abstract: In the middle of the 20th century in the United States, transportation and infrastructure development became a priority on the national agenda, instigating the development of mathematical models that would predict transportation network performance. Approximately 40 years later, transportation planning models again became a national priority, this time instigating the development of highly disaggregate activity-based traffic models called microsimulations. These models predict the travel on a network at the level of the individual decision-maker, but do so with a large computational complexity and processing time requirement. The vast resources and steep learning curve required to integrate microsimulation models into the general transportation plan have deterred planning agencies from incorporating these tools. By researching the stochastic variability in the results of a microsimulation model with varying random number seeds, this paper evaluates the number of simulation trials necessary, and therefore the computational effort, for a planning agency to reach stable model outcomes. The microsimulation tool used to complete this research is the Transportation Analysis and Simulation System (TRANSIMS). The requirements for initiating a TRANSIMS simulation are described in the paper. Two analysis corridors are chosen in the Metropolitan Phoenix Area, and the roadway performance characteristics volume, vehicle-miles of travel, and vehicle-hours of travel are examined in each corridor under both congested and uncongested conditions. Both congested and uncongested simulations are completed in twenty trials, each with a unique random number seed. Performance measures are averaged for each trial, providing a distribution of average performance measures with which to test the stability of the system. The results of this research show that the variability in outcomes increases with increasing congestion. Although twenty trials are sufficient to achieve stable solutions for the uncongested state, convergence in the congested state is not achieved. These results indicate that a highly congested urban environment requires more than twenty simulation runs for each tested scenario before reaching a solution that can be assumed to be stable. The computational effort needed for this type of analysis is something that transportation planning agencies should take into consideration before beginning a traffic microsimulation program. / Dissertation/Thesis / M.S. Civil Engineering 2010

Regional growth caused the emergence of traffic congestion and pollution in the past few decades, which have started to affect small urban areas. These problems are not only related to transportation system design but also to land use planning. There has been growing recognition that the relationship between land use and transportation needs to be understood and analyzed in a consistent and systematic way. Integrated urban models have recently been introduced and implemented in several metropolitan areas to systematically examine the relationship between land use and transportation. The general consensus in the field of integrated urban models is that each model has its own limitations and assumptions because they are each designed for different application purposes. This dissertation proposes a new type of methodology to integrate the regression-based land use model and the combined trip distribution-assignment transportation model that can be applied to both metropolitan areas and small urban areas. The proposed integrated land use and transportation model framework has three components: the regression-based land use model, the combined trip distributionassignment transportation model, and the interaction between these two models. The combined trip distribution-assignment model framework provides the platform to simultaneously integrate the transportation model with the land use model. The land use model is developed using an easy-to-implement method in terms of correlation and regression analysis. The interaction between the land use model and the transportation model is examined by two model frameworks: feedback model framework and simultaneous model framework. The feedback model framework solves the land use model and the transportation model iteratively. The simultaneous model framework brings the land use model and the transportation models into one optimization program after introducing the used path set. Both the feedback model and the simultaneous model can be solved to estimate link flow, origin-destination (OD) trips, and household distribution with the results satisfying network equilibrium conditions. The proposed integrated model framework has an “affordable and easy-toimplement” land use model; it can be performed in small urban areas with limited resources. The model applications show that using the proposed integrated model framework can help decision-makers and planners in preparing for the future of their communities.

The Utah Department of Transportation (UDOT) manages and maintains a complex state-wide network of highways. Recent incidents such as the collapse of the I-35W bridge in Min- neapolis, Minnesota, and the I-85/Piedmont Road fire and subsequent bridge collapse in Atlanta, Georgia, have brought identification of transportation network vulnerabilities to the forefront of UDOT’s planning efforts. Traditional estimates of transportation network impacts have focused on increases to user travel time or the volume of affected traffic, but studies of these disasters have revealed that when facing a degraded transportation network, people adjust their trip making in terms of destination, mode, and route choice. The objective of this thesis is to evaluate the relative systemic criticality of highway links on Utah’s highway network using a logit-based model sensitive to changes in destination choice, mode choice, and route path. The current Utah Statewide Travel Model (USTM) does not incorporate user mode or destination choice, making it unsuitable for this task in its present condition. Consequently, this thesis develops a logit-based model structure that evaluates the cost of impaired destination choices and mode choices for home-based and non-home-based personal trips resulting from a damaged highway network. The choice model logsums capture the total value of user choices and can be readily converted to monetary values, making them ideal for this purpose. The logit-based model is then applied to 40 highway links located at strategic locations on Utah’s network. When compared with a more traditional travel time increase estimation, the logsum and travel time models provide categorically different cost estimates, where the logsum results are typically lower than travel time estimates, with implications for policy making and UDOT’s planning strategy. The results further suggest that freight trips are likely more important considerations than passenger trips, and should be considered in future research.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18256号 / 工博第3848号 / 新制||工||1590(附属図書館) / 31114 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 教授 谷口 栄一, 准教授 松島 格也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Orientador: Rubén Augusto Romero Lázaro / Resumo: A análise de sistemas altamente complexos quando e analizado o problema de planejamento de expansão de redes de transmissão de longo prazo, é o foco principal deste trabalho. Os modelos e metodos propostos são aplicados ao problema de planejamento estático tradicional, que é um problema de otimização matemática classificado como NP-completo, não-linear inteiro misto. O qual envolve no investimento, variáveis operacionais contínuas e variáveis inteiras. O comportamento normal de cada sistema pode conter informação essencial para a criação de novos métodos, como os planos de corte baseados em cortes de diferença de ângulos para problemas de grande escala, o que é a base é o ponto de partida deste trabalho, derivando em desigualdades válidas é ciclos críticos. Os cortes angulares básicos reduzem o espaço de busca do problema e o tempo total de cálculo deste problema, enquanto ao método de inequações válidas que pode ser usado para fornecer limites inferiores sólidos no investimento ótimo do planejamento de transmissão, já que a diferença entre o modelo DC (modelo exato) e o modelo de transporte (modelo mais relaxado) são as restrições angulares. Os ciclos críticos têm sido desenvolvidos para melhoraralguns dos modelos tradicionais do problemas de planejamento da expansão da rede de transmissão de longo prazo. A razão por trás disso é a ausência da segunda lei de Kirchhoff, que completa a representação do sistema, mas aumenta a complexidade. Para resolver os problemas resultantes... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The analysis of highly complex systems when solving the long-term transmission network expansion planning problem is the main focus of this work. The proposed improved models and methodology are applied to the traditionalstatic planning problem, which is a mathematical optimization problem classified as NP-complete and mixed-integer nonlinear problem. It involves continuousoperating variables and integer investment variables. The normal behavior of each system can be shown essential information to the creation of new methods, as the cutting-planes based in bus-angle difference cuts for large-scale problems which were the starting point of this work, deriving in valid inequalities and critic cycles. The angular cuts aim to reduce the search space of the problem and the total computation time of this NP-hard problem as for the valid inequalities methodthat can be used to provide strong lower bounds on the optimal investment of the transmissionplanning, since the difference between the DC model (exact model) and the transport model (more relaxed model) are the angular constraints. Critic cycles has been develop in order to improve some of the traditional long-term transmission network expansion planning problem models. The reason behind it is the absence of second Kirchhoff’s law which completes the representationof the system, but increase the complexity. In order to solve the resulting problems, this work uses the modeling language AMPL with the solver CPLEX. In test systems w... (Complete abstract click electronic access below) / Doutor

Diese Arbeit stellt grundlegend zwei Modellentwicklungen der Verkehrsangebots- und Verkehrsnachfragemodellierung vor. Die erste Entwicklung bezieht sich auf die simultane Ziel- und Verkehrsmittelwahl in Abhängigkeit von Strukturgrößen und/oder Lagegunst. Es werden neue Randsummenbedingungen beschrieben und vorhandene neu definiert. Der neue Modellansatz erlaubt eine Bestimmung minimaler und maximaler Verkehrsaufkommen und stellt eine Erweiterung der theoretischen Grundlagen als auch der praktischen Anwendbarkeit dar. So können alle derzeit bekannten Randsummenbedingungen durch einen Algorithmus (auch innerhalb einer Quelle-Ziel-Gruppe) berechnet werden. Der zweite Ansatz ist ein Werkzeug, welches in Abhängigkeit des vorhandenen Verkehrsangebotes verkehrsplanerisch wünschenswerte quantitative Flächen- bzw. Gebietsnutzungen abschätzt. Aufbauend auf der Verkehrsangebots- und Verkehrsnachfragemodellierung werden Infrastrukturgrößen durch eine aufzustellende Zielfunktion (z. B. minimale Verkehrsarbeit), unter Beachtung vorhandener Freiheitsgrade der Flächennutzung je Verkehrsbezirk, optimiert. Diese Freiheitsgrade werden als minimale und maximale Strukturgrößengrenzen durch die Raum- und Stadtplanung definiert, womit sie den vielfältigen Einflussgrößen dieser Planungen unterliegen und dadurch städtebaulich verträglich sind. Der Modellansatz bildet die für die Infrastrukturplanung wichtigen Wechselwirkungen des durch den Stadt- und Verkehrsplaner angestrebten Systemoptimums (Infrastrukturgrößenverteilung eines Gebietes) mit dem durch den einzelnen Verkehrsteilnehmer angestrebten Nutzenoptimum (Verkehrsnachfrage) ab. / This work basically introduces two model developments of traffic supply and traffic demand modeling. The first development refers to the simultaneous destination and mode choice into dependence of structure sizes and/or accessibility. New constraints are described and available constraints were defined newly. The new model enables the determination of minimal and maximum volumes of traffic (constraints). The new explanatory model is an expansion of the theoretical bases and the practical applicability. So all currently known constraints can be calculated by one algorithm (also within an origin destination group). The second approach is a tool which describes desirable quantitative traffic planningly land uses against the available traffic supply. It uses an algorithm that keeps minimal and maximum structure size limits while it determining e.g. minimal traffic work. Within the algorithm the complete traffic demand will be calculated. The complete model shows the important interactions of the infrastructure planning by the town and transport planer (a striven system optimum) with the traffic demand by the single road user (a striven user equilibrium).

Orientador: Rubén Augusto Romero Lázaro / Resumo: Neste trabalho, consideram-se a análise teórica e a implementação computacional dos algoritmos Primal Simplex Canalizado (PSC) e Dual Simplex Canalizado (DSC) especializados. Esses algoritmos foram incorporados em um algoritmo Branch and Bound (B&B) de modo a resolver o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST). Neste caso, o problema PEST foi modelado usando os chamados modelo de Transportes e modelo Linear Disjuntivo (LD), o que produz um problema de Programação Linear Inteiro Misto (PLIM). O algoritmo PSC é utilizado na resolução do problema de Programação Linear (PL) inicial após desconsiderar a restrição de integralidade do problema PLIM original. Juntamente com o algoritmo PSC, foi implementada uma estratégia para reduzir o número de variáveis artificiais adicionadas ao PL, consequentemente reduzindo o número de iterações do algoritmo PSC. O algoritmo DSC é utilizado na reotimização eficiente dos subproblemas gerados pelo algoritmo B&B, através do quadro ótimo do PL inicial, excluindo, assim, a necessidade da resolução completa de cada subproblema e, consequentemente, reduzindo o consumo de processamento e memória. Nesta pesquisa, é apresentada uma nova proposta de otimização, e, consequentemente, a implementação computacional usando a linguagem de programação FORTRAN que opera independentemente de qualquer solver. / Doutor

Submitted by JEFERSON BACK VANDERLINDE null (jefersonbv@yahoo.com.br) on 2017-11-01T16:38:25Z No. of bitstreams: 1 jeferson_tese_final_20171101.pdf: 4860852 bytes, checksum: 2f99c37969be3815f82b1b4455a40230 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-11-13T15:38:34Z (GMT) No. of bitstreams: 1 vanderlinde_jb_dr_ilha.pdf: 4860852 bytes, checksum: 2f99c37969be3815f82b1b4455a40230 (MD5) / Made available in DSpace on 2017-11-13T15:38:34Z (GMT). No. of bitstreams: 1 vanderlinde_jb_dr_ilha.pdf: 4860852 bytes, checksum: 2f99c37969be3815f82b1b4455a40230 (MD5) Previous issue date: 2017-09-06 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho, consideram-se a análise teórica e a implementação computacional dos algoritmos Primal Simplex Canalizado (PSC) e Dual Simplex Canalizado (DSC) especializados. Esses algoritmos foram incorporados em um algoritmo Branch and Bound (B&B) de modo a resolver o problema de Planejamento da Expansão de Sistemas de Transmissão (PEST). Neste caso, o problema PEST foi modelado usando os chamados modelo de Transportes e modelo Linear Disjuntivo (LD), o que produz um problema de Programação Linear Inteiro Misto (PLIM). O algoritmo PSC é utilizado na resolução do problema de Programação Linear (PL) inicial após desconsiderar a restrição de integralidade do problema PLIM original. Juntamente com o algoritmo PSC, foi implementada uma estratégia para reduzir o número de variáveis artificiais adicionadas ao PL, consequentemente reduzindo o número de iterações do algoritmo PSC. O algoritmo DSC é utilizado na reotimização eficiente dos subproblemas gerados pelo algoritmo B&B, através do quadro ótimo do PL inicial, excluindo, assim, a necessidade da resolução completa de cada subproblema e, consequentemente, reduzindo o consumo de processamento e memória. Nesta pesquisa, é apresentada uma nova proposta de otimização, e, consequentemente, a implementação computacional usando a linguagem de programação FORTRAN que opera independentemente de qualquer solver. / In this research, the theoretical analysis and computational implementation of the specialized dual simplex algorithm (DSA) and primal simplex algorithm (PSA) for bounded variables is considered. These algorithms have been incorporated in a Branch and Bound (B&B) algorithm to solve the Transmission Network Expansion Planning (TNEP) problem. In this case, the TNEP problem is modeled using transportation model and linear disjunctive model (DM), which produces a mixed-integer linear programming (MILP) problem. After relaxing the integrality of investment variables of the original MILP problem, the PSA is used to solve the initial linear programming (LP) problem. Also, it has been implemented a strategy in PSA to reduce the number of artificial variables which are added into the LP problem, and consequently reduces the number of iterations of PSA. Through optimal solution of the initial LP, the DSA is used in efficient reoptimization of subproblems, resulting from the B&B algorithm, thus excludes the need for complete resolution of each subproblems, which results reducing the CPU time and memory consumption. This research presents the implementation of the proposed approach using the FORTRAN programming language which operates independently and does not use any commercial solver.

Diese Arbeit stellt grundlegend zwei Modellentwicklungen der Verkehrsangebots- und Verkehrsnachfragemodellierung vor. Die erste Entwicklung bezieht sich auf die simultane Ziel- und Verkehrsmittelwahl in Abhängigkeit von Strukturgrößen und/oder Lagegunst. Es werden neue Randsummenbedingungen beschrieben und vorhandene neu definiert. Der neue Modellansatz erlaubt eine Bestimmung minimaler und maximaler Verkehrsaufkommen und stellt eine Erweiterung der theoretischen Grundlagen als auch der praktischen Anwendbarkeit dar. So können alle derzeit bekannten Randsummenbedingungen durch einen Algorithmus (auch innerhalb einer Quelle-Ziel-Gruppe) berechnet werden. Der zweite Ansatz ist ein Werkzeug, welches in Abhängigkeit des vorhandenen Verkehrsangebotes verkehrsplanerisch wünschenswerte quantitative Flächen- bzw. Gebietsnutzungen abschätzt. Aufbauend auf der Verkehrsangebots- und Verkehrsnachfragemodellierung werden Infrastrukturgrößen durch eine aufzustellende Zielfunktion (z. B. minimale Verkehrsarbeit), unter Beachtung vorhandener Freiheitsgrade der Flächennutzung je Verkehrsbezirk, optimiert. Diese Freiheitsgrade werden als minimale und maximale Strukturgrößengrenzen durch die Raum- und Stadtplanung definiert, womit sie den vielfältigen Einflussgrößen dieser Planungen unterliegen und dadurch städtebaulich verträglich sind. Der Modellansatz bildet die für die Infrastrukturplanung wichtigen Wechselwirkungen des durch den Stadt- und Verkehrsplaner angestrebten Systemoptimums (Infrastrukturgrößenverteilung eines Gebietes) mit dem durch den einzelnen Verkehrsteilnehmer angestrebten Nutzenoptimum (Verkehrsnachfrage) ab. / This work basically introduces two model developments of traffic supply and traffic demand modeling. The first development refers to the simultaneous destination and mode choice into dependence of structure sizes and/or accessibility. New constraints are described and available constraints were defined newly. The new model enables the determination of minimal and maximum volumes of traffic (constraints). The new explanatory model is an expansion of the theoretical bases and the practical applicability. So all currently known constraints can be calculated by one algorithm (also within an origin destination group). The second approach is a tool which describes desirable quantitative traffic planningly land uses against the available traffic supply. It uses an algorithm that keeps minimal and maximum structure size limits while it determining e.g. minimal traffic work. Within the algorithm the complete traffic demand will be calculated. The complete model shows the important interactions of the infrastructure planning by the town and transport planer (a striven system optimum) with the traffic demand by the single road user (a striven user equilibrium).