Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

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In today’s world of volatile fuel prices and climate concerns, there is little study on the relation between vehicle ownership patterns and attitudes toward potential policies and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s household-fleet evolution. Results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are vehicle purchase price, type/class, and fuel economy (with 30%, 21% and 19% of respondents placing these in their top three). Most (56%) respondents also indicated that they would seriously consider purchasing a Plug-In Hybrid Electric Vehicle (PHEV) if it were to cost $6,000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings. 25-year simulations suggest that 19% of Austin’s vehicle fleet could be comprised of Hybrid Electric Vehicles (HEVs) and PHEVs under adoption of a feebate policy (along with PHEV availability in Year 1 of the simulation, and current gas prices throughout). Under all scenarios vehicle usage levels (in total vehicle miles traveled [VMT]) are predicted to increase overall, along with average vehicle ownership levels (per household, and per capita); and a feebate policy is predicted to raise total regional VMT slightly (just 4.43 percent, by simulation year 25), relative to the trend scenario, while reducing CO2 emissions only slightly (by 3.8 percent, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 17% and CO2 emissions by 22% (relative to trend). Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. And HEVs, PHEVs and Smart Cars are estimated to represent a major share of the fleet’s VMT (25%) by year 25 under the feebate scenario. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet), yet feebate-policy receipts exceed rebates in each simulation year. A 15% reduction in the usage levels of SUVs, CUVs and minivans is observed in the $5/gallon scenario (relative to trend). Mean use levels per vehicle of HEVs and PHEVs are simulated to have a variation of 753 and 495 across scenarios. In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have even more significant effects on energy dependence and greenhouse gas emissions. / text

A digital signature technique has evolved into varies digital signature schemes in different application environments. In general, a digital signature consists of a random number and a hash function in addition to signing function. The random number can be used to provide the randomization of digital signatures. The hash function can be used for generating a message digest that has a fix length and is convenient for signing. The random number that hides in the digital signature is a useful factor. If we can use this factor well, then the digital signature can carry the other secret messages. On the basis of the concept of a subliminal channel proposed by Simmon, we have proposed multiple subliminal channels that can carry more than one subliminal message to different subliminal receivers. Furthermore, by using the concept of a subliminal channel, we can use the random number as another secure parameter of the digital signature. This concept leads to a forward-secure digital signature with backward-secure detection when the subliminal channel is embedded in the signature. We have proposed a forward-backward secure digital signature. A hash function is an important tool for generating a message digest. The hash function used in a signature must be one-way and collision resistant. A signing message will map to a message digest via a hash function. In recent years, several chameleon hash functions have been proposed. A chameleon hash function is a trapdoor one-way hash function that prevents everyone except the holder of the trapdoor key from computing the collisions for a randomly given input. There are various studies that apply the chameleon hash function to online/offline digital signatures and sterilization signatures. In this thesis, we apply this concept to a network secure gateway. We have achieved fast blind verification for an application gateway, such as a firewall. Further, we propose triple-trapdoor chameleon hash function and apply to vehicle owenship identification scheme. We have achieved the fast identification for vehicle ownership without connect to online database. We also have proposed threshold chameleon hash function and achieved that the collision will control under the threshold value. The trapdoor information will be exposed after the number of collision has accomplished.

The transportation community is exploring how new "big'' databases constructed by companies or public administrative agencies can be used to better understand travelers' behaviors and better predict travelers' responses to various transportation policies. This thesis explores how a large targeted marketing database containing information about individuals’ socio-demographic characteristics, current residence attributes, and previous residential locations can be used to investigate research questions related to individuals' transportation preferences and the built environment. The first study examines how household vehicle ownership may be shaped by, or inferred from, previous behavior. Results show that individuals who have previously lived in dense ZIP codes or ZIP codes with more non-automobile commuting options are more likely to own fewer vehicles, all else equal. The second study uses autoregressive models that control for spatial dependence, correlation, and endogeneity to investigate whether investments in public transit infrastructure are associated with higher home values. Results show that willingness-to-pay estimates obtained from the general spatial Durbin model are less certain than comparable estimates obtained through ordinary least squares. The final study develops an empirical framework to examine a housing market's resilience to price volatility as a function of transportation accessibility. Two key modeling frameworks are considered. The first uses a spatial autoregressive model to investigate the relationship between a home's value, appreciation, and price stability while controlling for endogenous missing regressors. The second uses a latent class model that considers all these attributes simultaneously, but cannot control for endogeneity.

Shared modes of transport, including car sharing, have been pointed out as one way of reducing private car use, contributing to an efficient transportation system that fulfills societal and environmental goals.Previous studies show that a share of car sharing users sells or refrains from acquire a new vehicle, when entering car sharing. Also, on average, car sharing has been shown to reduce Vehicle Kilometers Traveled (VKT) by car among the users.This study is conducted in three parts. First, a literature review of the effects of car sharing on travel behavior and car ownership is presented. Second, an implementation of car sharing in an existing transport model is described and the estimated effects are analyzed in relation to the findings in the literature study. In the final part, the car sharing module is reformulated to model a station-based car sharing system, where the distances to car sharing vehicles are used to distribute the effect of car sharing on car ownership spatially.This work contributes to the field by connecting the results from previous research about car sharing with practical transport modelling. The model of the station-based car sharing system is a useful tool for planners when considering the placement of car sharing stations. Also, this study provides an updated literature review covering findings of the effects of car sharing on travel behaviour and car ownership.Keywords: car sharing, station-based car sharing, travel demand modelling, vehicle ownership modelling, four-step model

The first part of this thesis relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. The adoption and widespread use of plug-in vehicles will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. Predictions under the two feebate policy scenarios suggest shifts toward fuel-efficient vehicles, but with vehicle miles traveled (VMT) rising slightly (by 0.96% and 1.42%), thanks to lower driving costs. The stricter of the two feebate policies – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Total VMT values under the two feebate scenarios and low-PHEV-pricing scenarios were higher than those under the trend scenario (by 0.56%, 0.96%, and 1.42%, respectively), but only the low-PHEV-pricing scenario delivered higher overall GHG emission estimates (just 0.23% more than trend) in year 2035. The high-density scenario (where job and household densities were quadrupled) resulted in the lowest total vehicle ownership levels, along with below-trend VMT and emissions rates. Finally, the scenario involving a $7,500 rebate on all PHEVs still predicted lower PHEV market share than the $7 gas price scenario (i.e., 2.85% rather than 3.78%). The second part of this thesis relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Results suggest that low-income households respond the most under a $100-per-ton tax but increase GHG emissions under cap-and-trade scenarios, thanks to increased income via sale of their carbon credits. High-income households respond the most across all the scenarios under a 10-ton cap (per household member, per year) and trading at $100 per ton scenario. Highest overall emission reduction (47.2%) was estimated to be under $100 per ton carbon tax. High welfare loss was predicted for all households (to the order of 20% of household income) under both the policies. Results suggest that a carbon tax will be regressive (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work. / text

Climate change, increasing fine dust, changes in values and the accessibility of Carsharing are well discussed topics nowadays in combination with the vehicle ownerships in German households. This paper aims to characterize the vehicle ownership and to evaluate their effects. National and international literature discusses the vehicle ownership in different ways like car ownership as status symbol or the variable ‘vehicle ownership’ as a mediating variable. Basis of this analysis is a survey called ‘SrV - Mobilität in Städten’. The used data contains information about households in the 25 ‘großen SrV-Vergleichsstädte’. This information is available on different levels, which means that the information is available in separate datafiles for levels of ways, persons and households. The basis level for this analysis should be the household level. To get this level it is necessary to aggregate the information. As a result, we get several socioeconomic and alternative specific variables which must be investigated with descriptive and correlation methods in order to prove their suitability for the binary logit model. This model allows it to evaluate metric, nominal and categoric variables with the aim to find characteristics about vehicle ownership. Some results are for example that the vehicle ownership is more probable in households with more persons than in single-person households. Furthermore, the income and missing accessibility of alternatives have a positive effect on vehicle ownership. In addition, this model offers the possibility to predict the vehicle ownership in households. An interesting result is, that some variables have another effect than assumed. These results were compared with the findings of other papers. As a result, one can find some parallel and additional structures.:Abbildungsverzeichnis VII Tabellenverzeichnis IX Abkürzungsverzeichnis XI Symbolverzeichnis XIII 1 Einleitung 1 2 Literaturübersicht 3 3 Methodik 5 3.1 Deskriptive Analyse 5 3.1.1 Lage- und Streumaße 5 3.1.2 Zusammenhangsmaße 5 3.2 Binäre logistische Regression 7 3.2.1 Allgemeines 7 3.2.2 Modellformulierung 8 3.2.3 Schätzung der logistischen Regressionsfunktion 9 3.2.4 Prüfung des Gesamtmodells 10 3.2.5 Prüfung der Merkmalsvariablen 13 3.2.6 Residuen-Analyse 14 3.2.7 Interpretation der Regressionskoeffizienten 15 4 Daten 17 4.1 Datensatz 17 4.2 Aufbereitung der Daten 17 4.2.1 Zusammenhänge in der Multilevelstruktur 18 4.2.2 Wegedaten 18 4.2.3 Personendaten 19 4.2.4 Haushaltsdatei 20 4.3 Datengrundlage 21 5 Deskriptive Analyse 23 5.1 Vorgehen 23 5.2 Streu- und Lagemaße für kardinal skalierte und klassierte Variablen 23 5.2.1 Alternativenspezifische Variablen 23 5.2.2 Sozioökonomische Variablen 27 5.3 Korrelation zwischen den metrischen Variablen 29 5.4 Relative Häufigkeiten kategorialer Variablen 29 5.4.1 Höchste Schulausbildung im Haushalt 30 5.4.2 Höchste Berufsausbildung im Haushalt 30 5.4.3 Geschlecht 30 5.4.4 Altersklassen 31 5.4.5 Erwerbstätigkeit 32 5.5 Nominale Variablen 32 5.6 Beurteilung der Variablen anhand des korrigierten Kontingenzkoeffizienten nach Pearson 34 6 Binäres Logit-Modell 35 6.1 Schätzung der Regressionskoeffizienten 35 6.2 Prüfung des Gesamtmodells 37 6.2.1 Informationskriterien und Log-Likelihood-Wert 37 6.2.2 Likelihood-Ratio-Test 37 6.2.3 Pseudo-R2-Statistiken 37 6.2.4 Klassifizierung neuer Elemente 38 6.2.5 ROC-Kurve 38 6.3 Prüfung der Merkmalsvariablen 39 6.4 Residuen-Analyse 39 6.5 Interpretation und Diskussion der Regressionskoeffizienten 40 6.5.1 Metrische Variablen 40 6.5.2 Nominale Variablen 41 6.5.3 Kategoriale Variablen 42 6.5.4 Konfidenzintervalle 44 7 Fazit 45 8 Diskussion und Literatur 47 9 Kritische Würdigung und Ausblick 49 Anhang XVII Danksagung XXXI / In Zeiten des Klimawandels, erhöhten Feinstaubwerten, geänderten sozialen Wertevorstellungen und der Verfügbarkeit von Carsharing rückt der Pkw-Besitz in Haushalten immer wieder in den Fokus der Berichterstattung. Das Ziel dieser Arbeit ist es, Charakteristika zu finden, die den Pkw-Besitz beschreiben, und deren Wirkungen zu beurteilen. Der Besitz eines Pkws wird in der Literatur auf verschiedene Weise im Hinblick auf die Bedeutung als intervenierende Variable oder als Statussymbol untersucht. Als Grundlage dienen die Daten aus der Umfrage ‚SrV - Mobilität in Städten‘, wobei die Ergebnisse der 25 großen SrV-Vergleichsstädte verwendet werden. Diese Daten besitzen eine sogenannte Multilevelstruktur, das heißt, dass die Daten auf Wegeebene, Personenebene und Haushaltsebene separat vorliegen, wodurch eine Aggregation auf das Haushaltsniveau erforderlich wird. Der sich daraus ergebende Datensatz mit sozioökonomischen und alternativenspezifischen Variablen wird mithilfe deskriptiver Methoden sowie mit Zusammenhangsmaßen auf die Eignung als Variablen für die Anwendung des binären Logit-Modells untersucht, um aussagekräftige Ergebnisse generieren zu können. Mithilfe dieses Modells werden kardinale, kategoriale sowie nominale Variablen betrachtet und bewertet. Daraus lässt sich beispielsweise ableiten, dass der Pkw-Besitz in Haushalten mit zunehmender Personenzahl wahrscheinlicher ist, als bei Singlehaushalten. Auch das Einkommen und der fehlende Zugang zu Alternativen hat einen positiven Einfluss auf den Pkw-Besitz. Das Modell kann neben der Bestimmung der Eigenschaften dazu beitragen, den Pkw-Besitz in Haushalten zu prognostizieren. Interessant dabei ist, dass nicht alle Variablen die erwartete Wirkung entfalten. Die gefundenen Ergebnisse des Modells werden mit Erkenntnissen aus der Literatur verglichen, woraus sich einige Parallelen und Ergänzungen ergeben.:Abbildungsverzeichnis VII Tabellenverzeichnis IX Abkürzungsverzeichnis XI Symbolverzeichnis XIII 1 Einleitung 1 2 Literaturübersicht 3 3 Methodik 5 3.1 Deskriptive Analyse 5 3.1.1 Lage- und Streumaße 5 3.1.2 Zusammenhangsmaße 5 3.2 Binäre logistische Regression 7 3.2.1 Allgemeines 7 3.2.2 Modellformulierung 8 3.2.3 Schätzung der logistischen Regressionsfunktion 9 3.2.4 Prüfung des Gesamtmodells 10 3.2.5 Prüfung der Merkmalsvariablen 13 3.2.6 Residuen-Analyse 14 3.2.7 Interpretation der Regressionskoeffizienten 15 4 Daten 17 4.1 Datensatz 17 4.2 Aufbereitung der Daten 17 4.2.1 Zusammenhänge in der Multilevelstruktur 18 4.2.2 Wegedaten 18 4.2.3 Personendaten 19 4.2.4 Haushaltsdatei 20 4.3 Datengrundlage 21 5 Deskriptive Analyse 23 5.1 Vorgehen 23 5.2 Streu- und Lagemaße für kardinal skalierte und klassierte Variablen 23 5.2.1 Alternativenspezifische Variablen 23 5.2.2 Sozioökonomische Variablen 27 5.3 Korrelation zwischen den metrischen Variablen 29 5.4 Relative Häufigkeiten kategorialer Variablen 29 5.4.1 Höchste Schulausbildung im Haushalt 30 5.4.2 Höchste Berufsausbildung im Haushalt 30 5.4.3 Geschlecht 30 5.4.4 Altersklassen 31 5.4.5 Erwerbstätigkeit 32 5.5 Nominale Variablen 32 5.6 Beurteilung der Variablen anhand des korrigierten Kontingenzkoeffizienten nach Pearson 34 6 Binäres Logit-Modell 35 6.1 Schätzung der Regressionskoeffizienten 35 6.2 Prüfung des Gesamtmodells 37 6.2.1 Informationskriterien und Log-Likelihood-Wert 37 6.2.2 Likelihood-Ratio-Test 37 6.2.3 Pseudo-R2-Statistiken 37 6.2.4 Klassifizierung neuer Elemente 38 6.2.5 ROC-Kurve 38 6.3 Prüfung der Merkmalsvariablen 39 6.4 Residuen-Analyse 39 6.5 Interpretation und Diskussion der Regressionskoeffizienten 40 6.5.1 Metrische Variablen 40 6.5.2 Nominale Variablen 41 6.5.3 Kategoriale Variablen 42 6.5.4 Konfidenzintervalle 44 7 Fazit 45 8 Diskussion und Literatur 47 9 Kritische Würdigung und Ausblick 49 Anhang XVII Danksagung XXXI