This study has two major concerns: the identification of mode choice determinants, and the formulation of a mode choice model which accounts for the determinants defined. First, the concepts underlying users' behaviour are expounded. These concepts relate users' mode choice to several influences, the perceived attributes of the transportation system, his socio-economic characteristics, and sensitivity toward modal attributes. In this study, the latter is hypothesized to be a function of the user's time budget and other indications of his lifestyle. Thus, an individual may attach a great significance to savings in travel time as a result of his engagement in activities which put considerable demand upon his time, although most members of his income group may be sensitive primarily to the travel cost attribute. User's lifestyle may therefore create divergent sensitivities within the same socio-economic group. Previous research findings support the hypothesis that variations in these sensitivities are independent from the. socio-economic characteristics. The study's model makes use of this concept. The model is composed of two parts: the first is concerned with the grouping of all users according to their sensitivities toward attributes of the mode choice situation, and subsequently the calibration of a stochastic function to explain users' choices in each group. The second part of the model relates the user's time, age and occupation (as indications of some aspects of his lifestyle) to these sensitivity, which is an additional step to substantiate the rationale of the model and its predictive quality. The information to be fed into the calibration procedure is to be collected in a questionnaire survey on users' behaviour under choice conditions. The model is therefore a behavioural one; its basic function is to explain the predict users' choices. This approach is different, for example, from the propensity model approach, where users' preferences, rather than behaviour, are the basis for calibration. The model proposed in this study can be applied as a planning tool to demonstrate the impact of various transportation policies on users' choices. The model is capable of providing estimates of the number of users that would be attracted to public transit as a result of, for example, introducing a new transit system, improving the existing level of service, increasing parking charges or gasoline prices. Other applications include the assessment of the impact of introducing novel transportation modes on ridership under the assumption that their attributes are comparable to the existing ones. Also, since the model accounts for certain aspects of the individual's lifestyle, it is possible to link changes in the latter to his mode choice. / Applied Science, Faculty of / Community and Regional Planning (SCARP), School of / Graduate

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Includes bibliographies. / by David Damm. / Ph.D.

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During the past three decades, research in travel behavior has generally proceeded from broad-level, aggregate analysis of mode share--the proportions of walking, bicycling, transit, and vehicle travel occurring in traffic analysis zones, census tracts, neighborhood, or other geographical units--to fine-grained, disaggregate analysis of mode choices and other trip-making attributes at the individual level. One potential issue is whether there are differences in the types of conclusions drawn from results of analyses performed at these different levels, as these results directly inform transportation planning and policy. This thesis aims in part to confirm whether the types of conclusions drawn from different levels of analysis are different, and to what extent. We also examine the relationships between the built environment and non-work travel choices from a unique analysis perspective. To do this, we use data from a 2011 travel intercept survey in the Portland, Oregon metropolitan region that was administered at convenience store, bar, and restaurant establishments. We estimate, for each of the travel modes--walk, bicycle, and automobile--two analysis models: one binary logistic regression model for mode choice of the individual traveler going to the establishment and one multiple linear regression model for mode share of shoppers at the establishment. Both models control for socio-demographics, trip characteristics, and built environment measures of travelers. For the binary logistic regression models, the data are disaggregate and particular to the individual traveler. These models also controlled for attitudes and preference towards travel modes. For the multiple regression models, data are aggregated to the establishment. The built environment data in each model represent characteristics of urban form surrounding the establishment. The data being oriented to the destination-end of the trip, as well as providing controls on land use make this analysis unique in the literature, as most non-work travel studies use residential-based data. Results suggest that analyses performed at the two different levels provide policy-relevant but somewhat different conclusions. In general, characteristics of the individual and the trip have stronger associations with mode choices of individuals than when aggregated to the establishment and analyzed against the mode share patterns of shoppers. Instead, mode shares have stronger relationships with characteristics of the built environment. The built environment surrounding the destination has a much more pronounced association with mode shares at the establishment than with mode choices of individuals. The results highlight the usefulness of simple aggregate analysis, when appropriate. We also find large differences between modes in which characteristics are important for mode choice and mode share. Walking and automobile models behave somewhat similarly but in opposite directions, while bicycling behaves quite differently. These differences suggest on their own a move away from non-motorized travel to be considered as equivalent or assessed as one item in research and in practice.

Organizations specifically the airlines industry are increasingly facing the challenges of operational efficiency measurement. During the last years enormous attention has been given to the assessment and improvement of the performance of productive systems. However, literatures show that there are limitations of the existing models to measure efficiency uniformly and exhaustively across the airlines. The problems are due to lack of the technical efficiency measuring model which unifies and integrates different measuring models into a single model.Therefore, this thesis investigates assessment of the operational performance of world major airlines by employing integrated comparative models to address the above problems. In this study, technical efficiency is addressed among many performance issues by using three types of modes of performance measurement: a non parametric one, represented by Data Envelopment Analysis (DEA) and; a parametric one, represented by Stochastic Frontier Analysis (SFA) and the Balance Scorecard (BSC) which is a strategic management tools. Unlike most of the previous studies, this study integrates the BSC concepts into DEA and SFA model. To evaluate technical efficiency of major international airlines, the study use panel of unbalanced data for the year 2007-2014 to make integrated comparative analysis. The research project incorporates seven leading variables and four lagging variables taken from BSC concept to implement into the DEA and SFA. All the three models of performance measurements have their own strength and limitation if they are used alone. But if the three models are integrated and combined together, they would yield better comparative and quality of efficiency assessment. Therefore, the study primarily developed a model beginning from the theoretical framework assumption into building of a unified comparative model of integrated comparative operational efficiency assessment of airlines. The research design and methodology uses secondary data collection i.e. annual reports and business reports of airlines which are collected from the airlines own website. The huge amount of financial and operational data cannot be collected by using primary data collection method as it would make it practically impossible and expensive. So by employing secondary data collection method saves time, money and a panel data can be accessed and generated easily. Hence, from 100 world major airlines population which are ranked by revenue, simple random sampling is used to select 80 samples airlines for this study. First, the BSC identifies the input and output variables. Next, the DEA model ranks the efficiency measurement, identifies the slack variables and benchmarks the airlines. Third, the SFA model identifies technical efficiency, the random error and technical inefficiency. Finally, the technical efficiency estimates obtained from the two techniques are analyzed comparatively. The research makes further analysis of particular case of the Ethiopian Airlines in relation to the most efficient and inefficient airlines and in comparison of the regional analysis. After extensive tests have been conducted, ‘Balanced Frontier Envelopment’ model is developed. According to this model, it is a paramount to measure efficiency with combining the strength of three models together and gives better results than the previous one or two combined models. The developed and integrated strategic model enhances measuring of the operating technical efficiency of airlines. This model benefit the airlines industry in many ways such as minimizing the cost and maximizing profit through managing technical efficiency which lead into the success of the airlines. From the model perspective, therefore, result of DEA model is much higher than the result of SFA model. DEA model is easier to manipulate than the SFA model because the former does not need the functional form while the later requires a functional form. Furthermore, according to the efficiency finding of the study, first, the European regional airlines are relatively more efficient than the rest of regions in the world. Second, the North America regional airlines are the second more efficient regional airlines in the world. Third, the Ethiopian airlines are the most efficient in Africa when we compare among Egyptair, Kenyan Airways and South African Airways. Fourth, high revenue does not necessarily leads to the technical efficiency of the firm. / Business Management / D.B.L. (Business Leadership)