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11	Eine CarSharing-Zielgruppenanalyse der großen SrV-Vergleichsstädte Hahn, Christoph January 2015 (has links) Due to enormous growth rates during the last couple of years CarSharing has become an interesting field of research. This Master Thesis focuses on the analysis of CarSharing users. For this purpose more than 37.000 participants of the "Große SrV-Vergleichsstädte"-Survey were taken into account. After a short literature review and some general information about the statistical methods, the thesis tries to work out some major characteristics of the typical CarSharing user. It will be shown, that a high income and a young age are of central importance. Also when analysing other variables different user quotas are observed, but can mostly be explained with different age or income levels. At the end a binary logit model is sugested to differ between users and non users, by using the previous analysed observed characteristics as input.:Inhalt Tabellenverzeichnis iii Abbildungsverzeichnis v 1 Einleitung 1 2 Literaturüberblick 3 3 Methodische Grundlagen 7 3.1 Korrigierter Kontingenzkoeffizient nach Karl Pearson 7 3.2 Kolmogorov-Smirnov-Test 8 3.3 Logistische Regression 9 4 Vorbetrachtungen 12 4.1 Datenaufbereitung 12 4.2 Vergleich von tatsächlichen Nutzern und angemeldeten Personen 13 5 Der Typische CarSharing-Nutzer 17 5.1 Alter 17 5.2 Einkommen 22 5.3 Haushaltsgröße 27 5.4 PKW-Verfügbarkeit 36 5.6 Höchste Berufsausbildung 42 5.7 Zusammenfassung 45 6 Logistische Regression 46 7 Fazit 53 Anhang I Literaturverzeichnis III Ehrenwörtliche Erklärung V info:eu-repo/classification/ddc/330 ddc:330 info:eu-repo/classification/ddc/380 ddc:380 typical CarSharing user, logit model
12	Can light passenger vehicle trajectory better explain the injury severity in crashes with bicycles than crash type? Wahi, Rabbani Rash-ha, Haworth, Narelle, Debnath, Ashim Kumar, King, Mark, Soro, Wonmongo 03 January 2023 (has links) Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.
13	Ein simultanes Erzeugungs-, Verteilungs-, Aufteilungs- und Routenwahlmodell / A simultaneous Trip Generation, Distribution, Modal Split and Route Choice Model Dugge, Birgit 08 November 2006 (has links) (PDF) In dieser Arbeit wird ein simultanes Quell-, Ziel-, Verkehrsmittel- und Routenwahlmodell (Modell EVA-U) entwickelt, welches ein stochastisches Nutzergleichgewicht erreicht. Die Routenwahlmodelle der Verkehrsarten sind nicht mehr Teil der Umlegungsalgorithmen, sondern in das Nachfragemodell integriert. Dadurch ist eine konsistente Bewertung aller Alternativen (der Verkehrsarten) möglich. Das Simultanmodell EVA-U stellt eine Weiterentwicklung des Simultanmodells EVA von LOHSE dar. Das EVA-U-Modell ist den universalen Logit-Modellen zuzuordnen. Die Randsummenbedingungen der Verkehrsverteilung werden beachtet. Die Bewertung der Alternativen erfolgt mittels Generalisierter Kosten. Die Abhängigkeit von Routen wird berücksichtigt, ebenso die Tagesganglinie der Verkehrsnachfrage und die Fahrpläne des ÖV-Systems. Das Modell EVA-U erlaubt auch die Berücksichtigung von Routen intermodaler Verkehrsarten (z.B. P+R). Darüber hinaus ist die Integration eines Modells des ruhenden Verkehrs möglich. / In this thesis a simultaneous Trip Generation-, Distribution-, Modal-Split and Route Choice Model (modell EVA-U) is elaborated. The model tends to reach a stochastic user equilibrium. The route choice algorithms are not longer part of an assignment procedure but part of the demand model. A consistent assessment of properties of all transport systems is possible. The simultaneous model EVA-U is an advancement of the EVA-Model by Lohse. The model EVA-U is to be assigned to the generalised logit-models. All matrix constrains are taken into account. The assessment is effected by generalised costs. The dependence of routes is taken into account. Moreover, the integration of day time and the schedules of private transport lines is possible. Furthermore, it is possible to integrate a model of parked cars and circuits of inter-modal traffic forms (park and ride) in the Model EVA-U. Verkehrserzeugung Verkehrsverteilung Verkehrsaufteilung Routenwahl Umlegung EVA-U-Modell Verkehrsnachfragemodell IIA-Eigenschaft Zeitscheiben Parkraumbeschränkung Routensuchalgorithmen Simulation der Routenwahl des MIV Stochastisches Nutzergle Trip Generation Trip Distribution Modal-Split-Model Route Choice Assignment Model EVA-U transportation demand model IIA-Property Limitation of parking places route search algorithm stochastic us ddc:620 rvk:ZO 4600 Verkehrsmittel Verkehrsverteilung Wegewahl Simulation Verkehrsnachfrage Logit-Modell
14	Demand for Irrigation Water from Depleting Groundwater Resources: An Econometric Approach / Wassernachfrage und Bewässerung aus knappen Grundwasserressourcen: Ein ökonometrischer Ansatz Jamali Jaghdani, Tinoush 09 February 2012 (has links) No description available. 630 Landwirtschaft Veterinärmedizin YIO 900 Sonstige Agrarmärkte Agricultural Sciences Grundwasser Iran Rafsanajan Wassernachfrage Pistazienproduktion Translog-Kostenfunktion Räumliche konometrie Wasser-Spotmarkt Logit-Modell Heckman-Sample-Selection-Modell Groundwater Iran Rafsanjan water demand pistachio production translog cost function spatial econometrics spot water market logit model willingness to pay Heckman sample selection model 83.66 Agrarwirtschaft
15	Ein simultanes Erzeugungs-, Verteilungs-, Aufteilungs- und Routenwahlmodell Dugge, Birgit 13 April 2006 (has links) In dieser Arbeit wird ein simultanes Quell-, Ziel-, Verkehrsmittel- und Routenwahlmodell (Modell EVA-U) entwickelt, welches ein stochastisches Nutzergleichgewicht erreicht. Die Routenwahlmodelle der Verkehrsarten sind nicht mehr Teil der Umlegungsalgorithmen, sondern in das Nachfragemodell integriert. Dadurch ist eine konsistente Bewertung aller Alternativen (der Verkehrsarten) möglich. Das Simultanmodell EVA-U stellt eine Weiterentwicklung des Simultanmodells EVA von LOHSE dar. Das EVA-U-Modell ist den universalen Logit-Modellen zuzuordnen. Die Randsummenbedingungen der Verkehrsverteilung werden beachtet. Die Bewertung der Alternativen erfolgt mittels Generalisierter Kosten. Die Abhängigkeit von Routen wird berücksichtigt, ebenso die Tagesganglinie der Verkehrsnachfrage und die Fahrpläne des ÖV-Systems. Das Modell EVA-U erlaubt auch die Berücksichtigung von Routen intermodaler Verkehrsarten (z.B. P+R). Darüber hinaus ist die Integration eines Modells des ruhenden Verkehrs möglich. / In this thesis a simultaneous Trip Generation-, Distribution-, Modal-Split and Route Choice Model (modell EVA-U) is elaborated. The model tends to reach a stochastic user equilibrium. The route choice algorithms are not longer part of an assignment procedure but part of the demand model. A consistent assessment of properties of all transport systems is possible. The simultaneous model EVA-U is an advancement of the EVA-Model by Lohse. The model EVA-U is to be assigned to the generalised logit-models. All matrix constrains are taken into account. The assessment is effected by generalised costs. The dependence of routes is taken into account. Moreover, the integration of day time and the schedules of private transport lines is possible. Furthermore, it is possible to integrate a model of parked cars and circuits of inter-modal traffic forms (park and ride) in the Model EVA-U. info:eu-repo/classification/ddc/620 ddc:620

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