Assessing Interchange Effects in Public Transport: A Case Study of South East Queensland, Australia

Transcription

1 Aeing Interchange Effect in Public Tranport: A Cae Study of South Eat Queenland, Autralia Author Yen, Barbara, Teng, Chun, Mulley, Corinne, Chiou, Yu-Chiun, Burke, Matthew Publihed 2017 Conference Title Tranportation Reearch Procedia: World Conference on Tranport Reearch - WCTR 2016 Shanghai Verion Publihed DOI Copyright Statement 2017 The Author. Publihed by Elevier B.V. Thi i an Open Acce article ditributed under the term of the Creative Common Attribution-NonCommercial-NoDeriv 4.0 International (CC BY-NC-ND 4.0) Licene ( which permit unretricted, non-commercial ue, ditribution and reproduction in any medium, providing that the work i properly cited. Downloaded from Griffith Reearch Online

2 Available online at ScienceDirect Tranportation Reearch Procedia 25C (2017) World Conference on Tranport Reearch - WCTR 2016 Shanghai July 2016 Aeing Interchange Effect in Public Tranport: A Cae Study of South Eat Queenland, Autralia Barbara T.H. Yen a, *, Wen-Chun Teng b, Corinne Mulley c, Yu-Chiun Chiou d, Matthew Burke a a Urban Reearch Program, Griffith Univerity, 170 Keel Road, Nathan, Bribane, Queenland 4111, Autralia; b School of Engineering, Univerity of South Autralia; c Intitute of Tranport and Logitic Studie; The Univerity of Sydney, Autralia; d Department of Tranportation and Logitic Management, National Chiao Tung Univerity, Taiwan, ROC. Abtract Interchange or tranfer for paenger in large multimodal public tranport network are more or le inevitable. A zone baed fare ytem ha the potential to enure that there i no financial penalty for interchange. In South Eat Queenland (SEQ), Autralia, there i a zone baed fare ytem in place which doe not penalize tranfer within the ame zone but doe charge a full fare for an inter-zone tranfer in a ingle journey. Thi reearch invetigate the interchange effect from an analyi of paenger travel pattern uing the mart card data from the automated fare collection ytem in place in SEQ. Latent cla neted logit model are etimated with ocial demographic characteritic to meaure tranfer behaviour and are ued to invetigate the opportunity for better interchange policie to increae the network effect in the SEQ network. The reult identified paenger' heterogeneou preference toward travel alternative with markedly different market egment. The empirical reult identified paenger categoried into four egment of employee, tudent, wealthier people and enior. The finding ugget that public tranport network effect are mot important to the employee egment with tudent and enior egment being more likely to chooe direct alternative over alternative involving interchange. In order to enhance the public tranport network effect, two policie to encourage tranfer by paenger are invetigated uing imulation with the policy implication identified The Author. Publihed by Elevier B.V. Peer-review under reponibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Keyword:tranfer fare policy, mart card data, travel behaviour, tranfer behaviour; network effect Correponding author. Tel.: addre: t.yen@griffith.edu.au The Author. Publihed by Elevier B.V. Peer-review under reponibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY /j.trpro

3 4024 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Introduction Tranfer in large multimodal public tranport network are almot inevitable (Vuchic, 2006). One or more tranfer are made for trip in many of the major citie around the world: 30% of trip in London, 80% in New York, 70% in Munich, 40% in Pari and 50% in Melbourne(NYMTC, 1998; Tranport for London, 2001; GUIDE, 2000; Currie and Loader, 2010; Guoand Wilon, 2011). However, public tranport uer often link tranfer with inconvenience. Inconvenient interchange can dirupt paenger travel giving a negative travel experience and reduce public tranport' competitivene a compared to the car which provide a door-to-door ervice (Guo and Wilon, 2011). For example, paenger, particularly commuter and buine uer, elect the fatet and mot direct route for their journey (Conquet Reearch, 1977; Hine and Scott, 2000). An aement of public tranport tranfer option and uggetion for the improvement of interchange within large multimodal network can not only improve the quality of public tranport but will enlarge it network effect. Tranfer are a fundamental iue in large multi-modal ytem, but are largely overlooked in public tranport planning (Guo and Wilon, 2011). Often, a zonal baed fare ytem i introduced to mitigate the impact on the uer of having to interchange but little tudy ha been made of how paenger behave in repect of intra-zonal veru inter-zonal tranfer within an urban area where thee tranfer are treated differently by the fare ytem. Thi paper addree thi under developed reearch area to examine the current tranfer behaviour and the impact of the fare ytem on tranfer behaviour uing South Eat Queenland (SEQ), Autralia, a the cae tudy. A zone baed fare ytem i adopted in SEQ) uch that there i no penalty for a tranfer within the ame zone, but a full fee i charged for an inter-zone tranfer within a ingle journey with no eparate tranfer policy to encourage or integrate inter/intra-mode tranfer in the zone baed ytem. In many countrie, a tranfer dicount policy ha been hown to have poitive effect on increaing public tranport uage. For example, in Taiwan a different ticketing regime i in place. A dicount of NT$8 (about US$0.25 which i equal to 50% dicount of the tranfer trip) i provided to each bu tranit uer tranferring to or from the Taipei metro. Thi tranfer dicount ha ignificantly raied commuter ue of both the metro and the bu ytem. A motivation of thi paper i to undertand tranfer behaviour with a view to examining whether a pecific tranfer policy could increae public tranport uage in SEQ. The paper i tructured a follow. The next ection provide the literature context to tranfer behaviour and thi i followed by a decription of the SEQ tranport tudy area together with a ummary of the current fare ytem. The, methodology and data ued in the paper are then decribed. The penultimate ection provide the reult together with interpretation. The paper conclude with a dicuion and uggetion for further reearch. 2. The Literature context The aement of interchange can be conidered from the operator or from the paenger perpective. There are many tudie on public tranport tranfer from an operator' perpective, including inter-modal tranfer facility deign (Horowitz and Thompon, 1995; Smart et al., 2009; Hoeven et al., 2014; Harmer et al., 2014), location for tranfer (Clever, 1997; Vaallo et al, 2012), unreliability (Abkowitz et al., 1987; Carey, 1994; Rietveld et al., 2001), network acceibility (Hine and Scott, 2000; Shafahi and Khani, 2010; Currie and Loader, 2010), and public tranport coverage (Murray, 2001). Thi literature i extenive but ha in common the tendency to treat paenger a if they are a homogeneou egment without conideration of the paenger' trip characteritic in evaluating tranfer effect. The trip characteritic (e.g. travel time, travel cot, tranfer waiting and walking time, tranfer information, fare, afety and comfort, etc.) are identified to be the mot ignificant factor for paenger in electing travel with tranfer or not (Atkin, 1990; Callaghan and Vincent, 2007; Ieki and Taylor, 2009; Chowdhury and Ceder, 2013; Chowdhury et al. 2015) and hence it would appear enible to take thee account in tudying interchange behaviour. Although the travel behaviour literature ha looked at the impact on uer of ome apect of trip apect uch a the impact of tranfer penaltie on the value of time in travel (Ieki and Taylor, 2009; Guo and Wilon, 2011; Chowdhury et al., 2015). In addition Ieki and Taylor (2009) included a tranfer penalty a part of the traveller total generalized cot of travel by claifying the mot important uer' factor, including tranfer cot, time cheduling and tranfer facility attribute (i.e. acce; connection and reliability; information; amenitie; and ecurity

4 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) and afety). Guo and Wilon (2011) extended thi by including tranfer cot baed on both the operator' ervice upply and the cutomer' perception. Chowdhury and Ceder (2013) conducted a urvey to undertand paenger' perception on defined planned tranfer that conit of five attribute (network integration, integrated time-tranfer, integrated phyical connection of tranfer, information integration, and fare and ticketing integration).they found that public tranport uer' willingne to ue tranfer route increae if a better connection between public tranport mode i provided. Travel time ha been found to be more ignificant than waiting and walking time in tranfer, epecially for commuter by a number of tudie (Vande Walle and Steenberghen 2006; Xumei et al. 2011). Thi i enhanced by the reult of Chowdhury et al. (2015) who explored commuter' perception of tranfer uing the two trip attribute of travel time and cot in New Zealand and found that for more comfortable interchange, uer' valued thee a a 25 % reduction in travel time and a 10 % reduction in travel cot. Other tudie have hown that tranfer waiting time i valued more highly than tranfer walking time (Vande Walle and Steenberghen 2006; Ieki and Taylor 2009). The evidence i therefore mixed on tranfer valuation. In the field of travel behaviour much reearch ha ued traditional travel urvey to capture current and potential change in travel behaviour (for example, Meyer, 1999; Garling et al., 2002; Henher and Puckett, 2007). However, the major challenge for travel urvey i the validity of the urvey (doe the urvey itelf change traveller behaviour?, how do we account for differing repone rate?, doe different coding affect the reult?) Quetionnaire ha been hown to make very big difference and there i alway the difficult iue of whether or not participant are elf-elected in the recruitment proce with the conequential introduction of bia (Stopher et al., 2007). Therefore, thi tudy utilize an alternative data reource, public tranport mart card record, to preciely capture paenger' travel pattern. Many recent tudie have ued mart card data to evaluate public tranport behaviour and ha been hown to be a reliable ource (Blythe, 2004; Bagchi and White, 2005; Trepanier and Morency, 2010), travel behaviour (Bagchi and White, 2004; Seaborn et al., 2009; Munizaga et al., 2010), operational performance (Morency et al., 2007), and fare policie (Pelletier et al., 2011). Smart card typically provide more limited data than a quetionnaire, for example it i rare to find information on trip purpoe (Bagchi and White, 2004), but mart card data have the advantage of providing continuou trip data covering longer time period thu providing the opportunity of evaluating tranfer effect with accuracy. Reearch ha hown that travel behaviour i affected by a combination of intrumental, ituational and peronal factor and that thee will differ for ditinct group of people (Anable, 2005). In order to account for the heterogeneou preference of uer, market egmentation hould be introduced into travel behaviour analyi (Hair et al. 1998; Wedel and Kamakara 1998; Paraki and Abacumki, 2002; Anable 2005; Wen et al., 2012). The major purpoe of market egmentation i to group different type of people who hare well defined characteritic into a manageable number of group for analyi. In the previou reearch, different egment-pecific parameter have been ued, including trip purpoe (Tamboula et al., 1992), a lifetyle variable (e.g. invetment in car mobility) (Bekhor and Elgar, 2007), ocio-economic variable (i.e. houehold income, type of accommodation, dependency factor, and occupation level of commuter) (Ratogi and Rao, 2009; Wen and Lai, 2010; Wen et al. 2012). Thi paper will contribute to literature in two way. Firt, the revealed preference data ued in thi paper, the public tranport mart card tranaction data, ha been adopted to objectively capture paenger' travel pattern. Second, a market egmentation concept ha been introduced to meaure the heterogeneou preference of uer (in thi paper, public tranport paenger). The reult of thi paper alo ugget that the undertaking of a future cae tudy would provide important further information into tranfer behavior which i epecially important for large multi-modal ytem with zonal baed fare ytem. In ummary, the contribution come from the way in which thi paper ue the objective meaure of tranaction data, a identified by the automated mart card fare collection in SEQ to explore the tranfer effect from the paenger' travel pattern with a market egmentation cheme that incorporate parameter to differentiate between ditinct group of uer. Thi allow the etimation of tranfer effect for paenger and the ubequent ue of thee etimation reult to invetigate fare policy around tranfer effect by imulating potential alternative interchange fare policie.

5 4026 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) The SEQ tranport ytem A thi paper conider the tranfer behaviour of public tranport uer in SEQ, thi ection preent a brief outline of the current public tranport mode. The SEQ region of Autralia, which include Bribane, the Sunhine Coat and the Gold Coat, ha merged into a 200 kilometre long city (Spearritt, 2009). SEQ' public tranport ytem i made up of a network of train, tram, bue and ferrie. Figure 1 how the public tranport ervice network in SEQ a of January Figure 1 SEQ' public tranport network (Source: Autralian Rail Map)

6 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Train SEQ' rail network of over 200 kilometre connect to the Sunhine Coat and Gold Coat to Bribane. Operated by a diviion of the Queenland Government, the CityRail network ha relatively low riderhip if conidered on a world cale. The CityRail network ha 11 line and 214, motly 3-car, vehicle in ue (Soltani et al., 2015). Service frequencie are 30 minute off-peak in the outer uburb and 15minute (or le) in the inner uburb. The train network i entirely radial with eight line connecting in the central buine ditrict (CBD). Though there ha been fare-free tranfer on the whole public tranport network ince 2004, very few tation are erviced by feeder bue, epecially in the Bribane City Council juridiction where it bue motly head toward the buway and then onward to the CBD. Bu The bu network i radial and CBD-oriented. Many of the bu ervice run on buway that are recognied a being one of the mot ucceful Bu Rapid Tranit (BRT) ytem in the developed world. Bribane' BRT ytem deliver fat, comfortable, and cot-effective urban mobility through the proviion of egregated right of-way infratructure, rapid and frequent operation, and excellence in marketing and cutomer ervice (Golotta and Henher, 2008). Many of the bu ervice run on dedicated buway that Hoffman (2008) decribed a Quick-way a they are fully egregated from other traffic, with average top pacing of more than one kilometer. Ferry The ferry network i a linear paenger ferry ytem that provide relatively frequent ervice along river or parallel to horeline, ervicing multiple top (Thompon et al., 2006; Tai et al., 2014). The Bribane CityCat, CityHopper and CityFerrie combine to form a ferry ytem with 24 terminal. Tram The tram network, the Gold Coat light rail, only ervice a ingle 13-km route with 16 tation and began operation on 20 July The recent opening of thi ervice mean the data and the modelling in the following analyi doe not include tram paenger. TranLink, etablihed in 2004, i a Queenland Government agency which manage public tranport ervice covering Bribane and SEQ. With the exception of taxi, public tranport fare are integrated acro all public tranport mode in the SEQ region. In 2008, a mart card ticketing ytem branded the Go-card wa introduced, which allowed paenger to travel on all TranLink bu, train, ferry and tram ervice (Yen et al. 2015). The Go-card ytem i a region-wide, zone-baed cheme in which paenger ue one card for all public tranport mode. Figure 2 how the TranLink South Eat Queenland ervice area and fare zone. Fare are automatically collected baed on the zone travelled. All Go-card paenger mut tag on when boarding and tag off when alighting from bue or ferrie, or when entering and leaving a train or tram tation. Go-card reader are intalled on-board or on platform. A uch, the Go-card provide origin and detination data record for each tranaction (Soltani et al., 2015; Yen et al., 2015). Four type of Go-card are in ue: adult, child, enior and conceion the latter three type have a 50% fare reduction of the full adult fare.

7 4028 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Figure 2 TranLink South Eat Queenland ervice area and zone (Source: TranLink) 4. Data acquiition and methodology 4.1. Data In order to explore public tranport interchange behaviour, thi tudy examine difference in behaviour by categorizing paenger into different group with pecific uer characteritic, i.e. young paenger (e.g. tudent), employed people (e.g. worker), high-income group, etc. A one month cro-ectional lice of Go-card (mart card)

8 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) fare tranaction data i ued in combination with a et of neighbourhood variable derived from 2011 cenu data and ued to capture ocial-demographic characteritic. The one month (March 2013) 1 cro-ectional lice of Gocard tranaction wa provided by TranLink and ha approximately 15 million data point. After cleaning the data, 15.7% of data record were removed due to inconitent, miing or unuable data (i.e. trip where the paenger failed to tag-off their Go-card or tagged on and off quickly at the ame place) and then due to oftware contraint, the ample wa reduced to a total of 25,842 trip baed ample 2 randomly drawn from uer ID. The trip characteritic are alo derived from the Go-card data, including the travel zone number(); travel cot 3 ; travel time; whether a week day trip; peak hour trip 4 ; 9 journey trip or commuter trip. In SEQ, a volume rebate policy i adopted o that the network i free to paenger who travel for more than 9 paid journey in a 7 day period, from Monday to Sunday, regardle of zone travelled. Among thee variable, the 9 journey trip i ued to identify the free trip within each travel journey. Commuter trip are identified uing the Go-card ID bae with thoe making a minimum of 24 trip per month are being viewed a commuter with all their trip being treated a commuter trip. To capture the feeder bu ervice to train tation and the level of ervice quality aociated with thi ervice, data i captured uing Geographic Information Sytem (GIS). A feeder bu tation denity within a 2 km buffer of the train tation i calculated and ued a a proxy for the feeder bu ervice level on the bai that higher denitie of feeder bu top will be aociated with a better frequency of feeder ervice. Finally, in order to capture the paenger ocial demographic characteritic, a et of neighbourhood variable i collected from the 2011 cenu (the cloet available cenu data). The cenu data i a decriptive count of everyone who i in Autralia on one particular night, and alo of their dwelling. Thi data i the data for the whole population, not only public tranport paenger. The cenu i collected at the Statitical Area Level 1 (SA1), the mallet geography unit of cenu data. The SA1 ha a population of between 200 and 800 people with an average population of about 400 people. The variable obtained from the cenu data are ued a a proxy for individual ocio-economic characteritic and are ued to etimate the paenger travel mode preference. The ocio-economic characteritic included in the analyi are population denity, employment, percentage of tudent attending chool (i.e. older than 4 year), older people, meaured by the percentage of people older than 65 year), income (percentage of or houehold having an income of more than $1,500 per week), car ownerhip level (percentage of houehold with more than two car in the houehold), and the percentage of people uing public tranport a their major travel mode. Table 1 how the decriptive tatitic of the variable, including minimum, maximum, mean, tandard deviation, kewne, and kurtoi value. Table 1 Summary of decriptive tatitic of variable in the model Variable Unit Min Max Mean Standard Deviation Skewne Kurtoi Trip characteritic Travel time Minute Travel cot Dollar Travel zone Number journey trip Dummy Peak hour trip Dummy Weekday trip Dummy Commuter trip Dummy Public tranport ervice Feeder bu top denity_ Heavy rail Number Social demographic 1 March 2013 i elected due primarily a are no public holiday or chool break during thi month. 2 A ample i ued for computational reaon and to meet the eligibility requirement of tatitic oftware which can only handle 37,507 obervation. The ample contitute approximately 0.21% of the Go-card dataet. 3 Travel cot i derived from the travel zone number() uing the zone baed fare ytem ued in SEQ. 4 Travelling between 3:00am and 9:00am; and 3:30pm and 7:00pm on weekday.

9 4030 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Population denity People/10m Age (>65 year old) %* Student (>4 year old) %* Employment %* Public tranport a main travel mode %* Car number (more than 2) %* High income (>1,500/pw) %* *The percentage here i calculated by SA1 unit. For example, percentage of employment equal to all dependent number divided by the total population for each SA1. Table 1 how travel time, travel cot and travel zone are highly variable and thi i becaue SEQ i a 200 km linear city. The patial characteritic of trip are captured by the travel zone, that i, the zone number() travelled by each cardholder. Travel time i ued to preent the temporal characteritic and it i expected to have a negative impact on utility with longer journey yielding le utility for the paenger. A number of trip characteritic are ued to facilitate policy dicuion, e.g. peak hour trip, weekday trip and commuter trip, the 9 journey trip (capturing the effect of the volume rebate policy with an expected poitive aociation with utilitie). In term of the public tranport ervice variable, the denity of feeder bu ervice to train tation i expected to have a poitive impact on utility a well ince it make travel by train eaier. The ocial demographic variable are included ince they are expected to drive market egmentation Travel alternative choice model A decribed above, the literature identifie that different travel behaviour can be aociated with combination of intrumental, ituational and peronal factor which differ for ditinct group of people (Anable 2005). However, in thi cae we cannot oberve the underlying factor that might egment the market and o latent cla model which allow ubgroup to be identified on the bai of their behaviour are fundamental to thi modelling. More pecifically, a latent cla neted logit (NL) model i ued here a the contructed travel alternative choice can conider the effect of variable and alternative homogeneity that cannot be captured by the impler latent cla MNL modelling. A Figure 3 how, the tandard MNL model aume the ame preference tructure acro individual in addition to the independence of irrelevant alternative (iia) and independent and identically ditributed random variable aumption. In the cae examined here thi tandard MNL model would reult in biaed etimate and incorrect prediction if the upected heterogeneou preference exit (Wen and Lai, 2010). Figure 3 how the difference between an MNL and a neted approach. The latent cla neted logit (NL) modelling ued in thi tudy i an extenion of the latent cla multinomial logit (MNL) model and define the choice probability uing a tandard NL formulation (McFadden, 1978) which allow for the preence of individual preference heterogeneity. It i the individual preference heterogeneity that, according to the literature, i expected in travel behaviour that make thi approach a ound methodology.

10 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Figure 3 The evolution from the MNL to the latent cla MNL and NL approach There are ix travel alternative in thi tudy, including journey uing a bu only; train only; ferry only; bu to bu; bu to train 5 ; and bu to ferry. The latent cla NL model group travel alternative into a net, with incluive value parameter that capture flexible ubtitution pattern. The latent cla NL model ue the utility function a a tarting point, auming that deciion-maker (paenger in thi cae) make rational choice to maximie utility by conidering all travel alternative and electing the one that provide the greatet utility. The latent cla NL model in thi tudy divide the deciion-maker preference into a finite number of egment with homogeneity among the paenger preference within each egment. The probability that a paenger chooe variou travel alternative can be divided into two part: the probability of chooing the travel alternative in a given egment and the probability that a paenger belong to a given egment. Both are expreed by the formula for probability obtained uing a logit model. The probability function of a paenger belonging to a given egment i aumed to be related to the paenger characteritic with the utility function of paenger i belonging to egment for any travel mode m being expreed a: U β X ε (1) im im im where X im i a vector of paenger attribute, β i a vector of unknown egment-pecific parameter, and ε im i the error term that repreent the random part of the utility. If there are m travel mode then the probability that paenger i in egment chooe travel alternative m i expreed by the following equation: exp(β X im ) P i m (2) exp β X m im 5 A trip with a tranfer, including bu to bu, bu to train and bu to ferry trip, i meaured without conideration of the link equence. For example, if a journey conit of a bu tranfer to a train and then a tranfer to a bu, there are three trip within thi journey, bu to train trip, train to train trip, and train to bu trip. All thee trip are viewed a bu to train trip becaue the focu in thi tudy i to meaure tranfer effect only.

11 4032 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) where i the parameter to be etimated in egment and X im i the attribute of paenger i toward travel alternative m. Kamakura et al (1996) identifie for egment, the probability of paenger i chooing travel alternative m in net n i P i ( m n, ), and P i ( n ) repreent the probability that paenger i i in net n. n exp( β X im λn ) P i(m n,) (3) exp( β X λ ) m N n im n exp(λn Γin ) P(n t ) (4) exp(λ Γ ) n n in In each net, n, there are N travel mode. The analyi provide a diimilarity parameter for each net n a n, and logum variable for each net within each egment which i ued for aeing goodne of fit and interpretation. The diimilarity parameter capture the imilaritie between pair of alternative in the net. Similar to the tandard NL model, if the condition 0 < n for all and n hold, the model i conitent with utility maximization for all poible value of the explanatory variable and will not yield counterintuitive reult (Ortúzar and Willumen, 2001; Train, 2003). The latent cla MNL model i a retriction of the latent cla NL model: when all the diimilarity parameter in all egment are equal to one in the latent cla NL model, the latent cla NL model collape to the latent cla MNL model. Moreover, the tandard NL model can be regarded a a pecial cae in which the latent cla NL ha only one egment. One of the benefit of latent cla modelling i that it allow the determination of the number of egment. In thi cae here the number of egment are determined through the ue of the commonly ued performance indicator, uch a the Bayeian information criterion (BIC) and the contrained Akaike information criterion (AIC). The analyi determine the number of egment by tarting with two egment and adding one at a time until the additional egment no longer ignificantly improve the model goodne-of-fit and thi then reflect the optimal number of market egment. In practice, a the number of egment grow, it i le eay to improve the goodne of fit (Kemperman and Timmerman, 2009). The calculation method ued to identify the number of egment i a follow: AIC 2LL( ) 2K (5) BIC 2LL( ) Kln(O) (6) Where LL( ) i the logarithmic likelihood function value, K i the number of parameter and O i the number of ample. Lower value of AIC and BIC ignal better fit. 5. Reult 5.1. Model formulation A identified above, there are 6 different travel alternative in thi tudy. The invetigation centred on three model, contructed a a latent cla MNL model and two alternative latent cla NL model, repectively a hown chematically in Figure 4 which i the reult of invetigating a number of different neted tructure which can be behaviourally interpreted. The MNL model i inappropriate ince it require the independence of error term which i likely to be violated becaue of the way in which it i expected that ocial demographic characteritic and peronal preference might influence individual' alternative election. The latent cla NL model 1 eparate alternative by mode irrepective a to whether interchange i required in contrat to the latent cla model 2 which conit of one net with direct travel alternative, with alternative requiring interchange being included in another net. After analyi, the reult indicate that Model 2 provide the bet model fit with the rejection of the alternative model at the 5% ignificance level, baed on the likelihood ratio tet.

12 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Figure 4 Model tructure for preferred choice model 5.2. Model reult analyi Revealed preference data, a decribed above, are ued to analye paenger preference for travel alternative choice. For the ix travel alternative in thi tudy, the model include five model-pecific contant with the buferry paenger travel alternative being excluded a the reference alternative becaue of their low percentage of market hare (1%). Travel characteritic variable including travel time, travel cot, travel zone, weekday trip, peak hour trip, and commuter trip are pecified a generic variable. The variable 9 journey trip and feeder bu denity are conidered a alternative pecific variable which are then able to influence each travel alternative differently. The proper number of egment wa aeed by BIC and AIC. Table 2 how the reult of the modelling for the number of egment. A dicued above, thi i a equential proce in which an additional egment i added until the addition of a further egment doe not add ufficiently to goodne of fit. Table 2 how the four egment model i the preferred model a thi ha the maximum number of parameter, the larget non-adjuted and adjuted log likelihood ratio and the lowet BIC and AIC value. In addition it wa the only model in which the parameter coefficient were in line with expectation. There, we adopted for-egment olution for the propoed latent cla NL model 2. Table 2 Goodne-of-fit meaure of latent cla NL model Segment number() Number of parameter Final log-likelihood Likelihood ratio Adjuted likelihood ratio BIC AIC Table 3 preent the reult of the preferred four-egment latent cla NL model, including the ocioeconomic characteritic (population denity, age and income, etc) that drive the egment memberhip. The goodne of fit meaure ugget the model fit the data well, according to the higher value of adjuted likelihood ratio index (0.332). Paenger in egment 1 and egment 2 account for 74% of the ample. Thee egment are relatively large and have coniderable potential for the public tranport market. The advantage of thi latent cla model relative to the imple multinomial logit model i that it allow for identification of ditinct group of paenger' difference in travel mode preference. In the et of trip characteritic variable, the parameter for commuter trip and the 9 journey trip (for all alternative) in egment 1 are larger than thoe in egment 2. In contrat, the paenger in egment 2 were more enitive to the variable of travel zone a hown by the larger negative parameter..

13 4034 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Table 3 Etimation reult for four-egment latent cla NL model 2 with individual characteritic in memberhip function Segment 1 Segment 2 Segment 3 Segment 4 Contant Bu 1.993(6.27)*** 2.773(2.05)** 2.314(3.66)*** 1.502(1.93)** Train 2.151(2.99)** 2.465(3.13)*** 1.172(1.70)* 1.209(4.39)*** Ferry 0.536(1.76)* 0.371(1.64)* 0.833(1.72)* 0.283(1.66)* Bu-bu 2.250(1.98)** 1.821(2.22)** 2.207(2.01)** 0.322(1.99)** Bu-train 2.971(1.83)* 1.579(1.69)* 1.689(1.84)* 0.304(2.01)** Trip characteritic variable Travel time (-3.44)*** (-3.02)*** (-2.11)** (-1.97)** Travel cot (-1.96)** (-1.82)* (-2.08)** (-1.66)* Travel zone (-4.19)*** (-3.81)*** (-1.78)* (-2.01)** Weekday trip 1.095(1.69)* 1.687(2.42)** 0.822(1.92)* 0.533(3.84)*** Peak hour trip 1.923(6.99)*** 2.162(3.98)*** 1.281(2.03)** (-1.99)** Commuter trip 1.667(1.67)* 1.222(1.72)** 0.739(1.81)* 0.112(2.16)** 9 journey trip_ bu 0.732(1.99)** 0.541(1.64)* 0.272(1.97)** 0.222(1.72)* 9 journey trip_ train 1.032(1.72)* 0.839(2.06)** 0.296(1.72)* 0.372(1.66)* 9 journey trip_ bu-bu 0.082(1.66)* 0.069(1.77)* 0.076(1.88)* 0.001(1.65)* 9 journey trip_ bu-train 0.048(1.92)* 0.017(1.96)** 0.009(1.69)* 0.003(1.70)* Service variable Feeder bu denity_ bu-train 3.220(6.29)*** 0.992(1.86)* 1.298(2.14)** 0.394(1.65)* 1j4( t-value v.1) Direct net 0.473(7.52)*** 0.451(7.28)*** 0.272(1.98)** 0.528(3.17)*** Tranfer net 0.345(3.26)*** 0.451(2.54)** 0.638(1.77)* 0.267(3.03)*** Memberhip function Contant 0.289(1.77)* 0.187(2.23)** 0.093(1.82)* Population denity 0.992(1.66)* 0.983(1.71)* 0.533(1.92)* Age (>65 year) (-1.69)* (-2.00)** (-1.86)* Student (>4 year) 1.842(1.90)* 2.334(1.82)* 1.111(1.93)* Employment 2.678(2.31)** 1.992(1.99)** 1.867(2.11)** Main public tranport 1.928(4.88)*** 1.673(2.22)** (-2.52)*** Car number (more than 2) (-2.24)** (-3.69)*** 3.921(1.74)* High income(>$1,500/pw) (-1.82)* (-2.19)** 3.886(1.98)** Segment ize 39% 35% 17% 9% Number of parameter 96 Final log-likelihood Likelihood ratio Adjuted likelihood ratio BIC AIC Note: * indicate 0.1 level of ignificance; ** indicate 0.05 level of ignificance; *** indicate 0.01 level of ignificance. In general, mot variable how the expected ign (i.e. negative impact on utilitie for travel time, travel cot and travel zone) at a ignificance level of 90% or higher which ugget that paenger' utility i expected to be higher with lower travel time, travel cot and/or travel zone. The other generic variable, including weekday trip, peak hour trip and commuter trip have a poitive effect on the paenger travel utility but the peak hour trip in egment 4 ha a negative impact on the paenger in thi egment. Taken together, thi ugget that mot paenger' utility i expected to increae if paenger are high frequency uer who travel during peak period on weekday. The remaining explanatory variable are alternative pecific to the alternative. The 9 journey trip ha different and ignificant poitive coefficient for each alternative acro the different egment. The reult how thi variable ha a larger impact on paenger who elect a direct travel alternative (i.e. bu and/or train only trip). Thi ugget that paenger who are eligible for a 9 journey dicount tend to travel without tranfer. In term of the public tranport ervice variable, the public tranport ervice variable aured a feeder bu top denity doe have the expected impact with a ignificant and poitive effect on bu-train paenger. Thi variable alo ha the greatet impact on paenger in egment 1: the characteritic of the egmentation analyi i conidered next. For the egmentation analyi, a et of the ocioeconomic characteritic i ued to categorize paenger into four egment. The poible egment group are defined by comparing each egment' ocioeconomic characteritic to the bae egment.

14 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Segment 4 i elected a the bae egment becaue it ha the mallet memberhip ize. A detailed paenger egmentation analyi i introduced in next ection (ection 5.3) Paenger egmentation analyi Table 3 include the etimation reult for the preferred four-egment NL model, including howing the individual characteritic in the egment memberhip function. Segment 4 i choen a the bae becaue of it mall memberhip ize (9% only) and where all of the memberhip coefficient are normalized to zero. The etimate for all other egment are interpreted relative to egment 4. Segment 1 i the larget egment with 39% of paenger. Paenger in egment 1 are more enitive to travel cot a compared with paenger in the other egment; the larger alternative coefficient (alternative pecific contant) ugget that, relatively peaking, they prefer travel with an interchange, i.e. inter-tranfer (i.e. bu-train), and/or intra-tranfer (bu-bu). The individual profile of egment 1 member ugget worker with fixed time of work becaue their utility increae if they are commuter and travel in the peak period on weekday. Segment 1 paenger are younger than the older reference memberhip of egment 4 (age of over 65) and have the highet coefficient of employment rate. Member of egment 1 tend to reide in the region with the highet population denity and how the tronget preference, relative to egment 4 for the ue of public tranport a a mode of travel. Segment 1 member how the highet enitivity to feeder bu a there i a trong preference for travel alternative uing the train (both direct and tranfer trip). The paenger in egment 2 (35% of the total paenger) prefer to commute uing a direct ervice, epecially a direct bu ervice. Segment 2 paenger are very enitive to travel ditance (travel zone) but not to travel time. The reult provide evidence that mot paenger in thi egment might be low-income tudent who do not own car, becaue thi egment ha the lowet coefficient of older people (age < 65 year old), lowet car ownerhip, the mot negative income and the highet coefficient of tudent reident, acro the four egment. Segment 3 conit of time-enitive paenger who prefer to ue the bu whether or not a tranfer i needed. In thi egment, mot individual have higher income and own more than two car. The reident region for egment 3 are low population denity region with lowet percentage of tudent relative to egment 4. Thee reident tend not to ue public tranport a their main tranport mode. Paenger in egment 3 are relatively wealthy compared to all other egment. Segment 4 comprie the mallet percentage of paenger (9%) who live in the low population denity area. Segment 4 paenger are enitive to travel cot and prefer to ue a direct ervice, epecially a direct bu ervice. The paenger in thi egment might be elderly reident who tend to travel in off-peak period. In other word, paenger in egment 4 tend to be enior paenger. Table 4 ummarie the characteritic of each egment and label the poible group a employee, tudent, wealthier people and enior. Compared to all other egment, egment 1 ha the highet employment rate, thu egment 1 ha been identified a the "employee" group. Thi tudy ue the mot outtanding characteritic() in ocio economic characteritic to identify poible group for each egment. The ame rule applie for other egment. Therefore, egment 2/3/4 areidentified a "tudent"/"wealthier people"/"enior" becaue of the high percentage of tudent attending chool/high income and high car ownerhip level/elderly people. Table 4 Segment characteritic and claification Segment Segment 1 Segment 2 Segment 3 Segment 4 Claification Employee Student Wealthier people Senior Population denity HH H M L Elderly people (%) M L H HH Student (%) H HH M L Characteritic Employment (%) HH H M L Main public tranport (%) HH H L M Car number M L HH H High income M L HH H * HH/H/M/L indicate the highet/high/medium/low level.

15 4036 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Table 5 lit the alternative hare of each egment. Thi reflect the dicuion above with the employee egment preferring interchange alternative (more than 50%), the tudent and enior egment preferring direct travel alternative and the wealthier people egment howing more equal preference for both direct and trip requiring a tranfer. Employee egment, in particular, prefer inter mode tranfer (e.g., bu to train or vice vera). Table 5 Alternative hare (%) of each egment Segment Segment ize (%) Direct trip Tranfer requirement Bu Train Ferry Bu-Bu Bu-Train Bu-Ferry Employee Student Wealthy people Senior Average Tranfer policie In SEQ, thi zone baed ytem omit the impact of interchange on the network effect. Currently, a quarter of the trip identified in the Go-card data are tranfer trip. An early tudy (Thompon, 1977) analyed the benefit of the network effect in a public tranport ytem and concluded that good deign for a network baed on interchange can attract more paenger than a network deign with a radial ytem and thi ha been hown to be the cae in citie uch a Zurich, Switzerland. From a policy point of view, thi ection look at two poible policie to enhance interchange: firt providing fare dicount to tranfer trip and econd, increaing the denity of feeder bu top thereby improving ervice quality of bu ervice to train tation.. There are two type of poible tranfer fare dicount policie: the firt would give a dicount to all tranfer trip and the econd would give a dicount to inter-modal tranfer trip only (i.e. train-bu and bu-ferry). Figure 5 and 6 how the imulation reult for egment 1, the employee egment for thee two policy option. Variou increae in level of fare dicount are imulated; holding all other factor contant. In Figure 5, a fare dicount i provided to every tranfer trip, all tranfer related alternative increae their hare and direct trip will decreae in hare, a expected. If we provide a dicount only to inter-mode tranfer trip (Figure 6), the number of train-bu paenger will almot double becaue of the free tranfer. Thi inter-modal tranfer trip increae i mainly coming from bubu paenger with all other travel alternative only howing light change. The concluion i that for thee two fare dicount policie, the imulation reult confirm that the effect would be to encourage tranfer and thi may enhance the network effect for the public tranport ytem in SEQ. However, providing tranfer fare dicount to all tranport mode will, a expected, bring larger network effect a compared to providing a tranfer dicount to intermode tranfer only. All other egment how the ame tranfer hifting pattern but with different alternative hare.

16 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Bu Train Ferry Bu-Bu Bu-Train Bu-Ferry % 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 5 Alternative hare change in variou decreaing level of fare dicount on all tranfer trip for Segment Bu Train Ferry Bu-Bu Bu-Train Bu-Ferry % 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 6 Alternative hare change in variou decreaing level of fare dicount on inter-mode tranfer trip for Segment 1 A econd policy option i to improve the acceibility of train by enhancing the feeder bu ervice. The imulation for thi increae the denity of feeder bu top within the catchment area of train tation in order to improve intermodal integration. Taking the employee egment again a an example, the imulation reult in Figure 7 ugget that inter-mode tranfer, epecially for bu-train, would increae ignificantly and would hift from both bu only and bu-bu alternative. A imilar pattern can be found for the wealthier people egment, egment 3. Figure 8 how the impact for the egment 2, the typical tudent egment, and a imilar pattern emerge for the enior egment (not hown). A might be expected, an increae in the denity of feeder bu top increae the bu-train hare but thi i a hift from the train only alternative. For the tudent and enior egment, their move from direct to a tranfer trip could be conitent with taking a feeder bu to a train tation, rather than being given a lift or driving themelve. Again thi i a policy that would appear to increae the network effect.

17 4038 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Bu Train Ferry Bu-Bu Bu-Train Bu-Ferry % 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 7 Alternative hare change in variou increaing level of denity of feeder bu top for the employee egment (egment 1) Bu Train Ferry Bu-Bu Bu-Train Bu-Ferry % 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 8 Alternative hare change in variou increaing level in denity of feeder bu top for the tudent egment (egment 2) 7. Concluion Thi paper explore the interchange behaviour of paenger and ha categorized paenger into different group by modelling paenger travel behaviour in SEQ, Autralia. The data ued to invetigate tranfer behaviour were obtained from Go-card mart card tranaction data in SEQ together with ocioeconomic data from the cenu. The modelling reult identified paenger' heterogeneou preference toward travel alternative with markedly different market egment. The modelling reult how that at 90 % level of confidence (or better) the relative utility aociated with each alternative wa ignificant influenced by trip characteritic and the public tranport ervice level, with individual ocial demographic characteritic in the egment memberhip function. The empirical reult identified paenger categoried into four egment of employee, tudent, wealthier people and enior. The finding ugget that public tranport network effect are mot important to the employee egment with tudent and enior egment being more likely to chooe direct alternative over alternative involving interchange.

18 Barbara T.H. Yen et al. / Tranportation Reearch Procedia 25C (2017) Thi paper contribute to the literature by identifying the tranfer effect for different group of people. The modelling approach, uing latent cla NL modelling, provide information a to how different level influence utility i influenced by the election of different public tranport travel option. Thi allow a better undertanding of the paenger market and what mode of public tranport paenger actually prefer. In order to enhance the public tranport network effect, ome two policie to encourage tranfer by paenger are invetigated uing imulation. The reult how that the network effect for the SEQ public tranport ytem can be improved by providing fare dicount to trip requiring tranfer and increaing acceibility to train tation. Future reearch will focu on the invetigation of other potential determinant influencing different area, including the role of public tranport network deign, and/or mode ervice level (e.g., bu ervice frequency, ervice coverage, etc.). Further egmentation of the peak/off-peak time period will alo be conidered a a way of improving model fit and interpreting fare policie. A further conideration might be to conider a ditance baed fare ytem a oppoed to the current zonal ytem which would provide inight into the financial effect of public tranport fare policie. Finally, underpinning the modelling i that paenger are public tranport captive and have acce to the public mode option, bu, train and ferry in thi tudy. Thi i a limitation of the mart card data which doe not include private vehicle ue. Thi aumption may or may not be true in practice, and an area for future reearch would be to build tranport by car into the modelling a a poible alternative for paenger, to ee how the market hare might change. Acknowledgement Thi reearch wa ponored in part by National Science Council, Republic of China (NSC I ). Reference Abkowitz, M., Joef, R., Tozz, J., Dricoll, M.K., 1987.Operational feaibility of timed tranfer in tranit ytem. Journal of Tranportation Engineering, 113 (2), Anable, J Complacent Car Addict or Apiring Environmentalit? Identifying travel behaviour egment uing attitude theory. Tranport Policy 12, Atkin, S. T Peronal ecurity a a tranport iue: A tate-of-the-art review. Tranportation Review 10, Bagchi, M., White, P.R., What role for mart-card from bu ytem? Municipal Engineer 157, Bagchi, M., White, P.R., 2005.The potential of public tranport mart card data. Tranport Policy 12, Bekhor, S., Elgar, A., Invetment in mobility by car a an explanatory variable for market egmentation. Journal of Public Tranportation 10, Blythe, P., 2004.Improving public tranport ticketing through mart card. Proceeding of the Intitute of Civil Engineer, Municipal Engineer 157, Callaghan, L., and W. Vincent Preliminary evaluation of Metro Orange Line Bu Rapid Tranit Project. Tranportation Reearch Record 2034, Carey, M., 1994.Reliability of interconnected cheduled ervice. European Journal of Operational Reearch 97, Chowdhury, S., Ceder, A., Definition of Planned and Unplanned Tranfer of Public Tranport Service and Uer Deciion to Ue Route with Tranfer. Journal of Public Tranportation 16, Chowdhury, S., Ceder, A., Schwalger, B., The effect of travel time and cot aving on commuter' deciion to travel on public tranport route involving tranfer. Journal of Tranport Geography 43, Clever, R Integrated timed tranfer. Tranportation Reearch Record 1571, Currie, G., Loader, C., Bu network planning for tranfer and the network effect in Melbourne, Autralia. Tranportation Reearch Record 2145, Garling, T., Eek, D., Loukopoulo, P., Fujii, S., Johanon Stenman, O., Kitamura, R., Pendyala, R., Vilhelmon, B., A conceptual analyi of the impact of travel demand management on private car ue. Tranport Policy 9, Golotta, K., Henher, S.A., Why i the Bribane bu Rapid tranit ytem deemed a ucce? Road and Tranport Reearch 17, Guo, Z., Wilon, H.H.M., Aeing the cot of tranfer inconvenience in public tranport ytem: A cae tudy of the London Underground. Tranport Reearch Part A 45, Gupta, S., Chintagunta, P.K., On uing demographic variable to determine egment memberhip in logit mixture model. Journal of