Transit surveys based on on-board passenger interviews suffer from bias. Most commonly observed is the short trip bias: passengers travelling short distances are underrepresented in survey results. Biased data leads to an incorrect estimation of passenger demand can result in an inequitable allocation of revenues between transport operators. This paper examines how the short trip bias can be mitigated during the data ex-trapolation process. Four methods are examined: A simple extrapolation by boarding counts, three iterative proportional fitting models and an additional weighting concept are tested on simulated survey data. The simulative approach enables the evaluation of the examined methods concerning their effects in reducing short trip bias. A total of eight survey situations with selected parameters variated are simulated to allow conclusions about influencing factors. Results suggest that the most effective method is the weighting approach, followed by the iterative proportional fitting methods. Within the class of the iterative propor-tional fitting methods no significant difference is observed. Furthermore it is observed that the effectiveness of the weighting approach strongly relates to passenger numbers and selection rates. Furthermore an overview on topic related literature is given to examine practical approaches to reduce bias in survey data.

Transit surveys based on on-board passenger interviews suffer from bias. Most commonly observed is the short trip bias: passengers travelling short distances are underrepresented in survey results. Biased data leads to an incorrect estimation of passenger demand can result in an inequitable allocation of revenues between transport operators. This paper examines how the short trip bias can be mitigated during the data ex-trapolation process. Four methods are examined: A simple extrapolation by boarding counts, three iterative proportional fitting models and an additional weighting concept are tested on simulated survey data. The simulative approach enables the evaluation of the examined methods concerning their effects in reducing short trip bias. A total of eight survey situations with selected parameters variated are simulated to allow conclusions about influencing factors. Results suggest that the most effective method is the weighting approach, followed by the iterative proportional fitting methods. Within the class of the iterative propor-tional fitting methods no significant difference is observed. Furthermore it is observed that the effectiveness of the weighting approach strongly relates to passenger numbers and selection rates. Furthermore an overview on topic related literature is given to examine practical approaches to reduce bias in survey data.:ABBILDUNGSVERZEICHNIS VII TABELLENVERZEICHNIS VII ABKÜRZUNGSVERZEICHNIS VIII SYMBOLVERZEICHNIS IX 1 EINLEITUNG 1 2 ANFORDERUNGEN AN VERKEHRSERHEBUNGEN 5 2.1 Einnahmeaufteilung im SPNV 5 2.2 Aufbau von Verkehrserhebungen 8 2.2.1 Zweistufige Stichprobenziehung 8 2.2.2 Felderhebung: Zählung und Befragung 10 2.2.3 Datenaufbereitung 11 2.2.4 Hochrechnung und Auswertung 11 2.3 Problem der Stichprobenverzerrung 14 2.3.1 Präzision und Genauigkeit 14 2.3.2 Untererfassung von Kurzstreckenfahrern 15 2.3.3 Weitere Verzerrungsursachen 16 3 LITERATURÜBERBLICK: ANSÄTZE ZUR PROBLEMVERMEIDUNG 19 3.1 Hochrechnungsverfahren 19 3.1.1 Iterative Randsummenverfahren (IPF) 19 3.1.2 Gewichtungsverfahren 21 3.2 Weitere Ansätze 24 3.2.1 Platzgruppenverfahren 24 3.2.2 Anpassung der Startlösung des Hochrechnungsverfahrens 25 3.2.3 Veränderung der Auswahlprozedur der Fahrgäste 27 3.2.4 Veränderung des Erhebungsdesigns 28 4 METHODIK 31 4.1 Auswahl der Verfahren 31 4.1.1 Einsteigerhochrechnung 31 4.1.2 Fratarverfahren 32 4.1.3 Durchschnittsfaktormethode 32 4.1.4 MULTI-Verfahren 33 4.1.5 Gewichtungsverfahren von Keppeler und Schulze 34 4.2 Simulationsdaten 36 4.2.1 Fahrtdaten Linie 1 37 4.2.2 Fahrtdaten Linie 2 38 4.2.3 Befragungsszenarien 39 4.3 Gestaltung der IPF-Verfahren 40 4.3.1 Weitere Randbedingungen 40 4.3.2 Abbruchkriterien 42 4.3.3 Bester Iterationsschritt 44 4.3.4 Aufstellung der Startmatrix 44 4.4 Anwendung des Gewichtungsverfahrens 45 5 ERGEBNISSE 49 5.1 Charakteristik der Befragungsstichproben 49 5.2 Aggregierte Ergebnisse 51 5.3 Einfluss der Befragungsquoten 53 5.4 Verteilung der Verkehrsleistung nach Tarif 54 5.5 Konvergenzverhalten 55 6 DISKUSSION 57 6.1 Ergebnisinterpretation 57 6.2 Einordnung und Schlussfolgerungen 59 7 FAZIT UND AUSBLICK 63 EHRENWÖRTLICHE ERKLÄRUNG XVII

In my PhD, I advanced the scientific exploration of the Next Generation Transit Survey (NGTS), a ground-based wide-field survey operating at ESO’s Paranal Observatory in Chile since 2016. My original contribution to knowledge is the development of novel methods to 1) estimate NGTS’ yield of planets and false positives; 2) disentangle planets from false positives; and 3) accurately characterise planets. If an exoplanet passes (transits) in front of its host star, we can measure a periodic decrease in brightness. The study of transiting exoplanets gives insight into their size, formation, bulk composition and atmospheric properties. Transit surveys are limited by their ability to identify false positives, which can mimic planets and out-number them by a hundredfold. First, I designed a novel yield simulator to optimise NGTS’ observing strategy and identification of false positives (published in Günther et al., 2017a). This showed that NGTS’ prime targets, Neptune- and Earth-sized signals, are frequently mimicked by blended eclipsing binaries, allowing me to quantify and prepare strategies for candidate vetting and follow-up. Second, I developed a centroiding algorithm for NGTS, achieving a precision of 0.25 milli-pixel in a CCD image (published in Günther et al., 2017b). With this, one can measure a shift of light during an eclipse, readily identifying unresolved blended objects. Third, I innovated a joint Bayesian fitting framework for photometry, centroids, and radial velocity cross-correlation function profiles. This allows to disentangle which object (target or blend) is causing the signal and to characterise the system. My method has already unmasked numerous false positives. Most importantly, I confirmed that a signal which was almost erroneously rejected, is in fact an exoplanet (published in Günther et al., 2018). The presented achievements minimise the contamination with blended false positives in NGTS candidates by 80%, and show a new approach for unmasking hidden exoplanets. This research enhanced the success of NGTS, and can provide guidance for future missions.