Optimization of Adaptive Transit Signal Priority Using Parallel Genetic Algorithm

Transcription

1 TSINGHUA SCIENCE AND TECHNOLOGY ISSN /14 pp Volume 12, Number 2, Aprl 2007 Optmzaton of Adaptve Transt Sgnal Prorty Usng Parallel Genetc Algorthm Guangwe Zhou 1,** Albert Gan 2, L. Davd Shen 3 1. HDR Engneerng Inc., Tampa, Florda 33607, USA; 2. Lehman Center for Transportaton Research, Florda Internatonal Unversty, Mam, Florda 33174, USA; 3. College of Engneerng and Computng, Florda Internatonal Unversty, Mam, Florda 33174, USA Abstract: Optmzaton of adaptve traffc sgnal tmng s one of the most complex problems n traffc control systems. Ths paper presents an adaptve transt sgnal prorty (TSP) strategy that apples the parallel genetc algorthm (PGA) to optmze adaptve traffc sgnal control n the presence of TSP. The method can optmze the phase plan, cycle length, and green splts at solated ntersectons wth consderaton for the performance of both the transt and the general vehcles. A VISSIM (VISual SIMulaton) smulaton testbed was developed to evaluate the performance of the proposed PGA-based adaptve traffc sgnal control wth TSP. The smulaton results show that the PGA-based optmzer for adaptve TSP outperformed the fully actuated NEMA control n all test cases. The results also show that the PGA-based optmzer can produce TSP tmng plans that beneft the transt vehcles whle mnmzng the mpact of TSP on the general vehcles. Key words: adaptve traffc sgnal control; transt sgnal prorty; parallel genetc algorthm; traffc smulaton; traffc delay Introducton Transt sgnal prorty (TSP) s desgned to help transt vehcles to cross sgnalzed ntersectons wth less delay by modfyng the normal sgnal operatons. TSP s ncreasngly becomng a domnant form of preferental treatment for transt vehcles along urban arterals. There are three categores of TSP prorty strateges: passve, actve, and adaptve [1]. Passve prorty operates contnuously regardless of whether a transt vehcle s present, whle actve (actuated) prorty s responsve to the requests of transt servce based on a pre-determned sgnal prorty desgn. The state-of-theart studes on TSP are predomnantly focused on adaptve strateges, whch are not only responsve to transt Receved: ** To whom correspondence should be addressed. E-mal: guangwe.zhou@hdrnc.com Tel: requests n real-tme, but also to current traffc condtons through real-tme optmzaton of select performance crtera such as vehcle delay and stops [2]. The general approach to consderng the mpact of the hgher occupancy of transt vehcles n adaptve TSP optmzaton s by assgnng a hgher weght to transt vehcles. Tradtonally ths weghtng strategy s mplemented wthn macroscopc models such as determnstc queung models and stochastc shockwave models. Macroscopc models are n general not sutable for modelng adaptve sgnal control snce they do not consder ndvdual vehcle arrvals, whch are a necessary nput to adaptve traffc sgnals. The use of a global weghtng factor also unavodably assumes that the passengers n a transt vehcle enter an ntersecton one at a tme, rather than all at once, thus tends to overstate the mpact of transt vehcles. Although mcroscopc smulaton models based on vehcle dynamcs have the ablty to track the behavor and status of ndvdual vehcles, the computaton requred to track

2 132 each vehcle n each smulaton step can become too excessve for real-tme adaptve traffc control. Ths paper presents an adaptve TSP strategy desgned to optmze the system performance of both the transt and general vehcles usng an effcent optmzer based on the parallel genetc algorthm (PGA). The strategy also nvolves a mcroscopc delay performance model that allows ncorporaton of a more precse weghtng factor for assgnng transt prorty. To desgn and evaluate the strategy, a smulaton testbed that ncludes a mcroscopc traffc smulator, a PGA optmzer, and a dynamc data exchange nterface lnkng the dfferent system components was frst developed. The adaptve TSP strategy was then desgned and the correspondng models establshed. 1 Development of Smulaton Testbed In ths study, the proposed system was desgned and evaluated n a smulaton testbed usng the VISSIM smulator, a mcroscopc, tme-step, and behavorbased smulaton model that s partcularly well-known for ts strength n modelng transt operatons. VISSIM conssts of four maor ntegrated modules: geometrc network edtor, traffc flow smulator, traffc control and management smulator, and evaluaton. The traffc Tsnghua Scence and Technology, Aprl 2007, 12(2): control and management smulator module ncludes the network survellance system (such as loop detectors, automatc vehcle locaton (AVL), etc.), sgnal state generator, and roadway sgnal controller (such as pretmed and NEMA (Natonal Electronc Manufacture Assocaton) actuated controllers). VISSIM allows the users to code an actuated sgnal control logc va ts vehcle actuated program (VAP), a run-tme extenson that can be used to nterpret the actuated control logc and create sgnal control commands for VISSIM networks. The extenson also provdes a dynamc data exchange nterface (DDE module) to communcate wth external sgnal control programs. The DDE module can be called by VAP n VISSIM n every smulaton second to obtan the smulated realtme traffc nformaton. Fgure 1 shows the system structure and the data flow for the adaptve sgnal desgn usng VISSIM. In ths structure, VAP-DDE s responsble for the data exchange between the external model and the VISSIM network,.e., the detector data from a VISSIM network are transferred va VAP-DDE to the external model, and the sgnal control parameters optmzed by the external model are transferred va VAP-DDE to VISSIM [3]. Fg. 1 Structure for adaptve sgnal desgn n VAP-DDE As shown n Fg. 2, the proposed smulaton testbed ncludes three maor modules: smulaton, traffc predcton, and optmzaton. The smulaton module nvolves a test traffc network that performs the followng four functons: (1) smulate geometrc network and mcroscopc traffc flow; (2) smulate the status of traffc sgnal ndcator, sgnal controller, and survellance devces; (3) exchange data wth the other modules; and (4) evaluate the system performance. The traffc predcton module performs traffc status predcton based on detector and hstorcal data, as well as estmates transt arrval tmes. The core module, adaptve TSP, ams to optmze sgnal tmng schemes based on the outputs from the frst two modules, and provde adaptve TSP servce n response to transt requests. The data flow among the system components works as follows: data on traffc volume, speed, occupancy rate, transt travel speed, poston, occupancy, and lateness nformaton are collected by the detectors and an AVL system coded n the smulaton module. These data are transferred to the traffc predcton module, whch predcts the traffc status for the next control nterval (tme step). When combned wth real-tme transt status nformaton, the predcton serves as the nput to the adaptve TSP module, whch generates the optmal sgnal scheme based on the predcted traffc demand and transt status. These sgnal scheme data are then transferred to the smulaton module and converted to traffc sgnal for control devces.

3 Guangwe Zhou et al:optmzaton of Adaptve Transt Sgnal Prorty 133 Fg. 2 System archtecture of adaptve TSP smulaton testbed 2 Adaptve TSP Strateges and Model Formulaton Conventonal TSP strateges, ncludng passve and actve TSP, am to provde a green sgnal for transt vehcles upon ther arrvals at a sgnalzed ntersecton. The basc obectve behnd these strateges can be stated as a bnary logcal decson process,.e., f possble, allow transt vehcles to go through an ntersecton wthout stoppng; otherwse, make ther watng tme as short as possble. Ths basc obectve s realzed by arbtrarly nterruptng the sgnal tmng plans ntended to optmze the ntersecton or system-wde operaton, such as extendng the green tme and early termnatng the red tme for the approach(es) wth transt arrvals. To reduce the negatve mpact of such nterference on the exstng traffc operatons, recovery strateges such as green tme rembursement and coordnaton recovery are sometmes mplemented. For the proposed adaptve TSP strategy, the obectves descrbed above for transt operaton are ntegrated nto an optmzaton model at the system-wde level. Accordngly, the adaptve TSP wll attempt to acheve equlbrum between the system level and the user level (.e., the transt vehcles). As mentoned, the common approach to provdng prorty to transt vehcles has been to assgn a weghtng factor to the prorty approach or movement as part of the optmzaton obectve functon [4]. The delay estmaton n such an obectve functon s generally based on ether macroscopc analyss models such as the Hghway Capacty Manual (HCM) [5] delay model, or mesoscopc models based on traffc streams. However, assgnng a global weghtng factor to the entre approach or movement tends to overstate the mportance of transt vehcles. To avod ths problem, ths study appled a mcroscopc delay estmaton model that can make use of the nformaton from ndvdual vehcles. In addton, the weghtng strategy was appled to only ndvdual transt vehcles that requre TSP servce. The delay estmaton model for the subect network s formulated as follows: Mnmze AD(,, k, t) = 1 {[1 + wt ( )][( Dk, ( t) Ak, ( t)] } (1) k k where AD s the average vehcle delay for the subect network, s/vehcle; s the number of sgnal phases ncluded n the tmng plan for an ntersecton; s the number of lanes ncluded n a sgnal phase for an ntersecton; k s the number of vehcles arrvng at the ntersecton; w(t) s the dynamc weghtng factor assgned for on-comng transt vehcles at tme t; D s the departure tme of ndvdual vehcle from the ntersecton stoplne; and A s the deal arrval tme of each vehcle at the ntersecton stoplne n the absence of sgnal control. In ths delay estmaton model, when an approachng bus places a sgnal prorty request to the ntersecton controller, t wll be assgned a weghtng factor to address ts prorty over the general vehcles. The weghtng factor s defned based on a comprehensve consderaton of the passenger occupancy of the transt vehcle, the queung condton of all ntersecton movements, and the schedule lateness of the transt vehcle.

4 134 The weghtng factor for bus vehcle k on lane durng phase of the ntersecton s gven n the followng equatons: WF, k( t) = VOCk fl fq (2) 0, f TL 0; 1, f TL (0, TL accept ); f TL TL L = accept 1 +, f TL [TL accept, TL max ]; TLmax TLaccept 2, f TL > TL max (3) f Q 1 MaxQ ( t) = 1, 1 AllowQ ( t),, f fq < 0, then set fq = 0 (4) where WF k, ( t ) s the weghtng factor for bus k on lane of phase at tme t; VOC k s the passenger occupancy of transt vehcle k; f L s the adustment factor for transt vehcle schedule lateness; f Q s the adustment factor for queung condtons on the lanes of nontranst phases; TL s the transt schedule lateness n mnutes; TL accept s the acceptable schedule lateness for transt vehcles n mnutes; TL max s the maxmum schedule lateness for transt vehcles; MaxQ ( t ) s the maxmum queue length on lane of non-transt phases predcted for the next optmzaton horzon; and AllowQ ( t ) s the allowable queue accommodaton lengths on lane of non-transt phases. The adustment for transt vehcle schedule lateness, as shown n Eq. (3), ncludes the followng four consderatons: (1) f there s no schedule lateness (TL < 0), there s no need to provde transt sgnal prorty; (2) f the schedule lateness s below an acceptable schedule lateness specfed by system operators (0 < TL < TL accept), such as 5 mn, the adustment factor for schedule lateness s set to 1, whch means that the delays to passengers n the transt vehcle are taken nto account by convertng the vehcle delays nto those of the passengers; (3) f the schedule lateness s beyond the specfed acceptable schedule lateness but below the specfed maxmum schedule lateness (TL accept TL < TL max ), such as 20 mn, the passengers watng at bus stops wll ncur unexpected delays and the relablty of the bus route wll be affected, and hence, a more severe penalty factor wll be appled to the weght adustment; Tsnghua Scence and Technology, Aprl 2007, 12(2): and (4) f a transt vehcle s stuck upstream of the ntersecton because of an ncdent and ncurred unreasonable schedule lateness that s beyond the specfed maxmum schedule lateness (TL > TL max ), the weght factor s kept at 2, and s no longer proportonal to schedule lateness tme. The queue condton adustment factor consders the negatve mpact of the weghtng strategy on general vehcles, as denoted n Eq. (4). If the ntersecton s operatng under heavy congested condtons, provdng sgnal prorty to transt vehcles may adversely affect the entre ntersecton operaton, such as causng vehcles to spll back to the turn bay entrance or upstream ntersectons. Under ths stuaton, the request for TSP servce wll be reected. In Eq. (4), f the overall congeston level, whch s represented by the sum of ratos of the cumulatve maxmum queue length over the allowable queue accommodaton, s more than 1, the adustment factor f Q s set to 0,.e., no TSP servce wll be consdered. 3 Genetc Algorthm Soluton A parallel genetc algorthm was used as a search engne to fnd the near-optmal traffc sgnal tmng soluton that optmzes the system performance n terms of the ntersecton average vehcle delay. The optmzaton of real-tme adaptve traffc sgnals s both complex and demandng. Conventonal optmzaton methods, ncludng calculus-based (such as hll-clmbng), enumeratve, and random search methods, lack both the speed and robustness needed for such applcatons. Ths has led to the use of genetc algorthms (GAs) a machne-learnng search method based on the mechancs of nature selecton and natural genetcs [6]. Tradtonal GAs (.e., smple GAs) suffer from slow convergence and are not sutable for real-tme applcatons. Advances n the feld of evolutonary computaton now hold potental to overcomng obstacles of smple GAs. Parallel GA (PGA), whch s able to structure the populatons nto a number of subpopulatons runnng n parallel on ether a sngle processor or across multple processors, can provde more effcent and faster solutons to complex transportaton problems. A number of alternatves are avalable for parallel GAs. In ths study, an sland PGA was used for the proposed adaptve TSP system. An sland PGA s capable of optmzng n parallel many sub-populatons

5 Guangwe Zhou et al:optmzaton of Adaptve Transt Sgnal Prorty 135 by dvdng the populaton for sngle or smple GA nto small clusters or slands, each of whch operates as an ndvdual smple GA ndependently. Perodcally the ndvduals from an sland or a subpopulaton are allowed to mgrate to other slands or subpopulatons [7]. A notable advantage of sland parallel GA s ts ablty to reduce the chance of premature convergence to a local optmum a classcal problem ncurred by smple GA [8]. Island PGAs are able to structure the populaton for smple GA nto multple, separate, and runnng-nparallel subpopulatons, whch are allowed to evolve nearly ndependently. Ths allows each subpopulaton to control ts own operaton and to mantan ts own hgh-ftness ndvduals, hence avodng beng stuck n a local optmum by explorng dfferent parts of the search space. Another mportant advantage for sland PGA s ts relatvely low demand for calculaton resources. In the rest of ths secton, the smple GA for each sland was formulated to solve the proposed adaptve TSP problem. The obectve was to mnmze the total delay of the ntersecton subect to varous constrants, ncludng mnmum green tmes, mnmum and maxmum cycle lengths, and allowable queue accommodaton space as follows. Mnmze AD(,, k, t) = 1 k k subect to: G mn G, mn C C max C, mn G = mn C, { [1 + wt ( )][( Dk, ( t) Ak, ( t)] } x X Max Q( t) < Allow Q( t), G, C > 0, (set of conddate phasng plans) (5) A crucal step toward applyng GA for adaptve TSP optmzaton problem s to formulate the genetc representaton (.e., codng scheme) for potental solutons. The followng chromosome structure was constructed to represent the decson varables to be optmzed: {Phase sequence; cycle length; green splt 1; green splt 2; green splt k;} {( ); ( ); ( ); ( ); ( );} {PhI; CI; GI ; GI ; GI }. The strng length for each decson varable depends on the optmzaton resoluton for each varable. Before the decson varables can be substtuted nto the obectve functon for the calculaton of the ftness value, they have to be decoded wth a chromosome structure. The followng decodng scheme was developed to convert the decson varables from ntegers to the unts requred by the parameters of sgnal control: max PhNum x = PhI (6) m1 2 1 CI C = mn C + (max C mn C) (7) 2 m2 1 GI G = mn G + ( C mn C) (8) GI where max PhNum, mn C, max C, and mn G represent the maxmum number of phasng plans, the allowable mnmum sgnal cycle, maxmum sgnal cycle, and mnmum phase duraton ncludng yellow plus allred ntervals for phase, respectvely; PhI, CI, and GI represent the ntegers converted from the bnary 1 2 chromosome strngs for phasng plan number, cycle length, and green splt, respectvely; and m 1 and m 2 are the lengths of chromosome strng for PhI and CI. Through the process of decodng, some constrants were ncorporated nto the system model. However, the nequalty constrant, the queue accommodaton constrant, could not be ncorporated nto the system model drectly. A penalty functon was ntroduced nto the obectve functon to transform the constraned optmzaton problem nto the unconstraned problem as shown n Eq. (9). In ths equaton, r s the penalty coeffcent, and Φ[ ] s the penalty functon. Mnmze f (,, k,t) = AD(,, k,t) + r Φ[AllowQ() t MaxQ()] t (9) Snce genetc algorthms only maxmze the ftness value, the model n Eq. (9) has to be transformed nto a maxmzaton problem by applyng a coeffcent CO max wth Maxmze ftness value =CO max f [,, k, t ] (10) GAs are fundamentally an optmzaton technque by mmckng the natural genetcs and natural evoluton k

6 136 process. Once the chromosome strng codng and the ftness value evaluaton have been establshed, the mportant step toward the GA applcaton s to mmc the bologcal evoluton process through the GA operaton, ncludng reproducton/selecton, crossover, and mutaton, to search for a near-optmal soluton. More nformaton on GAs can be found n Goldberg [6]. 4 Expermental Desgn The system descrbed above was mplemented n a smulaton envronment. The PGA optmzer was connected to the VISSIM smulator va the DDE module, whch s responsble for the data exchange between the VISSIM smulator and the PGA optmzer. The arrval Tsnghua Scence and Technology, Aprl 2007, 12(2): tmes of ndvdual vehcles at the ntersecton stoplne n the absence of traffc sgnal was collected as the nput to the PGA optmzer, whch returns the optmzed sgnal control parameters ncludng TSP servce to the smulator. The adaptve TSP system was mplemented and tested for a star network shown n Fg. 3, whch ncludes a center ntersecton and four adacent ntersectons upstream of each approach of the center ntersecton. The four adacent ntersectons were desgned manly to create traffc platoons and fluctuatons. Each approach of the center ntersecton ncludes one through lane and one left-turn bay. Fg. 3 Confguraton of the smulated network In the smulaton network, twelve traffc nput ponts, three for each of the four approaches, were assgned to the 12 entry lnks. To analyze the senstvty of the proposed system, three levels of traffc loads were used: medum, hgh, and over-saturated. The overall volume over capacty for each traffc load s, respectvely, 0.75, 0.95, and 1.15, as calculated n the hghway capacty software (HCS). In order to create traffc fluctuatons, the one-hour smulaton was dvded nto 12 ntervals, wth each assgned a traffc volume randomly. In order to accurately control the traffc volumes on the approaches of the center ntersecton, all traffc comng from each traffc generaton pont were fed nto the approach of the ntersecton. The left-turn percentages at the ntersecton were generated for each nterval randomly. The overall percentage of left turns was about 30% of the total ntersecton approach volume. Eght bus routes were desgned for the smulated network. Each through or left-turn movement at the center ntersecton has one bus route. The bus start tmes for each route were randomly generated durng the one-hour smulaton. The proposed adaptve TSP was evaluated by comparng wth the fully actuated NEMA control wth actve TSP. Two types of bus treatments were also expermented: wth TSP strategy and wthout TSP strategy. An actve TSP strategy that was ntegrated nto the VISSIM NEMA sgnal control s ready for use n ths experment [3]. Ths type of TSP was compared wth the proposed PGA adaptve TSP. Moreover, the VISSIM NEMA TSP can only provde TSP servce to at most two movements. In ths study, the through movements on the maor-street westbound and eastbound were provded wth TSP servce n the NEMA fully actuated

7 Guangwe Zhou et al:optmzaton of Adaptve Transt Sgnal Prorty 137 sgnal control. In the PGA adaptve sgnal control, all through or left-turn movements at the center ntersecton can provde TSP servce to bus vehcles. Table 1 lsts the 12 smulaton experments for dfferent sgnal control and TSP strateges under the aforementoned three traffc loads. Table 1 Smulaton expermental desgn Sgnal control TSP strategy Traffc load NEMA actuated PGA adaptve Wthout TSP Wth TSP Medum Hgh Over-saturated Experment 1 Experment 2 Experment 3 Experment 4 Experment 5 Experment 6 Experment 7 Experment 8 Experment 9 Experment 10 Experment 11 Experment 12 5 Smulaton Results Three types of vehcle delays, ncludng the general vehcle (car) delay, bus delay, and the entre ntersecton vehcle delay, were used to measure the performance of dfferent experments. Fgures 4-6 show the average vehcle delays for buses and general vehcles (cars) for medum, hgh, and over-saturated traffc loads, respectvely. In terms of the overall beneft of PGA adaptve sgnal control, the fgures show that, when compared wth the NEMA fully actuated sgnal control, the PGA adaptve sgnal control consstently resulted n sgnfcant delay mprovement for the entre ntersecton, regardless of whether TSP s provded. Wthout TSP, the adaptve sgnal control reduced the ntersecton vehcle delay by 18% for medum traffc load, 34% for hgh traffc load, and 23% for oversaturated traffc load. When compared wth NEMA TSP, the PGA TSP mproved the ntersecton delay by 20% for medum traffc load, 46% for hgh traffc load, and 13% for over-saturated traffc load. Fg. 4 Intersecton average vehcle delay under medum traffc load

8 138 Tsnghua Scence and Technology, Aprl 2007, 12(2): Fg. 5 Intersecton average vehcle delay under hgh traffc load Fg. 6 Intersecton average vehcle delay under over-saturated traffc load In terms of the overall mprovement to bus operaton, t s not meanngful to compare the bus delays for the case when no TSP s provded. Wth TSP, the PGA strategy demonstrated sgnfcant advantages for under-saturated traffc loads when compared wth NEMA strategy: 58% reducton n ntersecton bus delay for medum traffc load and 85% for hgh traffc load. Snce the TSP wth NEMA control only provdes TSP servce to the two eastbound and westbound through movements, t s not desrable to compare the overall ntersecton bus delay. The bus delay for the two movements as shown n Fgs. 7-9 gves some dfferent fndngs. As shown n Fg. 7, under the medum traffc load, the NEMA TSP and the PGA TSP result n almost the same eastbound bus delay. However, for the westbound drecton the NEMA TSP provdes a much lower bus delay (1.8 s/vehcle) than the PGA TSP (14.5 s/vehcle). Fgure 8 shows that under the hgh traffc load, the PGA TSP can reduce bus delay n both drectons by about 50% when compared wth the NEMA TSP. In the case of over-saturated traffc load, as shown n Fg. 9, the NEMA TSP can provde better TSP servce than the PGA TSP n both drectons. It can be concluded from the above results that the adaptve TSP does not always provde better TSP servce to bus vehcles when compared wth the NEMA TSP, although the PGA TSP s able to produce a sgnfcant reducton n vehcle delay for the entre ntersecton. The reason s that the NEMA TSP arbtrarly nterrupts the normal sgnal operaton to provde TSP servce when bus vehcles are detected, but the PGA TSP consders the system beneft when a TSP request s receved,.e., t always tres to fnd the optmal system-wde performance. A better way of lookng at the possble beneft produced by the PGA TSP may be to compare the bus delays from adaptve sgnal control wth and wthout TSP. Ths comparson s more reasonable because the PGA adaptve sgnal control s able to produce sgnfcant reducton n vehcle delay for the entre ntersecton. Fgures 4-6 show that when compared wth PGA wthout TSP, PGA wth TSP can reduce bus delay by 62% (22.9 s/vehcle vs. 8.7 s/vehcle), whle ncrease the

9 Guangwe Zhou et al:optmzaton of Adaptve Transt Sgnal Prorty 139 Fg. 7 Average bus delay for maor street thrus under medum traffc load Fg. 8 Average bus delay for maor street thrus under hgh traffc load Fg. 9 Average bus delay for maor street thrus under over-saturated traffc load passenger car delay by 5% for the medum traffc load (17.4 s/vehcle vs s/vehcle). Under the hgh traffc load, the PGA TSP resulted n 84% reducton n bus delay (63.6 s/vehcle vs s/vehcle) and 8% ncrease n passenger car delay (43.0 s/vehcle vs s/vehcle). Under the over-saturated traffc load, the

10 140 PGA TSP resulted n 24% reducton n bus delay and 16% ncrease n passenger car delay. Wth undersaturated traffc loads, the PGA TSP s able to acheve a sgnfcant reducton n bus delay whle avodng any adverse mpact to the general traffc. Although such benefts are reduced under over-saturated traffc load, delays to both buses and general vehcles stll represent a sgnfcant mprovement over the NEMA control wth or wthout TSP. 6 Conclusons Ths paper presented an adaptve transt sgnal strategy that ams to optmze the system performance of both transt and general vehcles usng the parallel genetc algorthm. The strategy ncorporates a more precse weghtng factor for assgnng transt prorty and was mplemented n a smulaton envronment usng the VISSIM mcroscopc smulator. The smulaton experments showed that the proposed PGA optmzaton could provde more effcent and robust search for optmal solutons for the adaptve sgnal control wth TSP. The results from the smulaton experments also showed that the proposed PGA-based adaptve sgnal control could provde sgnfcant benefts under medum, hgh, and over-saturated traffc load when compared wth the NEMA fully actuated sgnal control. In addton, the PGA-based adaptve TSP strategy presented n ths paper can effcently mprove the bus Tsnghua Scence and Technology, Aprl 2007, 12(2): operaton n the smulated ntersecton area wth no lmtatons on the number of bus routes and TSP requests. References [1] Balke K N. Development and laboratory testng of an ntellgent approach for provng prorty to buses at traffc sgnalzed ntersectons [Dssertaton]. Texas A&M Unversty, [2] Head K L. Improved traffc sgnal prorty for transt. TCRP Proect A-16, Interm Report, [3] PTV Planung Transport Verkehr AG. VISSIM Manual, October, [4] Skabardons A. Control strateges for transt prorty. In: The 79th Transportaton Research Board Annual Meetng. Washngton, D.C., [5] Hghway Capacty Manual. Transportaton Research Board, Natonal Research Councl, Washngton, D.C., [6] Goldberg D E. Genetc Algorthms n Search, Optmzaton, and Machne Learnng. Addson Wesley, [7] Goodman E. An ntroducton to GALOPPS (genetc algorthm optmzed for portablty and parallelsm 3. Release 3.2, (software and user s Gude). msu.edu/software/galopps, [8] Chen H Ab-Lebdeh G, Goodman E. Improvng performance of genetc algorthms for transportaton systems: The case of parallel genetc algorthms. In: The 83rd TRB. Washngton, D.C., 2004.