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.

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

The motivation behind this research is the need for a real-time implementable, yet accurate, procedure for estimating an origin-destination (O-D) trip-table based on entering and exiting traffic volume data for a given freeway section. These tables help in on-line control of traffic facilities, and consequently, are of significant use in alleviating traffic congestion. The dynamism of the approach captures the variations in the traffic counts with time which are in tum used to predict user travel patterns. Two models have been developed for this problem, one based on a least squares estimation and the other based on an <i>1</i>₁ norm approach. Two projected conjugate gradient schemes are investigated for solving the constrained least squares problem, and an interior point affine scaling algorithm that is applied to the dual problem is explored for solving the <i>1</i>₁ estimation linear programming problem. Computational results are presented on a set of test problems involving the determination of O-D trip tables for both intersection and freeway scenarios in order to demonstrate the viability of the proposed methods. These results exhibit that, unlike as reported in the literature based on previous efforts, properly designed parameter optimization methods can indeed provide accurate estimates in a realtime implementation. Hence, this research presents a competitive alternative to the iterative statistical techniques that have been heretofore used because of their real-time processing capabilities, despite their inherent inaccuracies. We hope that the proposed technology enhances existing methods for constructing O-D trip-tables from traffic counts. / Master of Science

The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the development's negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example. The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITE's Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITE's TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes. This research developed and tested a method to adjust ITE's Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITE's Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories. 195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITE's Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments. For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITE's Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions.

Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation have direct implication for adults’ activity-travel patterns. A better understanding of children’s activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In contrast to the need to examine and model children’s activity-travel patterns, existing activity-based research and modeling systems have almost exclusively focused their attention on the activity-travel patterns of adults. Therefore, the goal of this research effort is to contribute to the area of activity-based travel demand analysis by comprehensively examining children’s activity-travel patterns, and by developing a framework for incorporating children within activity-based travel demand modeling systems. This dissertation provides a comprehensive review of previous research on children’s activity engagement and travel by focusing on the dimensions characterizing children’s activity-travel patterns and the factors affecting these dimensions. Further, an exploratory analysis examines the weekday and weekend activity participation characteristics of school-going children. The study focuses on the overall time-use of children in different types of activities, as well as on several dimensions characterizing the context of participation in activities. In addition, the dissertation discusses the treatment of children within current activity-based travel demand modeling systems and conceptualizes an alternative framework for simulating the daily activity-travel patterns of children. An empirical analysis is undertaken of the post-school out-of-home activity-location engagement patterns of children aged 5 to 17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete-continuous extreme value (MDCEV) model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. Finally, the paper identifies the need and opportunities for further research in the field of children’s travel behavior analysis. / text

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.

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This study examines multiple destination recreation trips and the economic valuation of recreation sites using the travel cost method. One common assumption of the travel cost method is that all travel costs incurred by a visitor are exclusively for a trip to a single site. However, this assumption is often invalid, particularly in the eastern United States where there are numerous recreation areas close to large urban populations. Few researchers have attempted to overcome the difficulty of incorporating multiple destination trips into the travel cost method. Those researchers that have proposed methods have not provided a definitive guideline for how to account for multiple destination trips in the travel cost method, and have not compared their methods. This study proposes a simple model to assist in understanding the varying suggestions by researchers who have attempted to incorporate multiple destination trips into travel cost analyses. The difficulty of defining a recreation good or service, the identification of recreation substitutes, and possible decision processes used by individuals to identify recreation trip destinations are also discussed. Data collected at Shenandoah National Park, Virginia, are used in a zonal travel cost model to estimate the consumers' surplus associated with on-site recreation use at the Park, and to compare proposed methods for handling multiple destination trips. The results of this study show that the travel cost method is sensitive to assumptions about multiple destination visitors, as well as which visitors are included in travel cost analyses. Consumers' surplus estimates ranged from $38 to $8249 per visitor, depending on the assumptions about multiple destination trips, and which visitors were included in the analyses. The results of this study suggest that the travel cost method can be used as an information system, rather than as a method to determine a single estimate of recreation value in monetary terms. The travel cost method is capable of providing a manager with information about relative magnitudes of willingness to pay for a resource by a variety of visitor groups. By varying the assumptions about visitors to the site, a manager can determine a range of consumers' surplus estimates, which may be more useful than a single estimate, to better assist in management decisions regarding the mixture of resources desired by individuals. / Ph. D.

VINNOVA;KTH;URBS / Um conceito bastante em voga atualmente e o de cidades inteligentes. Ele define um tipo de desenvolvimento urbano capaz de reduzir os impactos ambientais, melhorando os modelos atuais de acesso a recursos naturais, transportes, gestão do lixo, climatização residencial e sobretudo a gestão da energia (produção e distribuição). O massivo volume de dados produzidos por cidades inteligentes oferece uma grande oportunidade para analisar, compreender e melhorar o modo como elas funcionam e se desenvolvem. Esta explosão na quantidade de informações tem elevado a importância do aprendizado a partir de dados a um patamar extremamente elevado. Esta dissertação tem por objetivo descrever uma metodologia para aquisição e exploração de dados de um dos mais importantes pilares de cidades inteligentes: o sistema de transporte público. Como obter, armazenar e utilizar tais dados a fim de prover a todos os envolvidos, serviços telemáticos de alto valor agregado e o problema que se busca resolver neste trabalho. Cinco serviços telemáticos são propostos sob forma de prova de conceito: avaliação da cobertura da rede de transporte atual, seguida de uma proposta de novas linhas de ônibus; avaliação indireta da ocupação diária dos ônibus da cidade; cerca-eletrônica com os limites geográficos definidos pelos itinerários das linhas; serviços de alerta de velocidade e de manutenção. Os resultados são bastante coerentes e promissores, abrindo um grande leque de possíveis trabalhos futuros a serem explorados. / Smart city is a very trendy concept today. It defines a type of urban development capable of reducing environmental impacts, enhancing current models of access to natural resources, better transportation systems, waste management, residential climatization and, above all, energy management (production and distribution). The huge data volume produced by smart cities offers a great opportunity to analyze, understand and improve the way cities work and grow. This explosion in the amount of digital information has elevated the importance of learning from data to a higher level. This document aims at describing a methodology for acquiring and exploring data from one of the most important pillars of smart cities: the public transportation system. How to acquire, store and use such data in order to provide to all stakeholders telematics services with high added value is the problem that is sought to solve in this work. Five telematics services proof of concept are proposed: assessment of current network coverage followed by the proposal of some new bus lines; indirect evaluation of buses’ passengers occupation during the day; geofence with geographical boundaries according to itineraries; speed alert and maintenance reminder services. The results are very coherent and promising, opening up a wide range of possible future work to be explored.