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

This effort seeks to answer the question; can a transferable model be developed from easily obtainable, publicly available land-use, census, roadway, and network data for the use in safety performance functions? 474 stop-controlled minor legs were used as the training data set using ordinary least squares regression. A best-fit model of maximum independent variables, n=12 was chosen using an exhaustive approach using Mallow's Cp to select the model with least bias in the predictors. The results of the analysis revealed that the combination of variables from Washington, Ohio, and North Carolina did not have a strong relationship. The best-fit model incorporated functional class information of the major-leg, minor leg functional class information, longitudinal markings, access to a parking lot, and population density of census tract. Validation of the model demonstrated an average 59 percent error between the model estimated and actual AADT values for validation data set (n=54). Furthermore, separate models for each state revealed a lack of uniformity in the dependent variables, and more variance description of the state specific AADT.

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.

Submitted in fulfilment of the academic requirements for the degree of Magister Technologiae: Department of Civil Engineering and Surveying, Faculty of Engineering and the Built Environment, Durban University of Technology, 2009. / The annual incidence of pedestrian casualties on South African roads is approximately 34 000. This includes 4 000 deaths, 10 000 serious injuries and 20 000 minor injuries, costing the country an estimated R2,55 billion. A large number of injuries involve children and this is distressing but preventing this is a challenge. Statistics indicate that pedestrians are most at risk. School children have been identified as forming a considerable percentage of pedestrians. Consequently it is important to understand factors that influence children's travel patterns as an initial step toward reducing the accident rate. This study examines children's travel patterns at primary and secondary schools in the eThekwini area. In the absence of statistics regarding journeys to transport children to school, a questionnaire survey was designed to determine demographics, mode of travel to school, travel cost and duration, factors influencing choices of alternate modes of travel and problems experienced during school travel in terms of road safety. In addition, an on-site investigation was undertaken to assess the relevant engineering aspects including geometric design, traffic calming, signage and other traffic management aspects. This study focuses on scholar transport and discusses the findings of pilot and focal surveys. On analyzing the data, various problems were identified, namely: the road environment favours drivers over pedestrians, an absence of a formal travel plan, poor driver behaviour and an absence of dedicated school buses. A range of possible solutions is recommended. The recommendations focus on the Engineering, Enforcement and Evaluation aspects.

Submitted in fulfilment of the academic requirements for the degree of Magister Technologiae: Department of Civil Engineering and Surveying, Faculty of Engineering and the Built Environment, Durban University of Technology, 2009. / The annual incidence of pedestrian casualties on South African roads is approximately 34 000. This includes 4 000 deaths, 10 000 serious injuries and 20 000 minor injuries, costing the country an estimated R2,55 billion. A large number of injuries involve children and this is distressing but preventing this is a challenge. Statistics indicate that pedestrians are most at risk. School children have been identified as forming a considerable percentage of pedestrians. Consequently it is important to understand factors that influence children's travel patterns as an initial step toward reducing the accident rate. This study examines children's travel patterns at primary and secondary schools in the eThekwini area. In the absence of statistics regarding journeys to transport children to school, a questionnaire survey was designed to determine demographics, mode of travel to school, travel cost and duration, factors influencing choices of alternate modes of travel and problems experienced during school travel in terms of road safety. In addition, an on-site investigation was undertaken to assess the relevant engineering aspects including geometric design, traffic calming, signage and other traffic management aspects. This study focuses on scholar transport and discusses the findings of pilot and focal surveys. On analyzing the data, various problems were identified, namely: the road environment favours drivers over pedestrians, an absence of a formal travel plan, poor driver behaviour and an absence of dedicated school buses. A range of possible solutions is recommended. The recommendations focus on the Engineering, Enforcement and Evaluation aspects.

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.