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Disruptions in public transport are the major cause of passenger dissatisfaction (van Lierop et al., 2018) and they result in decreased public transport usage (Nazem et al., 2018). Much of the research on disruptions has focused on robustness of networks (Cats, 2016), network planning during disruptions (van der Hurk et al., 2016), and passenger information provision (Bruglieri et al., 2015). However, limited focus has been on understanding the changes to travel behaviour caused by planned long-term disruptions, such as closures due to construction work. Hence, this study analyses the effects of a long-term closure of an important suburban railway line in the Greater Copenhagen area (Denmark) on the travel behaviour of public transport passengers. Using a large-scale smart card dataset the travel behaviour before, during and after the closure is compared and analysed across different groups of travellers focusing on both short-term and long-term changes to the travel patterns across groups., Proceedings from the Annual Transport Conference at Aalborg University, Vol. 27 No. 1 (2020): Proceedings from the Annual Transport Conference at Aalborg University

Transportsystemer i storbyområder er under pres som følge af stigende behov for mobilitet,og for både vejtrafikken og den kollektive transport er kapaciteten ved at væreopbrugt. Mange timer spildes hver dag i trængslen på byernes veje, og såfremt transportsystemetskal kunne følge med mobilitetsudviklingen, kræver det, at den kollektivetransport bidrager med væsentlig kapacitet til at flytte mennesker fra A til B. Den kollektivetransport er, udover gang og cykling, derudover også en væsentlig bidragsyder til dengrønne omstilling af transportsystemerne i storbyområderne, og det er derfor vigtigt medmodeller og analyser, der kan benyttes til at gøre den kollektive transport mere attraktiv.Kollektive transportsystemer er often komplekse med en kombination af linjer med en relativlav frekvens og faste afgangsminuttal (køreplansbaserede linjer såsom tog og lokalebusser), samt linjer med høj frekvens (frekvensbaserede linjer såsom metro og A-busser),hvor kun tiden mellem afgange publiceres til passagererne. På grund af systemernes kompleksitetkræver det avancerede modeller for at kunne evaluere, hvilket serviceniveau engiven køreplan giver passagererne, da der skal tages højde for skift mellem linjetyper ogdisses attraktivitet. Desuden kræver sådanne modeller detaljerede input fra pålideligedatakilder, der beskriver passagerernes adfærd på de forskellige delkomponenter af densamlede kollektive rejse. Denne ph.d.-afhandling omhandler adskillige aspekter inden formodellering af kollektiv transport set fra et passagerperspektiv. Afhandlingen er inddelti tre dele: Del 1 præsenterer nyskabende rutevalgsmodeller, der bruges til at evaluereserviceniveauet baseret på netværk med både frekvens- og køreplansbaserede linjer. Del2 fokuserer på rutevalgspræferencer for passagererne fra dør til dør i sådanne kompleksenetværk på basis af rapporterede ture med kollektiv transport. Den tredje og sidste del indeholdertre forskellige analyser af passagerers rejseadfærd baseret på data fra Rejsekortet,samt et lignende automatisk billetsystem fra Hong Kong.Den første del af afhandlingen dækker de udfordringer der er, når serviceniveauet forpassagerernes rejse fra A til B skal evalueres. To studier og artikler fokuserer på kombinationenaf frekvens- og køreplansbaserede linjer i ét kollektiv transportnetværk og hvordanrejsetider for attraktive ruter vurderes heri.Den første artikel udvikler en ny metode, hvorved evalueringen af hvilke ruter passagerernevælger fra A til B, kan beskrives på en mere adfærdsmæssig korrekt måde. Først genereresde mulige ruter fra A til B i et net med kombinerede køreplaner vha. en heuristik, hvordet sikres at passagerer ved et valg af en inkluderet rute også ankommer til destinationenindenfor en rimelig tidsramme. I et efterfølgende skridt fordeles passagerer i mellem deinkluderede alternative ruter vha. en diskret valgmodel. Resultaterne fra modellen visersig at være stabile uagtet om en linje defineres med en eksakt køreplan med minuttal ellerblot dens frekvens. En af modellens fordele er, sammenlignet med andre modeller, attrafikplanlæggerne kan nøjes med færre antagelser om den faktiske køreplan for en linjeog derved gøre det lettere at evaluere og sammenligne adskillige køreplansscenarier.Den anden artikel foreslår en metode, der benytter Markovkæder til at identificere rejsetidsfordelingerfor forskellige rutevalgsalternativer, mens der samtidig tages højde forstokastiske køretider som følge af forsinkelser. Givet relevante alternativer kan metodenudregne rejsetidsfordelingen for rejsende fra A til B for forskellige kombinationer af linjerog således også ruter gennem netværket. Både køreplans- og frekvensbaserede linjerkan bruges som input til modellen. Ved at benytte Markovkæder, kan sandsynlighedernefor at nå et skift beskrives analytisk, hvorved traditionelt krævende simulationsmodellerkan undgås. Flere detaljerede analyser kan udledes baseret på de resulterende rejsetidsfordelinger,hvilket kan blive et vigtigt værktøj for køreplanlæggere.Mens Del 1 af afhandlingen afdækker nye metoder til at vurdere serviceniveauet af engiven køreplan, omhandler Del 2 inputtet til disse modeller ved at undersøge passagerernesrutevalgspræferencer. Rejsende evaluerer en rutes attraktivitet baseret på adskilligekomponenter såsom rejsetid og antal skift, men det vigtige er hvordan f.eks. et ekstraskift mellem transportmidler vægtes i forhold til en evt. kortere samlet rejsetid. To forskelligeartikler i afhandlingen beskriver passagerernes præferencer baseret på data indsamleti Transportvaneundersøgelsen (TU), hvor passagerer har rapporteret hvilken rute de harvalgt i kollektiv transport i Hovedstadsområdet. I begge artikler findes præferencerne vedbrug af diskrete valgmodeller, hvor sandsynligheden for den valgte rute maksimeres udfra delkomponenterne af den valgte rute og de alternative ruter.Den tredje artikel omhandler således, hvordan passagerer vælger i mellem frekvens- ogkøreplansbaserede linjer. Separate parametre estimeres for ventetid for hhv. frekvens- ogkøreplansbaserede linjer, og dette viser, at passagererne har en højere gene ved at ventepå frekvensbaserede linjer ift. køreplansbaserede linjer. Dette kan muligvis forklares vedfrygten for at vente på en bus der aldrig kommer (fordi ingen fast køreplan kendes), menopvejes til dels ved at ventetiden for frekvensbaserede linjer generelt er kortere end forkøreplansbaserede linjer. De separate parametre er vigtige at inkludere i rutevalgsmodeller,da det ellers risikeres at skabe skævheder i fordelinger mellem frekvens- og køreplansbaseredelinjer. Sådan skævheder kan i sidste ende lede til fejlagtige konklusioneri cost-benefit analyser. Artiklen undersøger ligeledes hvordan passagerernes præferencerfor tid i de enkelte kollektive transportmidler ændrer sig, i forhold til hvor lang tid dertilbringes i køretøjet. Her findes det, at der er en meget lav marginal negativ nytte forat tilbringe kort tid i f.eks. metro, men at længere tids ophold i et metrotog marginaltopleves væsentligt værre. Dette er i kontrast til tiden i et regionaltog, hvor kort tid i togethar en marginalt stor negativ nytte, mens den marginale negative nytte falder væsentligtjo længere tid passageren er i toget.Den fjerde artikel har fokus på hvordan passagerer vælger ruter givet hvilke stationstyperder besøges undervejs. Artiklen gør i første omgang rede for, hvilke stationskarakteristikasom udenlandske studier fundet har en stor påvirkning på passagerernes rutevalg. Udaf disse bliver tre karakteristika udvalgt, som formodes at have indvirkning på danskepassagerers rutevalg. Analysen viser, at rutevalgssandsynligheden påvirkes positivt af,hvorvidt der på en af skiftestationerne er en lille butik eller lignende, samt hvorvidt derer rulletrapper, der kan lette gangturen i skiftet. Ydermere er det også signifikant, atpassagerer fravælger stationer, hvor det er svært at orientere sig i skiftet. Det er såledesmuligt at estimere separate skiftestraffe for stationer med forskellige karakteristika. Detbedst mulige skifte har således en straf svarende til 5,4 minutter i bus, hvorimod detværste er sammenlignligt med 12,1 minutter i bus. Disse resultater er vigtige, da dekan bruges til at kvantificere effekten på passagerflows af stationsopgraderinger eller nyedesigns. Sådanne mindre ændringer kan vise sig at være mere omkostningseffektive endsporopgraderinger eller andre forbedringer af jernbanenettet, og samtidig give en bedreoplevelse til passagererne.Den sidste del af afhandlingen, Del 3, omhandler tre analyser baseret på rejsekortdatasamt et system som ligner Rejsekortet i Hong Kong. Rejsekortdata dækker efterhåndenflere og flere rejser i den kollektive transport, og datamængden er både god i forhold tilanalyser af passagerernes rejsemønstre over længere perioder samt for detaljerede studieraf enkeltdele af rejserne. Desuden kan forskelle i passagerernes adfærd analyseres med enhøj detaljegrad.Den femte artikel omhandler sporarbejdet på S-togsbanen mellem Valby og Frederikssundi sommeren 2018. Rejsekortdata benyttes til at analysere passagerernes rejsemønstre bådefør og efter den tre måneder lange lukning af banen, hvor togbusser servicerede linjen medtilhørende forlængelser af rejsetiderne. Passagererne inddeles i forskellige grupper baseretpå deres rejsemønstre før og efter sporarbejdet. Da passagerenes rejsemønstre ofte ændrersig selvom der ikke er sporarbejder, sammenlignes ændringerne på banen til Frederikssundmed en tilsvarende bane til Køge, som ikke havde nogen større sporarbejder. Der ses ingenstørre forskel mellem banerne i ændringen fra forår til efterår for passagerne med en højrejsefrekvens i foråret. Dog sker der en nedgang i passagertallet på Frederikssundsbanenefter sporarbejdet, når der sammenlignes med banen til Køge. Dette skyldes til dels, at derikke tiltrækkes lige så mange frekvente rejsende til Frederikssundsbanen henover sommer.Ved at analysere de daglige rejsemønstre for passagerer der pendlede på Frederikssundsbanenfør sporarbejdet, kan det konkluderes at 17% næsten stoppede med at benyttekollektiv transport under sporarbejdet, men returnerede til et frekvent rejsemønster eftersporarbejdet. Dette indikerer, at nogle passagerer faktisk tilvælger kollektiv transport,selvom de ikke er tvunget til det.Den sjette artikel benytter også data fra Rejsekortet, men i stedet for at fokusere påpassagerernes rejsemønstre, fokuseres der på at estimere gangtiderne, der er nødvendigefor skift mellem bus og tog. Denne tid er vigtig at kende, så gode korrespondancermellem bus og tog kan skabe et endnu mere attraktivt kollektivt transportsystem. Ved atkombinere data fra Rejsekortet med data fra GPS-lokationer for busserne, så kan tiden,fra bussen ankom til passageren tjekkede ind på perronen, relativt simpelt beregnes. Doger de rå data behæftet med store bias, da passagerer i nogle tilfælde venter i ventesalenfør de går ned på perronen eller ligefrem benytter muligheden for at shoppe i løbet afskiftet. Derfor benyttes en maskinlæringsmodel til at klassificere de passagerer, som gikdirekte ned til perronen i forhold til de passagerer, som foretog sig noget andet underskiftet. Dermed kan der opnås relativt sikre estimationer af gangtidsfordelingen for deskiftende passagerer, som kan benyttes i stedet for tidskrævende manuelle processer medat definere gangtiden fra et busstoppested til perronen. Modellen benyttes til at estimeregangtidsfordelinger for 129 stationer i Østdanmark, og resultaterne heraf viser, at der eren større andel af passagerer med aktivtet, når der findes flere butikker i stationsområdet.Den sidste artikel i afhandlingen, baserer sig på data fra metroen i Hong Kong, som imyldretidsperioderne har så mange passagerer, at det væsentligt overskrider den kapacitetder er i systemet. Én specifik situation, som udløses af den enorme trængsel i systemet,analyseres i artiklen. I situationen vælger nogle passagerer at køre forbi den station, hvorde under ikke trængselspåvirkede omstændigheder ville skifte. Derefter skifter de på enanden station længere nede af linjen og kører tilbage forbi den normale skiftestation. Dettekaldes ’Reverse routing’ og sker udelukkende fordi passagerer kan risikere, at de ikke kankomme med de første 3-4 tog der afgår fra stationen på grund af trængsel. Der findes iartiklen ingen endelig konklusion på omfanget af denne ’Reverse routing. Et interessantresultat er dog, at de passagerer som skal rejse længst efter skiftet har en væsentlig andenadfærd, end dem som kun skal rejse kort efter skiftet. Dette kunne indikere, at nogle afdisse passagerer, som skal langt efter skiftet, vælger at benytte sig af ’Reverse routing’for at opnå en højere sandsynlighed for en siddeplads eller blot et bedre sted at stå. Dettekan også hænge sammen med de stigende marginale tidsværdier, som blev fundet formetropassagerer i den tredje artikel. Artiklen belyser også kort, hvorvidt der ses lignendeeksempler på specielle rutevalg i den danske Metro, men ud fra rejsekortdata kan dethurtigt konkluderes, at trængselsniveauet ikke er højt nok i Metroen, til at dette sker.Sammenfattende bidrager denne ph.d.-afhandling til fire hovedpunkter: i) udvikling afnye og detaljerede modeller for evalueringen af serviceniveauet af forskellige køreplansscenariermed frekvens- og køreplansbaserede linjer, ii) afdækning og kvantificering afden signifikante negative betydning af skift for rejser med den kollektive transport, samtanalyser af hvilke stationskarakteristika der kan sænke denne negative påvirkning, iii) atvise hvorledes den enorme mængde af data fra Rejsekortet kan benyttes til at analyserepassageradfærd over tid, samt gangtider på skiftestation, og iv) at analysere trængslens effektpå rutevalg i metronetværk. Samlet set dækker afhandlingen bredt modelleringen oganalyser af passageradfærd i kollektiv transport, og bidrager med yderligere viden til denallerede eksisterende literatur. Flere nye metoder er udviklet i ph.d.’en, især ift. brugenaf Rejsekortdata, og dette kan forhåbentlig benyttes som springbræt i fremtidige studiermed fokus på kollektiv transport. Transport systems in metropolitan areas are on both the road and public transport side challenged on providing sufficient capacity for the increasing mobility needs. An increasing number of hours is wasted in congestion on the roads, and good public transport service is needed to provide sufficient capacity in the transport system. Public transport is not only seen as a way of increasing the mobility in metropolitan areas, but also as one of the important contributors to the transition for more sustainable mobility in urban areas. The public transport systems must thus be an attractive alternative to taking the car to attract more passengers, and facilitate the transition for a more sustainable transport system.The public transport systems are often complex with a mix of lines, where passengers for some services rely on a published detailed timetable (schedule-based lines), while they for other high frequency lines rely on the headway between consecutive services on the line (frequency-based lines). Due to the complexity of these systems, advanced models are needed to analyse the level of service provided to the passengers given the timetable of the network. Furthermore, the inputs for these models require analysis of various aspects of passenger travel behaviour based on reliable data sources.This PhD thesis concerns several aspects within modelling of public transport systems with a passenger oriented perspective. The thesis is split into three main parts; Part I, Assignment models for mixed schedule- and frequency-based public transport systems, presents novel methodological approaches for determining the level of service for passengers in public transport networks with both schedule- and frequency-based services; Part II, Route choice models for mixed schedule- and frequency-based public transport systems, focuses on passengers’ route choice preferences from origin to destination in these complex networks, based on revealed passenger route choice surveys. The third and final part of the thesis, Studies on public transport passenger behaviour based on smart card data, covers three analyses of passenger travel behaviour based on smart card data. Part I of the thesis covers the difficult task of assigning (predicting) passengers to routes from origin to destination in order to evaluate the level of service provided to the passengers. Specifically, two studies focus on the combination of schedule- and frequency-based services in public transport networks and how to assess the travel times on the attractive routes in such a network. The first paper develops a novel methodology to assign passengers in a mixed schedule- and frequency-based network. First, choice-sets with different possible routes are generated based on a heuristic, which requires that the passenger can reach the destination within a certain threshold using the specific route. A subsequent step distributes the passengers across the alternatives using a discrete choice model. The resulting flow distributions across alternatives are stable regarding the specification of a line as either schedule- or frequency-based. Compared to other models, this allows the modeller to make fewer assumptions on the actual schedule of a line, and eases the evaluation when several timetable scenarios need to be compared. The second paper proposes a method that uses Markov chains to identify the travel time distributions for different route choice alternatives, when stochastic running times of a line due to delays are taken into account. Given a set of attractive lines for passengers travelling from origin to destination, the methodology calculates the travel time distribution for different combinations of lines and thereby alternative routes through the network. Both schedule- and frequency-based lines can be part of the input to the model. By using Markov chains the probability of reaching a connecting service, can be analytically described, whereby the use of traditional demanding simulation models can be avoided. Several detailed analyses can be derived based on the resulting travel time distributions, which can become an important tool for timetable planners.While Part I of the thesis covers the evaluation of the level of service offered to the passengers, Part II covers the input to these models by investigating the route choice preferences of the passengers. Travellers evaluate the attractiveness of a route based on several features such as travel time and number of transfers, but the specific challenge concerning how passengers trade off for example routes with a high travel time vs. routes with lower travel time which include more transfers persist. These trade-offs are investigated in two papers using a dataset covering self-reported trips using public transport in the Greater Copenhagen area. In both papers a discrete choice model is the basis for the extraction of the passenger route choice preferences, and this is achieved by comparing the observed routes with a large set of alternative routes the passenger could have chosen. The third paper investigates the trade-offs passengers have to make when choosing between alternatives with different waiting times and in-vehicle times. Waiting time for schedule-based services, such as regional trains and local busses, are estimated separately from frequency-based services (metro and high-frequency busses) and this shows, that passengers have a higher nuisance for waiting for frequency-based services compared to waiting for schedule-based services with a known timetable. However, in general lower waiting times for frequency-based services makes the decision between alternatives with schedule- or frequency-based services almost the same. If the differences in parameters of waiting time are not accounted for in assignment models, there is a risk of creating a biased flow estimation, which can eventually lead to wrong conclusions in feasibility studies. The paper also investigates whether the marginal dis-utility of in-vehicle time varies across and within each sub-mode, i.e. metro, bus and trains. It is shown, that the marginal dis-utility for metro considerably increases for longer trips whereas the marginal dis-utility decreases for in-vehicle time in trains. The fourth paper focuses on the choice of transfer location in passenger route choice. The paper reviews existing literature on transfer attributes which affects passenger route choice, and selects three attributes found to be important for passenger route choice. The analysis shows that passengers prefer routes, which includes a shop available at any of the transfer stations visited during the trip, thus indicating the preference for being able to do smaller grocery shopping en-route. Passengers also prefer escalators over regular stairs,and prefer that transfers should be easy to navigate through. Using these attributes, it is possible to disentangle the transfer penalty for stations with different characteristics. The best possible transfer thereby has a penalty equivalent to spending 5.4 minutes extra in a bus, whereas the worst possible transfer is comparable to spending 12.1 minutes in a bus. The results have important policy implications for evaluation of different station designs and how the resulting passenger flows will be, if stations are upgraded or redesigned. Such investments can turn out to be more cost effective than track upgrades or other improvements of the railway, while still providing a better level-of-service to passengers.The final part of the thesis, Part III, covers three analyses based on smart card data. Smart card data is available in rich numbers from automatic fare collection systems and is becoming an increasingly important tool for analysing passengers’ travel behaviour. This thesis uses smart card data with different degrees of detail, and the studies span from analysing the individual mobility to more aggregated analysis, where smart card data covers the heterogeneity of passenger behaviour. The fifth paper investigates individual mobility over a long time period based on data from the Danish smart card, Rejsekort. The study analyses travel behaviour before, during and after a three month track closure on a suburban rail line in the Greater Copenhagen area, where replacement busses served the line resulting in significantly increased travel times. Passengers are clustered based on their travel behaviour before and after the track closure. A similar track section is used as comparison to the changes in ridership at the suspended track section, as the individual passenger travel behaviour changes considerably over time due to changes in individual employment and general seasonal trends. By comparing the changes in travel behaviour for passengers travelling frequently before the disruption on either the affected or reference line, no apparent difference is seen for the period after normal operations resumed. However, the total ridership on the affected line decreased compared to the reference line, and a comparison of the changes in passenger travel for the different groups, suggests that the deficit is a result of less attraction of new passengers on the affected line. By analysing the daily travel patterns for the group who commuted on the affected line before the disruption, it is found that 17% of the passengers almost entirely stopped using public transport during the disruption, but returned to a regular usage of public transport after the normal operations resumed. This indicates, that at least some passengers favor public transport and are not forced to use the public transportsystem.Data from Rejsekort is also used in the sixth paper, but on a more detailed level. The paper fuses smart card data and automatic vehicle location data to estimate the walking time used from alighting a bus until the passenger taps in at a train platform. This walking time is essential to know, as it is used in timetabling and synchronisation of busses and trains. Using the raw observed times from the data fusion leads to significantly overestimated walking times, as some passengers are doing activities during their transfer. Therefore, a hierarchical Bayesian mixture model is used to isolate the passengers doing activities during the transfer from the passengers walking directly. The results show that the model is able to accurately replicate the observed walking times and estimate the walking time necessary to walk from bus stop to train platform. The study establishes a more data-driven procedure for estimation of walking times at transfers, and is appliedto 129 stations in the Eastern part of Denmark. Tests show that the share of passengers doing activities during their transfers increases with the number of shops available near the transfer station. Whereas the two preceding papers focus on the use of data from Rejsekort, the seventh and final paper utilises an extensive smart card dataset from Hong Kong. The number of passengers in the Metro in Hong Kong exceeds the available capacity during the peak hours, and the paper describes and analyses unusual path choice behaviour that stems from the excessive crowding. Under the excessive crowding situations passengers can be observed to do reverse routing, namely choose to transfer at a station further down a line in order to travel backwards and pass the station where passengers would usually transfer in uncrowded conditions. Such reverse routing can increase the travel time reliability and also increase the chance for the passenger to get a seat or better standing position in the train. However, based on the analysis, no final conclusions can be made on the share of passengers using this option of reverse routing. However, the results indicate that passengers travelling furthest after transferring have a slightly different behaviour, which could stem from a higher degree of reverse routing. It can also be substantiated by the finding in the third paper, that the marginal value of time is increasing for passengers using the metro. A short paragraph in the paper also considers whether such unusual route choices are occurring in the Danish Metro, but based on analysis of data from Rejsekort, this can quickly be ruled out to be the case. In summary, this PhD thesis has contributed to i) new methodologies to assign passengers to routes for detailed and analytical evaluation of the level of service provided to passengers in mixed schedule- and frequency-based public transport system, ii) revealing and quantifying of the significant dis-utility of transfers in public transport route choice in combination with detailed analysis of the important characteristics of station attributes, and iii) develop two novel methods using data from Rejsekort for analysing both longterm travel behaviour and walking times at transfers, and iv) investigate the effects of crowding on passenger path choice in congested metro systems. Overall the thesis coversa broad span of public transport modelling and contributes to already existing knowledge in the domain. Several new methodologies are developed, especially on the use of smart card data, and these can be used for further research within the domain.

This study provides insight into how passengers value transfer facilities compared to other components of a route when choosing their route in public transport. A thorough analysis of previously estimated parameters for public transport route choice and a selection of the best available data for describing passenger preferences for transfer attributes led to a short list of three variables, which elaborates the general transfer penalty often included in route choice models in public transport. These are shopping availability, number of escalators at transfer stations and the difficulty of wayfinding at transfers. Shopping availability and the number of escalators improve the passengers’ utility of a certain route while more difficult wayfinding at transfer points have a negative impact on the utility of a route. The effect of the additional transfer attributes in the model is in general only significant for trips with work related purposes (commuting and business trips). The more detailed route choice model including transfer attributes makes it possible to evaluate the effect of different station designs and improvements of existing station facilities., Proceedings from the Annual Transport Conference at Aalborg University, Vol. 26 No. 1 (2019): Proceedings from the Annual Transport Conference at Aalborg University