High precision data from household travel survey (HTS) is extremely important for the transportation research, traffic models and policy formulation. Traditional methods of data collection were imprecise because they relied on people’s memories of trip information, such as date and location, and the remainder data had to be obtained by certain supplemental tools. The traditional methods suffered from intensive labor, large time consumption, and unsatisfactory data precision. Recent research trends to employ smartphone apps to collect HTS data. In this study, there are two goals to be addressed. First, a smartphone app is developed to realize a smartphone-based method only for data collection. Second, the researcher evaluates whether this method can supply or replace the traditional tools of HTS. Based on this premise, the smartphone app, TravelSurvey, is specially developed and used for this study. TravelSurvey is currently compatible with iPhone 4 or higher and iPhone Operating System (iOS) 6 or higher, except iPhone 6 or iPhone 6 plus and iOS 8. To evaluate the feasibility, eight individuals are recruited to participate in a pilot HTS. Afterwards, seven of them are involved in a semi-structured interview. The interview is designed to collect interviewees’ feedback directly, so the interview mainly concerns the users’ experience of TravelSurvey. Generally, the feedback is positive. In this study, the pilot HTS data is successfully uploaded to the server by the participants, and the interviewees prefer this smartphone-based method. Therefore, as a new tool, the smartphone-based method feasibly supports a typical HTS for data collection.

No description available.

Call number: LD2668 .P7 1976 L34

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Civil Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaf 121. / by Serge Tsygalnitzky. / M.S.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998. / Includes bibliographical references (p. 181-184) and index. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / This study develops a model of a person's day activity schedule that can be used to forecast urban travel demand. It is motivated by the notion that travel outcomes are part of an activity scheduling decision, and uses discrete choice models to address the basic modeling problem-capturing decision interactions among the many choice dimensions of the immense activity schedule choice set. An integrated system of choice models represents a person's day activity schedule as an activity pattern and a set of tours. A pattern model identifies purposes, priorities and structure of the day's activities and travel. Conditional tour models describe timing, location and access mode of on-tour activities. The system captures trade-offs people consider, when faced with space and time constraints, among patterns that can include at-home and on-tour activities, multiple tours and trip chaining. It captures sensitivity of pattern choice to activity and travel conditions through a measure of expected tour utility arising from the tour models. When travel and activity conditions change, the relative attractiveness of patterns changes because expected tour utility changes differently for different patterns. An empirical implementation of the model system for Portland, Oregon, establishes the feasibility of specifying, estimating and using it for forecasting. Estimation results match a priori expectations of lifestyle effects on activity selection, including those of (a) household structure and role, such as for females with children, (b) capabilities, such as income, and (c) activity commitments, such as usual work levels. / (cont.) They also confirm the significance of activity and travel accessibility in pattern choice. Application of the model with road pricing and other policies demonstrates its lifestyle effects and how it captures pattern shifting-with accompanying travel changes-that goes undetected by more narrowly focused trip-based and tour-based systems. Although the model has not yet been validated in before-and-after prediction studies, this study gives strong evidence of its behavioral soundness, current practicality, potential to generate cost-effective predictions superior to those of the best existing systems, and potential for enhanced implementations as computing technology advances. / by John L. Bowman. / Ph.D.

The object of this research is to develop a travel demand model. The model predicts outdoor recreational travel of urban recreationists for camping. The development of this model is structured by a set of methodological criteria. These criteria relate to destination choice behavior in the context of recreation travel, and analytical structures of travel demand, in addition to the criterion of model performance. The thrust of this research is to define and evaluate a destination choice function with respect to recreational travel.

In the 1976, the Institute of Transportation Engineers (ITE) compiled their first Handbook of guidelines and methods for evaluating development-level transportation impacts, specifically vehicular impacts (Institute of Transportation Engineers 1976). Decades later, these methods--essentially the same as when they were originally conceived--are used ubiquitously across the US and Canada. Only recently, with the guidelines in its third edition of the ITE's Trip Generation Handbook (Institute of Transportation Engineers 2014) new data and approaches have been adopted--despite substantial evidence that questions the accuracy of older data, automobile bias, and lack of sensitivity to urban contexts. This dissertation contributes to this literature by focusing on the data, methods, and assumptions so commonly included in development- or site-level evaluation of transportation impacts. These methods are omnipresent in development-level review--used in transportation impact analyses or studies (TIAs/TISs) of vehicular or mode-based impacts, vehicle miles traveled (VMT) and estimates of emissions, scaling or scoping development size, and evaluating transportation system development, impact or utility fees or charges. However, few have evaluated the underlying characteristics of these foundational data--with few exceptions--this manuscript takes aim at understanding inherent issues in the collection and application of ITE's data and methods in various urban contexts. This manuscript includes a compiled dissertation, four papers written consecutively. The first, evaluates state-of-the-art methods in Chapter 2--identifying gaps in the literature. Two such gaps are explored in Chapter 3 and Chapter 4. In Chapter 3, a larger implicit assumption present in ITE's methods--that the existing land-use taxonomy is an optimal and accurate way to describe land use and segment data. Results indicate a simplified taxonomy would provide substantial reductions in cost corresponding with a minor loss in the model's explanation of variance. Following, Chapter 4 explores a common assumption that requires ITE's vehicle trips be converted into person trips and applied across contexts. The results point to the need to consider demographics in site-level transportation impact analysis, particularly to estimate overall demand (person trips, transaction activity) at retail and service development. In Chapter 5, the findings from this research and previous studies are extrapolated to evaluate and quantify the potential bias when temporal, special, and social contexts are ignored. The results indicate the compounding overestimation of automobile demand may inflate estimation by more than 100% in contexts where ITE should be applicable (suburban areas with moderate incomes). In the conclusions (Chapter 6), the implications of this work are explored, followed by recommendations for practice and a discussion of the limitations of this research and future work.

Car dependency which corresponds to excessive and inappropriate usage of the car itself is becoming a major problem all around the world. It shapes urban areas in such a way that the overall urban form as well as individual activities all develop to accommodate cars. However, introducing nonmotorized modes is seen as a remedy to overcome the car dependency. It offers a chance for retrieving healthy urban environments and healthy social lives and also for revitalizing/enhancing public spaces in cities. This means increasing the quality and quantity of pedestrian areas which have been receiving increasing emphasis all around the world. In this thesis, it is intended to show that pedestrianization can be, and should be, a valid transport policy in growing metropolitan areas that are becoming more and more car-oriented, such as Ankara. As a case study, this study assesses potentials and possibilities for creating a pedestrian environment in Ankara, Tunali Hilmi Street. To do so, detailed pedestrian surveys/analyses including v pedestrian counts, pedestrian movement analysis, pedestrian static analysis and pedestrian space analysis throughout the street and questionnaires with potential stakeholders including pedestrians, shop-owners and taxi-drivers are carried out. Through these analyses, it is assessed whether Tunali Hilmi Street is suitable for such an arrangement in terms of pedestrianization or not. As a result, this thesis makes an emphasis on non-motorization and pedestrianization as a method of preventing increasing car-oriented arrangements and revitalizing city centre for the case of Tunali Hilmi Street.

Researchers in multiple areas have shown that characterizing and accounting for the uncertainty inherent in decision support models is critical for developing more efficient planning and operational strategies. This is particularly applicable for the transportation engineering domain as most strategic decisions involve a significant investment of money and resources across multiple stakeholders and has a considerable impact on the society. Moreover, most inputs to transportation models such as travel demand depend on a number of social, economic and political factors and cannot be predicted with certainty. Therefore, in recent times there has been an increasing emphasis being placed on identifying and quantifying this uncertainty and developing models which account for the same. This dissertation contributes to the growing body of literature in tackling uncertainty in transportation models by developing methodologies which address the uncertainty in input parameters in traffic assignment models. One of the primary sources of uncertainty in traffic assignment models is uncertainty in origin destination demand. This uncertainty can be classified into long term and short term demand uncertainty. Accounting for long term demand uncertainty is vital when traffic assignment models are used to make planning decisions like where to add capacity. This dissertation quantifies the impact of long term demand uncertainty by assigning multi-variate probability distributions to the demand. In order to arrive at accurate estimates of the expected future system performance, several statistical sampling techniques are then compared through extensive numerical testing to determine the most "efficient" sampling techniques for network assignment models. Two applications of assignment models, network design and network pricing are studied to illustrate the importance of considering long term demand uncertainty in transportation networks. Short term demand uncertainty such as the day-to-day variation in demand affect traffic assignment models when used to make operational decisions like tolling. This dissertation presents a novel new definition of equilibrium when the short term demand is assumed to follow a probability distribution. Various properties of the equilibrium such as existence, uniqueness and presence of a mathematical programming formulation are investigated. Apart from demand uncertainty, operating capacity in real world networks can also vary from day to day depending on various factors like weather conditions and incidents. With increasing deployment of Intelligent Transportation Systems, users get information about the impact of capacity or the state of the roads through various dissemination devices like dynamic message signs. This dissertation presents a new equilibrium formulation termed user equilibrium with recourse to model information provision and capacity uncertainty, where users learn the state or capacity of the link when they arrive at the upstream node of that link. Depending on the information received about the state of the upstream links, users make different route choice decisions. In this work, the capacity of the links in the network is assumed to follow a discrete probability distribution. A mathematical programming formulation of the user equilibrium with recourse model is presented along with solution algorithm. This model can be extended to analytically model network flows under information provision where the arcs have different cost functional form depending on the state of the arc. The corresponding system optimal with recourse model is also presented where the objective is minimize the total system cost. The network design problem where users are routed according to the user equilibrium with recourse principle is studied. The focus of this study is to show that planning decisions for networks users have access to information is significantly different from the no-information scenario. / text

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xi, 184 p.; also includes graphics (some col.). Includes bibliographical references (p. 158-167). Available online via OhioLINK's ETD Center