13th COTA International Conference of Transportation Professionals (CICTP 2013) Cen ZHANG a, Jing TENG b *

Transcription

1 Avilble online t ScienceDirect Procedi - Socil nd Behviorl Scien ce s 96 ( 203 ) th COTA Interntionl Conference of Trnsporttion Professionls (CICTP 203) Cen ZHANG, Jing TENG b * Ph.D. cndidte, Key Lbortory of Rod nd Trffic Engineering of the Ministry of Eduction, Tongji University, 4800 Con Highwy, Shnghi 20804, P.R.Chin. E-mil:zhngcen_20@tongji.edu.cn b Associte Professor, Key Lbortory of Rod nd Trffic Engineering of the Ministry of Eduction, Tongji University, 4800 Con Highwy, Shnghi 20804, P.R.Chin. E-mil: tengjing@tongji.edu.cn Abstrct Since dwell time usully tes lrge prt of bus trvel time, the lrge vribility in dwell time lwys mes ccurte prediction of rrivl time\trvel time difficult. On the other hnd, Automtic Vehicle Loction (AVL) nd Automtic Pssengers Counters (APC) systems re incresingly implemented for trnsit opertion, which yield vst mount of rel time dt. The emphsis of this reserch is to develop bus dwell time model bsed on AVL nd APC dynmic dt, which is cpble of providing rel time informtion on bus rrivl times. This model cn be used for stop-bsed control strtegies s well. The dwell time model estblished in this pper not only includes the number of pssengers bording nd lighting, but lso considers secondry fctors lie crowding nd fre type. The number of bording nd lighting pssengers is estimted by pssenger rrivl rte, bus hedwy, nd cpcity. Collection method, service mode, cpcity restriction nd occupncy of the vehicle re ll ten into ccount in the model. Furthermore, the model is vlidted with the dt of bus line Jiding 3 in Shnghi, Chin. It is compred with two previously developed models for the sme route in four dt sets. The results indicte tht the models cn be well pplied in high demnded urbn bus lines, especilly in presence of high occupncy of vehicles. Since the effectiveness of estimtion models is verified by sttisticl nlysis methods, it will help in obtining relible lgorithm which cn be dopted for bus rrivl time/trvel time prediction nd ssessing trnsit stop-bsed dynmic control ctions. 203 The Authors. Published by Elsevier by Elsevier Ltd. Open B.V. ccess under CC BY-NC-ND license. Selection nd/or peer-review under responsibility under responsibility of Chinese of Overses Chinese Trnsporttion Overses Trnsporttion Assocition (COTA). Assocition (COTA). Keyword: rrivl time prediction,utomtic vehicle loction, utomtic pssengerr counter,dwell time, cpcity limits, in-vehicle occupncy *Jing Teng. Tel.: ; fx: E-mil tengjing@tongji.edu.cn The Authors. Published by Elsevier Ltd. Open ccess under CC BY-NC-ND license. Selection nd peer-review under responsibility of Chinese Overses Trnsporttion Assocition (COTA). doi: 0.06/j.sbspro

2 330 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) Introduction Mny countries hve been fcing incresing chllenges in terms of trffic congestion nd people re encourged to dopt public rther thn privte trnsporttion, thus helping in relieving congestion nd ssocited problems. Severl mesures hve been ttempted ll over the world, including Chin, to me public trnsport bus services more ttrctive to the community. One such mesure is to provide ccurte bus rrivl informtion to users pre-trip nd in bus stops to minimize wit time. Therefore, growing interest hs been developing in bus rrivl time/trvel time prediction. Since dwell time tes up significnt frction of the trip time long serviced bus line, vrition of dwell time my lrgely ffect the ccurcy of trvel time prediction. Most of the studies in the re of bus trvel time prediction include bus dwell time implicitly in the lin trvel time. Indeed, running time in lins nd dwell time t stops re ffected by different issues. Recently, the new pproch is to divide the bus trvel time into two components-running times nd dwell time t bus stops nd nlysis ech seprtely. Bus dwell time Estimtion nd Prediction s the bse of bus trvel time prediction, cn lso be used for the ppliction of stop-bsed control strtegies. According the dwell time prediction, nd the current trffic conditions nd bus hedwy, the control strtegy in the sttion cn be selected nd djusted ccurtely. However, dynmic dwell time prediction is chllenging ts, since there re so mny fctors contributing to dwell time. The Trnsit Cpcity nd Qulity of Service Mnul defined bus dwell time s the durtion of time of the trnsit vehicle stopped for serving pssengers. It includes the totl pssenger bording nd lighting times nd the time needed for the bus to open nd close doors. As to specific bus, the door opening time nd closing times re generlly fixed, bording nd lighting times my vry in different condition; therefore, the number of bording nd lighting t bus stops re liely the most significnt fctors cusing dwell time vritions. Fctors contributing to dwell time lso include the configurtion nd occupncy of the bus, the method of fre collection, service mode. In fct, things become little different in chin. Compred with the developed countries, the high popultion density in the urbn city leds to high pssenger demnd for trnsit. In the pe hours, the phenomenon tht vehicles re so crowd tht people cn t get on nd off esily even sometimes pssengers hve to wit for the next bus since there is no room for one more. All these conditions tht often occur in Chin, my cuse the lrge dwell time prediction error to gret extent. And the chrcteristics of pssengers in chin re quite different from the other countries. In other spect, due to Automtic Vehicle Loction (AVL) nd Automtic Pssengers Counters (APC) systems hve been incresingly implemented for trnsit opertion, vst mount of potentilly rel time dt could be obtined from these systems. These me the dynmic dwell time prediction possible in the complex conditions. Literture Review Historiclly, vrious methods, such s historic nd rel-time pproches, mchine lerning techniques (rtificil neurl networ, support vector mchines), model bsed pproches (Klmn filtering) nd sttisticl methods (regression nlysis, time-series), hve been dopted in the prediction of bus rrivl time. It cn be seen tht, no mtter wht methods re introduced, in the most of the studies the dt of trvel time insted of dwell time t stops nd lin running time ws used. It mens tht dwell time is included in trvel time. However, running time in lins nd dwell time t stops re ffected by different issues. Although, the literture vilble on trvel time or rrivl time prediction ting dwell time into ccount is exiguous before 2003.In recent yers, few study re trying to predict rrivl time through seprting lin running time nd dwell time. Shlby nd Frhn (2003) used dt collected with utomtic vehicle loction (AVL) nd utomtic pssenger counters (APC) for the prediction of bus rrivl time. They developed model for bus rrivl prediction which consisted of two Klmn filters, one for predicting the trvel time nd the other for predicting the dwell time s function of the number of pssengers lighting nd bording the bus t ech bus stop. Pdmnbn nd Vnjshi (2003) tried to explicitly incorporte the dwell time ssocited with the totl

3 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) trvel times of the buses under heterogeneous trffic conditions. In these reserches, estimtion models of running time nd dwell time model were estblished respectively. But only the number of pssengers lighting nd bording ws ten into ccount in the dwell time prediction. In fct, dwell time is ffected by mny issues. In TCRP Report 00, the fctors tht ffect dwell time consist of pssenger bording nd lighting volumes, fre pyment method, in-vehicle circultion, nd stop spcing. Further studies find out more fctors, such s the vehicle type, time of dy, service type, stop loction, wether condition nd pssenger behvior lso hve gret contribution to dwell time. Among these fctors, the pssenger demnd fctor is greed to be the principl determinnt of dwell time nd ws nlyzed most. In the most study, it is proportionl to pssenger bording or lighting volume, or both. Levinson(983) developed liner regression model for dwell time estimtion with the totl number of bording nd lighting pssengers. Guenthner nd Sinh (983) developed nturl logrithm model for dwell time estimtion using the sum of bording nd lighting pssengers s vrible. Kittelson nd Assocites estblished multivrite liner regression model considering bording nd lighting pssengers s seprte vribles. Shlby nd Frhn (2003) ssumed tht bording pssengers t ech bus stop hve more significnt effect thn lighting pssengers on bus dwell time t tht stop, nd the model re only reltive with bording pssengers. Different countries hve different trffic fetures. Emilio G. Moreno González (202) proposed bus dwelltime model contins the influence of occsionl incidents in the bording process in Mdrid, Spin. And Ahilesh Koppineni developed bus rrivl time prediction system prototype for the specil trffic conditions in Indin, such s bus bre down, congestion, overting, trffic jm, brupt stoppge of services nd unscheduled chnges in routes. Very few bus rrivl predictions hve been crried out under Chin trffic conditions. Compred with the developed countries, the high popultion density in the urbn city nd the unstble pssenger flow cuse tht in the pe hour the demnd exceeds the cpcity, high crowdedness level in vehicle extends the bording nd lighting time, nd the cpcity limits the number of bording pssengers. Thus, there is need for models tht cn cpture the specil effect with little dt requirement. This pper is im t using AVL nd APC dt to estimte the dwell time in the rrivl time prediction in chin. 2. Dt Collection The dt used for this study were collected from bus line Jiding 3 in Shnghi, Chin. The route length is pproximtely 6.4 m, spnning 7 bus stops in ech direction, 4 of which re locted t points of high pssenger demnd. And it hs high index of occuption, with demnd of more thn 600 px/h in ech direction in rush hour. All of the buses in line Jiding 3 re equipped with AVL but without APC. So the dt of pssengers were collected mnully using on-bord counting from 4:00 PM to 6:00 PM on April.7, April 4 nd April 2, which re three successive Fridy in 202, with the records of 72 trips. The vehicles on this route re high-floor buses nd hve 22 set nd two doors the rer door is for lighting only, while bording t the front door re permitted. Automtic fre collection system is used in the route, nd pssenger cn py by csh or IC crd. 3. Model Development As discussed lredy, most of the existing trvel time prediction models include bus dwell time implicitly in the lin trvel time. The pproch presented here is to divide the totl trvel time of bus into two components lin running time nd dwell time t bus stops. Only bus dwell times re modeled in this study. So it is ssumed tht rel-time informtion on pssenger bording nd lighting t bus stops nd bus rrivl nd deprture times is nown from AVL nd APC systems, nd lin running time prediction is ccurte. The pssenger bording nd lighting time is the min prt of dwell time. In order to predict the dwell time, the pssenger bording nd lighting number should be predicted first. The model consists of two seprte prts, pssenger bording nd lighting prediction lgorithm nd bus dwell time estimtion lgorithm.

4 332 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) In order to better understnd the prediction-modeling frmewor, the process of bus dwell time prediction is + illustrted s follow: When bus + leves the stop n, the deprture time T n is nown from the AVL system. At,D this instnt, the next lin running time stop n+( + T n +,A + T n +,D cn be determined. + RT nn, ) will be predicted. Subsequently, nd the predicted rrivl time of the bus t the downstrem bus + DT will be estimted by the bus dwell time model, nd DT RT nn, DT n + + DT n RT nn, + DT n T n,a T n,d T n T T +,A n +,D n,a T n,d T n +,A T n+,d Fig.. Illustrtion of bus opertion (buses from the sme route) 3.. Pssenger bording nd lighting prediction lgorithm The first lgorithm is Pssenger bording nd lighting Prediction Algorithm which mes use of the historicl dt nd the informtion of previous bus on the current dy to predict the bording nd lighting Pssengers. In the pe hour, some pssengers cn t bord the bus, since lc of enough spce in the vehicle, ws observed in the study. The cpcity of buses hs been considered in this lgorithm. The bording pssengers cn be predicted by the following expression: Pb (,, ) n n PTn A Tn A R () Pb : predicted bording pssengers for bus (+) t stop ( n+) : predicted pssenger rrivl rte t stop (n+) PT, A: predicted rrivl time of bus (+) t stop (n+) T, A: ctul rrivl time of bus t stop (n+) PT T : predicted hedwy for bus t stop n+, A, A R : predicted remin pssengers fter deprture of bus from stop n+ The number of in-vehicle pssengers cn be clculted s: N Nn b (2) N : number of in-vehicle pssengers for bus fter deprture from stop n+ n N n : number of in-vehicle pssengers for bus fter deprture from stop n b : ctul bording pssengers for bus t stop n+ n

5 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) : ctul lighting pssengers for bus t stop n+ Here, the prmeter S is introduced in the model to indicte whether the cpcity is reched.it is defined s: S C N (3) S : sturtion of cpcity for bus fter deprture from stop n+,if Sn 0,it presented tht the number of pssengers in the bus reched the cpcity; N : number of in-vehicle pssengers for bus fter deprture from stop n+; C : Mximum number of pssengers cn be in the bus ; If pssengers exceed the cpcity of vehicle, there re remin pssengers tht hve to wit for the next bus. The number of remin pssengers cn clculte s: Pb b, Sn 0 Rn (4) 0, Sn 0 R : predicted remin pssengers fter deprture of bus from stop (n+); Pb : predicted bording pssengers for bus(+) t stop( n+); b : ctul bording pssengers for bus t stop (n+); S : sturtion of cpcity for bus fter deprture from stop (n+); The number of the lighting pssengers cn be estimted by pssengers in-vehicle: P N (5) P : predicted lighting pssengers for bus(+) t stop( n+) N : number of in-vehicle pssengers for bus fter deprture from stop n+ n : the predicted percentge of pssengers light t stop( n+) And the prmeter is clculted by historic dt s: = n n m (6) i i : the percentge of the lighting pssengers in stop i tes up in history dt sttistic ; m: the totl number of stops in the bus line ; 3.2. Bus dwell time estimtion lgorithm Trditionlly, the dwell time hs been described s liner function of the number of bording nd lighting pssengers, ffected by certin prmeters tht represent the speed of entry nd exit, plus ded time for opening nd closing doors. Severl functionl forms hve been suggested. A well-nown Americn model is the Highwy Cpcity Mnul (HCM) (2) nd Trnsit Cpcity nd Qulity of Service Mnul (TCQSM) formuls for the dwell time (3): t d=pt +Pbt b+t oc (7)

6 334 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) t :the dwell time in the bus stop; d P : lighting pssengers per bus through the busiest door (p); t : lighting pssenger service time (s/p); P : bording pssengers per bus through the busiest door (p); b t : bording pssenger service time (s/p); b t oc : the time for opening nd closing doors(s). The Europen experience strted with the wor of Pretty nd Russel (4) tht proposed the following dwell time model. T :the dwell time in the bus stop; i : the time ech pssenger tes for lighting; b : the time ech pssenger tes for bording; j m n T C mx ; b (8) i i j m: the number of lighting pssengers; n : the number of bording pssengers; C: the ded time for opening nd closing doors. In Chin, mnul collection nd Automtic fre collection re dopted. Usully, in the bus pyment system dopt mnul collection on-bord, which pssengers re permitted bord nd light through ll the doors of the bus, Eqution 7 is suitble for the service. Automtic fre collection device (pssengers cn py fre by csh or IC crd) usully instlled in the front door of bus, so pssenger cn only bord through the front door nd light by the rer door. Eqution 8 ssumes tht bording nd lighting pssenger flows re distinct for the vehicles. In this condition tht pssengers cn only bord through one door nd light through the other door. So Eqution 8 is pplicble in the Automtic fre collection. In the front sitution, the dwell time cn be estimted by the following expression: DT tb Pb t P tn (9) In the ltter sitution, the dwell time cn be predicted by the following expression: DT (, n MAX tn Pbn tb P) tn (0) In fct, crowding inside the bus will impct pssenger ctivity when the pssengers on bord (cusing the crowded sitution) re on the bus upon rrivl nd deprture from the stop. The crowding effect inside the bus is considered in this model. Usully, the more crowd in the bus, longer the verge lighting nd bording time per pssenger is. In order to nlysis the crowding in the bus impct on lighting time per pssenger nd bording time per pssenger, this pper defines the crowding rte s follow: ( N S+ )/( C+ S +) () S + : the crowding rte in the vehicle + t the stop n+; :the number of set in the vehicle +; Here, time correction item is introduced to represent the impct of the crowdedness: j

7 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) tb :verge bording time for the vehicle + t stop n+; n tb tb m ( - ), - 0 b b b tb, - b 0 t t m -, - 0 t, - 0 tb : verge bording time per pssenger : the crowding rte begins to ffect pssengers bording time b m : bording compenstion coefficient; b t : verge lighting time for the vehicle + t stop n+; t :verge lighting time per pssenger; m :lighting compenstion coefficient; :the crowding rte begins to ffect pssengers lighting time (2) (3) 4. Model Performnce Evlution In order to ssess the predictive performnce of the bus dwell time model, it is compred with two previously developed models for the sme route. Model A which only tes bording pssengers into ccount is: DT tn+ + tb n+( PT, A T, A) The Model B considers the effect of both bording nd lighting pssengers is s the following form:. DT tn+ +mx( tb n+( PT, A T, A), t Nn )) The Model C is the one proposed in this pper, which considers the effect of crowdedness in vehicles nd the cpcity limits. As mentioned erlier, the AVL nd APC dt for the study route were vilble for 3 dys. The three models were clibrted with using dt of 2 dys only, with the third dy s dt held out for performnce evlution. The prmeters re show in the tble.and rges of vlues for the prmeters observed in shnghi re: Ded time:3.0 to 2.0 seconds Alighting time:.0 to 3.0 seconds per pssenger Bording time:.6 to 8.0 seconds per pssenger Averge lighting time:.3 Averge bording time:.88 Averge ded time:4.6 The verge bording time begn to increse significntly fter >0.3 while lighting time strt to sor up >0.4. Tble. The prmeters of the models Sttion NO n pss/s n tb s t s tn s C 60

8 336 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) S mb m b Four testing dt sets were used. The first set includes ll test dt, while the dt re divided into three ctegories, corresponding to three different in-vehicle conditions: non-crowding, crowding nd exceeding cpcity. After clcultion, ll the necessry dt required for model testing ws extrcted nd nlysed. Three

9 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) prediction error mesurements were computed for ll developed models to test the model performnce. These error indices include: Men bsolute error ( men ), which indictes the expected error s frction of the mesurement men = X true t X pred t N t Root men squred error ( rs ), which cptures lrge prediction errors 2 rs X true t X pred t N t Mximum bsolute error ( mx ), which cpture the mximum prediction error mx mx X true t X pred t N: the number of smples; X true (t): mesured vlue t time t; X pred (t) :predicted vlue t time t; Here bsolute error insted of reltive error re used to indicte the performnce, since the predicted vlue of dwell time is used to clculte the rrivl time or trvel time, bsolute error cn reflect the effect of ccurcy in the rrivl time/trvel time prediction directly. Sometimes reltive error is lrge, while bsolute error is low, it cn ffect the ccurcy of rrivl time/trvel time prediction little. Three bsolute error indictors were selected for evlution of performnce Tble 2 shows the three error mesures men, rs, mx for the test dt, while Figure 2 (,b,c) summrize the performnce of the three prediction models for ech condition. Obviously, the lower the error is, the better the model performnce is. 5. Anlysis nd Results The results summrized in Tble 3, it shows tht for ll the conditions the model C provides the minimum vlue for the error mesures men, rs, mx pointing to the fct tht its performnce ws the best compred with the other models, except for the uncrowned condition where model B nd C hve the sme performnce. Tble 2 nd Figure 2 (,b,c,d) show there is no significnt difference in the performnce of the three models for the non-crowding scenrio, but In generl, the model C lwys gives lower vlue for the bsolute error indices nd shows the best prediction performnce in ll three conditions. In the non-crowding scenrio, model C ws lmost the sme s model B, there were no difference in the results however, it showed superior performnce to the other models in the crowding nd the exceeding cpcity scenrios, the vlue of rs which reflect lrge prediction errors nd mx which cpture the mximum prediction error were much lower thn model B nd A. It mens tht in the ltter two cses, the lrge errors cn be reduced effectively. And in fct, lrge errors lwys ppered in the ltter two conditions. Due to the uncertin behviour of the pssengers nd driver, the dwell time forecsting cn t be so ccurte. However, lrge errors my lrgely ffect the rrivl time/trvel time prediction. Reducing lrge errors cn effectively improve the precision of prediction. And in the most conditions, compred with A, the model B hd better performnce. It shows tht ting both lighting nd bording pssengers into ccount mes contribute to improve the ccurcy. In word, performnce of the model proposed in this pper ws similr to the model B in the condition without crowding in vehicle, but it showed superior performnce to the other models in the crowding nd the cpcity limits scenrios. These results showed the superior performnce of the model C compred with other prediction models in terms of the bsolute error, nd it lso demonstrtes how this model cn cpture dynmic chnges due to different bus opertion chrcteristics t stops.

10 338 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) Tble 2. Absolute error results of the prediction models All Non-crowding Crowding Exceeding cpcity model (360 Predictive vlues (207 Predictive vlues) (4 Predictive vlues) (2 Predictive vlues men rt mx men rt mx men rt mx men rt mx A B C () (b) (c) (d) Fig. 2. Absolute error results of the prediction models 6. Conclusion Trvel time/rrivl time prediction systems re predominntly found in mny countries for mny yers nd re bsed on historic dt bse or trvel time ptterns. One of the min chllenges involved in bus trvel time or rrivl time prediction. Most of the reported studies in the re of bus trvel time prediction, the new pproch recently is to divide the totl trvel time into two components - running time nd dwell time t bus stops, nd nlysis them seprtely. However, the studies before don t te crowdedness nd cpcity limits into ccount, which re the mjor fetures in Chin. This pper proposes the model tht is suitble in the conditions here.

11 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) In conclusion, the models proposed before to predict bus dwell time nd models proposed in this reserch cn both predict the dwell time with cceptble err. However, inclusion of other fctors lie service type, cpcity limits, fre collection methods, crowdedness in-vehicle, will increse the Accurcy of the bus dwell time prediction. The model C includes these fctors cn provide better estimtes in the high in-vehicle occupncy condition, which conforms to the sitution of Chin's urbn trnsit. However, clibrtion of the model C would be required to determine the model prmeters to suit the existing condition.so the model B, which hs less prmeter, is better in the line tht seldom with high occupncy in the vehicle. Since the model for this reserch ws developed bsed on AVL nd APC dt, lc of APC counter in the bus, the dt re ll collected by mnul. Indeed, mnul collected dt nd APC dt re not totlly sme, tht my ffect the result of the prediction. And for engineering prctice the usefulness of the dwell time model, the dynmic rel-time testing with AVL nd APC dt, which hve become criticl issues in Chin, is necessry. Becuse dwell time is predicted seprtely nd its effect on bus rrivl times t downstrem stops is ccounted for, the model lso cn be used for ssessing trnsit stop-bsed dynmic control ctions. The dwell time model, only prt of the rrivl time prediction models, developed bsed on dt from one bus route in Shnghi. Some more different lines need be tested to increse the Relibility of the model. Further wor cn improve the model developed here in severl wys. Better representtive distributions of pssenger rrivls t bus stops could be ttempted insted of the implied uniform distribution. Wht s more, ccording to the chrcteristic of different sttions, the prmeters of sttions cn be set seprtely. And in the observtion, other fctors such s bunching phenomenon cn ffect the dwell time. The modify items for these fctors cn be dded in the model in the future reserch. Acnowledgements This reserch ws supported by the Ntionl Nturl Science Foundtion of Chin (Grnt No ) nd the Fundmentl Reserch Funds for the Centrl Universities. The uthors will lso owe their gret pprecition to Advnced Public Trnsit System lbortory of Tongji University for ssistnt in dt collection. References Highwy Cpcity Mnul (2000). Trnsporttion Reserch Bord of the Ntionl Reserch Council, Wshington D.C. Shlby nd A.Frhn (2003). Bus trvel time prediction for dynmic opertions control nd pssenger informtion systems. Trnsporttion Reserch Bord, the 82nd Annul Meeting (CD-ROM), Wshington, D. C. R.P.s. Pdmnbn, Lelith Vnjshi, nd Shnr C.Subrmnin (2009). Estimtion of Bus Trvel Time Incorporting Dwell Time for APTS Applictions. IEEE Intelligent Vehicles Symposium, Proceedings, p TCRP Report 00 (2003). Trnsit Cpcity nd Qulity of Service Mnul (2nd Edition). Trnsporttion Reserch Bord of the Ntionl Acdemies, Wshington D.C. Dueer,K.J.,T.J.Kimpel, nd J.G..Strthmn (2004). Determinnts of Bus Dwell Time. Journl of Public Trnsporttion, 7, Rjbhndri,R.,S.I.Chien,nd J.R. Dniel (2003). Estimtion of Bus Dwell Times with Automtic Pssenger Counter Informtion. Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord, 84, Mrtin, M. (2008). Modeling the Fctors Affecting Bus Stop Dwell Time: Use of Automtic Pssenger Counting, Automtic Fre Counting, nd Automtic Vehicle Loction Dt. Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord, 2072, Levinson, H.S. (983). Anlyzing Trnsit Trvel Time Performnce. In Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord, 95, -6. Shlby, A., nd A.Frhn (2004). Prediction Model of Bus Arrivl nd Deprture Times Using AVL nd APC Dt. Journl of Public Trnsporttion, 7, 4-6. Guenthner,R.P.,nd K.C.Sinh (983). Modeling bus delys due to pssenger bordings nd lightings. In Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord,95, 7-3. Kittelson nd Assocites. Trnsit Cpcity nd Qulity of Service Mnul 2nd Edition. Trnsporttion Reserch Bord, 6-7.

12 340 Cen Zhng nd Jing Teng / Procedi - Socil nd Behviorl Sciences 96 ( 203 ) Emilio G. Moreno González, Mnuel G.nd Romn Oscr Mrtínez Álvro (202). Bus dwell-time model t min urbn route stops: A study cse in Mdrid-Spin. Trnsporttion Reserch Bord, the 9nd Annul Meeting (CD-ROM), Wshington, D. C. Zogrfos, K.G., nd H.S.Levinson (986). Pssenger Service Times for No-Fre Bus System. Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord, 05, Koppineni, Chitny, Siddhrth nd Vnjshi (202). Development of n Automted Bus Arrivl Time Prediction System under Indin Trffic Conditions. Trnsporttion Reserch Bord, the 9nd Annul Meeting (CD-ROM), Wshington, D. C. Lin, T.,nd N.H.M. Wilson (993). Dwell Time Reltionships for Light Ril Systems. Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord, 36, Fricer, J.D. (20). Bus dwell time nlysis using on-bord video. Trnsporttion Reserch Bord 90th Annul Meeting CD-ROM, Trnsporttion Reserch Bord of the Ntionl Acdemies, Wshington, D.C. Mrshll, L.F., H.S. Levinson, L.C. Lennon,nd J. Cheng (990). Bus Service Times nd Cpcities in Mnhttn. Trnsporttion Reserch Record: Journl of the Trnsporttion Reserch Bord, 266, Jiswl,S., J. Buner, nd L. Ferreir (200). Influence of Pltform Wling on BRT Sttion Bus Dwell Time Estimtion: Austrlin Anlysis. Journl of Trnsporttion Engineering, 36,