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Showing 1 to 3 of 3 (0.069 seconds)Spelling suggestions: "subject:"short trap"" "subject:"short tri""
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Hochrechnung von Fahrgastbefragungen im Öffentlichen Verkehr – Ansätze zur Vermeidung von StichprobenverzerrungenNeumann, Marcus 04 July 2017 (has links) (PDF)
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.
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Hochrechnung von Fahrgastbefragungen im Öffentlichen Verkehr – Ansätze zur Vermeidung von StichprobenverzerrungenNeumann, Marcus 07 June 2017 (has links)
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
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GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS DataDalumpines, Ron 26 September 2014 (has links)
Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use).
The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit.
There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD)
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