Lunhu Xu School of vl Engneerng and Transortaton, South hna Unversty of Technology, GuangZhou, Guangdong, 50640, hna Emal: lhxu@scut.edu.cn Abstract Ths aer rooses a real-tme traffc sgnal ntellgent control method wth transt-rorty. The objectve of ths control method s to reduce the delays of assengers and secal vehcles. Transt-rorty s dvded nto the secal transt-rorty whch s an absolute rorty and the normal transt-rorty whch s a relatve rorty. When the detectors n the red hase detect secal vehcles arrval, the hase wll become a secal hase, the current green hase must be nterruted, and the secal hase wll be run. After the secal vehcles ass through, the next runnng hase selecton wll be done usng the hase selecton method wth normal-transt-rorty, by ths tme, the hase wth more urgency wll be selected. It embodes transt-rorty dea. The green ncrease tme of current hase s nferred by a fuzzy controller of whch the nuts are the vehcles number of current hase and next hase. Mult-layer neural network s used to realze ths fuzzy controller. omared wth fxed-tme control method and the fuzzy control method, smulaton research shows that ths method obtans a good erformance n decreasng the delays of assengers and secal vehcles. Index Terms Traffc sgnal control, Transt-rorty, Fuzzy Neural network, Traffc smulaton I. INTRODUTION In the modern urban transortaton system, traffc congeston s more and more serous. Traffc sgnal control lays an mortant role n allevatng ths stuaton. It s dffcult to buld accurate mathematcal models for a traffc system whch s a tme-varant stochastc comlex system. A fuzzy control system, whch mtates the fuzzy concet of the human bran and successful control strategy, s alcable to the tme-varant traffc control system. Pas [] frstly ut forward the fuzzy control method for a traffc juncton n 977, and many further Manuscrt receved Ar., 20; revsed Se. 5, 20; acceted Nov. 2, 20. orresondng author: Lunhu Xu. Project number: 200GQS0076 researches usng fuzzy logc technology are taken laced for the traffc sgnal control. A fuzzy logc traffc sgnal controller for an solated ntersecton usng a two-stage fuzzy logc rocedure s desgned [2] of whch the erformance s better than the traffc-actuated controller. A mult-hase adatve control algorthm s resented based on the learnng ablty of fuzzy neural control [3], whch can not only decrease the average vehcle delays but also adjust the sgnal erod automatcally. A model called Fuzzy Logc Mult-hased Sgnal ontrol (FLMuS) has been develoed for solated sgnalzed ntersectons and obtans encouraged results [4]. An adatve fuzzy logc sgnal controller (AF) s resented for the urban traffc network [5]. Schmöcker [6] have resented a mult-objectve sgnal control method usng fuzzy logc of whch the membersh functons are otmzed by a genetc algorthm usng the VISSIM mcroscoc traffc smulator. The results of a case study n London rove that the method s ractcal and effcent. In most of these methods, the hase sequence of traffc sgnal control usually s fxed. Whle the fxed hase sequence method wll generate the unnecessary delays when the number of vehcles n one hase s few and others s large. So, some methods [7][8][9] whch the sequence of hases s changeable and flexble are resented n order to decrease vehcle delays more effectvely at the ntersecton. However, the control objectve of those methods s to average vehcle delays, whch means that the bus wth more assengers and the car wth fewer assengers wll be treated equally. So t s unfar for the bus assengers. Esecally, some secal vehcles such as ambulance, fre truck, olce vehcle and other emergency vehcles are also treated as the socal vehcles, whch greatly nfluence the effcency of these emergency vehcles. us rorty or transt sgnal rorty technques can mrove the servce level of urban ublc transortaton system. So t s more and more concentrated by many 202 AADEMY PULISHER do:0.4304/jsw

2 JOURNAL OF SOFTWARE, VOL. 7, NO. 8, AUGUST researchers [0-3]. A rule-based model called SPPORT (Sgnal Prorty Procedure for Otmzaton n Real Tme) whch rovdes secalzed mechansms for transt rorty s roosed n [0]. In [], the ntegrated models for adatve bus-reemton control can make a reemton decson whch consdered vehcle, bus schedule and assenger delay. A statstcal samlng method [2] s used to smulate vehcle delays nstead of the conventonal mcroscoc traffc smulatons. Otmzaton of the transt-rorty sgnal control whch combnes Genetc Algorthms and VISSIM (a traffc mcrosmulaton), lays a good erformance [3]. A realtme, rule-based, reactve arteral bus sgnal rorty algorthm s studed n [4]. However, most of them are based on the fxed-tme or actuaton control, and don t consder the rorty of these emergency vehcles. Ths aer rooses a real-tme traffc sgnal ntellgent control method wth transt-rorty. The objectve of ths method s to reduce the delays of assengers and secal vehcles. Frst, the hase wth more urgency s referental to be selected as the next runnng hase by the end of current hase. Ths embodes transt-rorty dea. Second, the green ncrease tme of current hase s nferred by a fuzzy controller of whch the nuts are the vehcles number of current hase and next hase. Mult-layer neural network s used to realze ths fuzzy controller. omared wth the tradtonal fuzzy control method and fxed-tme control method, smulaton research shows that ths method obtans a good erformance n decreasng the delays of assengers and secal vehcles. II. TRANSIT-PRIORITY INTELLIGENT SIGNAL ONTROL METHOD There are two knds of transt-rorty. One s secal transt-rorty whch s an absolute rorty. The other s normal transt-rorty whch s a relatve rorty. When the detectors n the red hase detect secal vehcles arrval, ths hase wll become secal hase, the current green hase must be nterruted, and the secal hase wll be run. After the secal vehcles ass through, the next runnng hase selecton wll be done usng the hase selecton method wth normal-transt-rorty, by ths tme, the hase wth more urgency wll be selected. Generally, n the traffc control, the cycle tme should be shorter when the vehcle number s less, but t can not be shorter than m 5 s (m s the number of hases), to avod a stuaton where vehcles of one drecton can not ass through the ntersecton wthn 5s, mactng traffc safety. When the vehcle number s large, the cycle tme should be longer, but can not exceed 200s, otherwse the red tme wll be too long, and drvers wll not tolerate t. The traffc sgnal control s based on fuzzy logc technology n the aer. The mult-hase fuzzy control method for traffc sgnal wth transt-rorty can be descrbed as follows: Ste : Assgn the mnmum green tme G mn and maxmum green tme G max for each hase. The green tme of any hase wll be greater than G mn and less than G max. Assume the current runnng hase s the hase, and has been runnng for G. Ste 2: Detect secal vehcles all the tme, f secal vehcles aear, the corresondng hase wll be the secal hase and the next runnng hase j. y the tme, f the current hase just s hase j, then the green tme wll contnue to the tme all secal vehcles have assed through, else the current green hase must be nterruted when G > G mn, and convert to hase j, that s to say, hase j wll become the current runnng hase. After secal vehcles ass through or there are no secal vehcles detected, contnue to Ste 3. Ste 3: Detect the vehcle number l of the hase by the end of G, and select the next hase j accordng to the method wth normal transtrorty. Ste 4: If l =0, or l <v (v>0), and Δl =l + -l s larger than a fxed value e (e 0), or G =G max, then turn to the next hase + and back to Ste 2; Otherwse, contnue to Ste 5. Ste 5: Accordng to exerence of olceman and fgure of ntersectons, buldng rules of fuzzy control. Determne the green ncrease tme ΔG accordng to the fuzzy rules and the values of l and Δl. f G +ΔG>G max, then ΔG=G max - G, otherwse G +ΔG G, back to Ste 2. III. METHOD FOR PHASE SELETION WITH NORMAL TRANSIT-PRIORITY For the fxed-hase-sequence mult-hase traffc sgnal control at the ntersecton, when dfferent-drecton traffc flow s unbalanced, t often haens that one hase wth few vehcles gets the rght of way, and other hases wth more vehcles have to wat. It wll ncrease more vehcle delays. Wth changeable hase sequence control, ths stuaton can be avoded. In ths aer, the selecton of next hase s accordng to the traffc urgency of every hase. The traffc urgency of hase s related to the vehcle tye, the assenger number of vehcles, the vehcle number and the stong tme of the vehcles n ths hase. All vehcles have urgency weght. The hase wth hghest urgency wll be selected as the next runnng hase. The traffc urgency U of hase can be descrbed as follows: U = max{ U, U 2} () U / = (2) max Tsto Tmax, or H veh H det U 2 = (3) 0 else = w N + w N (4) 202 AADEMY PULISHER

3 740 JOURNAL OF SOFTWARE, VOL. 7, NO. 8, AUGUST 202 h N + h N = h (5) H veh + nr Where, s traffc caacty of hase (not traffc volume). max s maxmum traffc caacty of hase. T sto s the accumulatve sto tme of the frst vehcle of hase. T max s the lmt tme drvers can tolerate. H veh s the length of vehcle team, H det s the dstance between two detectors n a lane. w and w are the weght of bus and car, whch can be seen as the mean assenger caacty of bus and car. N and N are the number of buses and cars n hase. h and h are the average sace headway of cars and buses, nr s the number of lanes. Tsto s a condton for the next hase. If the traffc urgency of one hase s always mnmal wthn a tme erod, ths hase can not get the rght of way. T sto can avod ths stuaton. When the stong tme of the vehcle of one hase T sto s larger than T max, the next hase must be the latter. IV. FUZZY ONTROLLER DESIGN The fuzzy controller ncludes three arts: fuzzy model, fuzzy reasonng model and fuzzy decson model. The rocess of fuzzy s to turn measured value (exact value) to fuzzy subset. In the aer, the nut varables of fuzzy controller are l and Δl, where l s the vehcle number of the lane, Δl(Δl=l + -l ) s the dfference between the l of current hase and next hase. The lngustc values of l are Q (zero), Q 2 (very few), Q 3 (few), Q 4 (medum), Q 5 (lttle long), Q 6 (long), Q 7 (too long). The lngustc values of Δl are N, NS, O, PS, P. The membersh functons for varable l and Δl are shown n Table I and Table II. The outut varable s the green ncrease tme ΔG. The lngustc values of ΔG are G (zero), G 2 (very few), G 3 (few), G 4 (medum), G 5 (lttle long), G 6 (long), G 7 (too long). The membersh functons for varable ΔG are shown n Table III. TALE I. MEMERSHIP FUNTIONS FOR VARIALE l Fuzzy l sets Q Q Q Q Q Q Q TALE II. MEMERSHIP FUNTIONS FOR VARIALE Δl Fuzz Δl y sets N NS O PS P TALE III. MEMERSHIP FUNTIONS FOR VARIALE ΔG Fuzzy ΔG sets G G G G G G G TALE IV. FUZZY ONTROL RULES l Δ l N NS O PS P Q G G G - - Q 2 G 2 G 2 G 2 G G Q 3 G 3 G 3 G 3 G 2 G 2 Q 4 G 4 G 4 G 4 G 3 G 3 Q 5 G 5 G 5 G 5 G 4 G 4 Q 6 G 6 G 6 G 6 G 5 G 5 Q 7 G 7 G 7 G 7 G 6 G 6 Fuzzy reasonng summarzes eole s control exerence, dfferent control may be adoted accordng to dfferent measured value, for traffc control, seven rules may be acqured accordng to ts features. Table IV shows the 33 fuzzy rules by summarzng ractce and exert s exerence. Maxmum defuzzfcaton aroach s used for Fuzzy decson. V. P NEURAL NETWORK ALGORITHM USED TO IMPLEMENT FUZZY ONTROLLER Although the fuzzy controller wth the advantage of exert reasonng doesn t requre accurate mathematc models, the fuzzy rules and membersh functons are unalterable, whch can t adat varable traffc flow constantly. Artfcal neural network s a system whch made u of many nodes called neuron, t can smulate basal features of bran, and deals wth nformaton by adotng arallel and dstrbuted mode, ts resonse seed of hardware 202 AADEMY PULISHER

4 JOURNAL OF SOFTWARE, VOL. 7, NO. 8, AUGUST mlementaton s very hgh, meanwhle, t has the followng functons: adatve, self-learnng and faulttolerance, etc. The fuzzy controller above can be mlemented by a four-layer neural network. The fuzzy controller network s a feedforward network that encodes the decsonmakng n the fuzzy rule base. The actvaton functons of the network are dfferent fuzzy set oeratons. West North East Layer Inut layer Δl l Traffc flow Vehcle detectors Layer 2 Lngustcs layer P PS O NS N Q7 Q6 Q5 Q4 Q3 Q2 Q South Layer 3 Rules layer Layer 4 Outut layer ΔG hase hase 2 hase 3 Phase 4 Fgure 2. Structure of Intersecton and Phases Fgure. Structure of Fuzzy Neural Network. The frst layer s nut layer. The nodes reresent nut lngustc varables l and Δl. The second layer s membersh-functon layer. Ths layer comutes the values of the membersh functons of the nut varables. The thrd layer s a fuzzy rule base layer. It does the AND-MIN oeraton. The fourth layer s an outut layer. It s a defuzzfcaton rocess whch calculates the total outut of the rule base. The network tranng rocess s that: Inut the values of the algorthm n network and the samle values showed n table -4. Intalze the weghts of the network to, the fuzzfcaton of the condton art s erformed. Tran the weghts of the network n term of the error gradent descent. If the traned error s less than the demanded traned error, the tranng can be end and the weghts are oututted, else the tranng of the weghts wll contnue. VI. SIMULATION RESEARH In order to comare the effect of dfferent control methods, we buld the smulaton late and test the erformance of three traffc control methods on the comuter. The three methods are fxed tmng control (FT), fuzzy control (F) and ths aer s transt-rorty fuzzy neural network control (TP-FNN). We rogram the smulaton rocedure wth MATLA 7.0 and V++. The reresentatve sgnal control ntersecton and the hases are shown grahcally n Fg.2. In each lane there are two vehcle detectors used to detect the number of cars and buses. The dstance between the two detectors n any lane s assumed to 50m. If the average sace headway of car s 6m, and the buses s 2m, then the 50m lane could at most contan 25 cars or 2 buses, and all the vehcles n the lane can ass through the ntersecton wth the mean seed of 0m/s n the erod of mnmum green tme (assumed to 5s). Assume the maxmum green tme of the straght drecton flow s 55s, the maxmum green tme of left turn flow s 30s, and the maxmum stong tme whch drvers can tolerate Tmax s 20s. Suosng arrval rate of traffc flow at ths ntersecton s from 0.0 to 0.6 vehcles er second. Secal vehcle arrval rate s vehcle er 5 mnutes. uses arrve randomly and ts roorton vares from 0% to 30%. Assume mean assenger caacty of cars (the weght) w s 3, and mean assenger caacty of bus w s 30. Smulaton s carred out eght tmes, and smulaton tme er tme s one hour. Durng smulaton, the delays of vehcles and assengers are evaluated by followng equatons: D = n v D n = n (6) n D = ( w D + wd ) n w + n w (7) = = 202 AADEMY PULISHER

5 742 JOURNAL OF SOFTWARE, VOL. 7, NO. 8, AUGUST 202 Here D v s the mean vehcle delays, D s mean assenger delays, D s the delay of vehcle, n s the number of cars, n s the number of buses, n=n +n. In the secal transt-rorty smulaton, we get erfect result whch the secal vehcle delays range s from 3. to 8.8 second. The normal transt-rorty smulaton results of three methods are shown n Fg.3-4. Fg.3-4 shows that Mean assenger delays (MPD) of TP-FNN are ncreased by 23% than F at best. The delays usng the last two methods have evdent mrovement comarng wth the method of fxed tmng control. Ths aer s method has best erformance n decreasng assenger delays than any other methods, and ts mean vehcle delays s also less than others. Mean Vehcle Delay(MVD) (second) Mean Passenger Delay(MPD)(second) MVD of FT MVD of F MVD of TP-FNN Vehcle arrval (vehcle er second) Fgure 3. Smulaton Results of MVD MPD of FT MPD of F MPD of TP-FNN Vehcle arrval (vehcle er second) Fgure 4. Smulaton Results of MPD VII. ONLUSIONS In ths aer, a method called transt-rorty fuzzy neural network control (TP-FNN) s aled to the sgnal control of ntersecton. In ths method, the secal vehcles wll be absolutely referental. In the normal transt-rorty, the hase selecton s accordng to traffc urgency of every hase; the desgn of a fuzzy controller for green ncrease tme consders the vehcle number n the current hase and next hase. A four-layer neural network s used to mlement ths fuzzy controller. Smulaton results show that ths fuzzy neural controller lays good erformance. The mean assenger delays and mean secal vehcle delays are reduced obvously. AKNOWLEDGMENT Ths work was suorted by Natonal Natural Scence Foundaton of hna ( ) and by the Natural Scence Foundaton of Jangx Provnce of hna( 200GQS0076). REFERENES [].P. PAPPIS, E. H.MAMDANI. A fuzzy logc controller for a traffc juncton, IEEE Trans on system. Man and ybernetcs, vol.7, no.0, , 977. [2] M.. Traba, M.S. Kaseko and M. Ande, A two-stage fuzzy logc controller for traffc sgnals, Transortaton Research Part, vol.7, no.6, , 999. [3] L. XU, L. XI, L. ZHONG, Adatve mult-hase fuzzy control of sngle ntersecton based on neural network, hna Journal of Hghway and Transort, vol. 8, no. 3, , [4] Y. S. Murat, E. Gedzloglu, A fuzzy logc mult-hased sgnal control model for solated junctons. Transortaton Research Part : Emergng Technologes, vol. 3, no.,. 9-36, [5] R. L, H. Lu, us-rorty traffc sgnal mult-layer fuzzy control model, Qnghua Daxue Xuebao/Journal of Tsnghua Unversty, vol. 46, no. 9, [6] Schmöcker J.D., Ahuja S., Mchael G.H. ell, Multobjectve sgnal control of urban junctons - Framework and a London case study. Transortaton Research Part : Emergng Technologes, 2008, v 6, n 4, [7] X. FAN, Y. LIU, Alterable-Phase Fuzzy ontrol ased on Neutral Network. Journal of Transortaton Systems Engneerng and Informaton Technology. 2008, vol. 8, no., [8] H. Gao, L. L, R. Lu, F. Wang, hangeable hases sgnal control of an solated ntersecton, Proceedngs of the IEEE Internatonal onference on Systems, Man and ybernetcs, vol. 5, , [9] G. JIANG, H. GUO, "Traffc self-organzng sgnal control method for congested solated ntersecton," Journal of Harbn Insttute of Technology, vol. 42, , 200. [0] M. onrad, F. Don, S. Yagar, Real tme traffc sgnal otmzaton wth transt rorty, recent advances n the sgnal rorty rocedure for otmzaton n real tme model, Transortaton Research Record, 634. Washngton D: Transortaton Research oard, , [] G. hang, M. Vasudevan,. Su, Modellng and evaluaton of adatve bus-reemton control wth and wthout Automatc Vehcle Locaton systems, 202 AADEMY PULISHER

6 JOURNAL OF SOFTWARE, VOL. 7, NO. 8, AUGUST Transortaton Research Part A: Polcy and Practce. 996, vol.30, [2] D. Yao, Y. Su, Y. Zhang, L. L, S. heng, Z. We, ontrol strateges for transt rorty based on queue modelng and surrogate testng, Journal of Intellgent Transortaton Systems: Technology, Plannng, and Oeratons, vol. 3, no. 3, , 2009 [3] J. Stevanovc, A. Stevanovc, P. Martn, T. auer, Stochastc otmzaton of traffc control and transt rorty settngs n VISSIM, Transortaton Research Part : Emergng Technologes, vol. 6, no. 3, , [4] Z. Zou, "An Arteral us Sgnal Prorty Algorthm," Journal of Tongj Unversty(Natural Scence), vol. 36, , 200. Xanyan Kuang s born n Jangx Provnce, hna, n 976. He s currently a Ph.D. canddate at South hna Unversty of Technology, GuangZhou, hna. He receved hs MS degree n control theory and control engneerng from Jangx unversty of Scence and Technology, hna, n 2005 and S degree n comuter alcaton from Wuhan unversty, hna, n 999. He s an assocate rofessor at Jangx unversty of Scence and Technology, GanZhou, hna. Hs major nterests nclude ITS, traffc control and smulaton, and otmal control. Lunhu Xu s an rofessor(doctoral suervsor) n School of vl Engneerng and Transortaton, South hna Unversty of Technology, GuangZhou, 34000, hna. Hs research nterests nclude ntellgent control, otmal control, traffc engneerng and ITS. 202 AADEMY PULISHER