With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using a Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand.

In transportation planning, one of the first steps is to estimate the travel demand. A product of the estimation process is the so-called origin-destination matrix (OD matrix), whose entries correspond to the number of trips between pairs of zones in a geographic region in a reference time period. Traditionally, the OD matrix has been estimated through direct methods, such as home-based surveys, road-side interviews and license plate automatic recognition. These direct methods require large samples to achieve a target statistical error, which may be technically or economically infeasible. Alternatively, one can use a statistical model to indirectly estimate the OD matrix from observed traffic volumes on links of the transportation network. The first estimation models proposed in the literature assume that traffic volumes in a sequence of days are independent and identically distributed samples of a static probability distribution. Moreover, static estimation models do not allow for variations in mean OD flows or non-constant variability over time. In contrast, day-to-day dynamic models are in theory more capable of capturing underlying changes of system parameters which are only indirectly observed through variations in traffic volumes. Even so, there is still a dearth of statistical models in the literature which account for the day-today dynamic evolution of transportation systems. In this thesis, our objective is to assess the potential gains and limitations of day-to-day dynamic models for the estimation of the OD matrix based on link volumes. First, we review the main static and dynamic models available in the literature. We then describe our proposed day-to-day dynamic Bayesian model based on the theory of linear dynamic models. The proposed model is tested by means of computational experiments and compared with a static estimation model and with the generalized least squares (GLS) model. The results show some advantage in favor of dynamic models in informative scenarios, while in non-informative scenarios the performance of the models were equivalent. The experiments also indicate a significant dependence of the estimation errors on the assignment matrices. / In transportation planning, one of the first steps is to estimate the travel demand. A product of the estimation process is the so-called origin-destination matrix (OD matrix), whose entries correspond to the number of trips between pairs of zones in a geographic region in a reference time period. Traditionally, the OD matrix has been estimated through direct methods, such as home-based surveys, road-side interviews and license plate automatic recognition. These direct methods require large samples to achieve a target statistical error, which may be technically or economically infeasible. Alternatively, one can use a statistical model to indirectly estimate the OD matrix from observed traffic volumes on links of the transportation network. The first estimation models proposed in the literature assume that traffic volumes in a sequence of days are independent and identically distributed samples of a static probability distribution. Moreover, static estimation models do not allow for variations in mean OD flows or non-constant variability over time. In contrast, day-to-day dynamic models are in theory more capable of capturing underlying changes of system parameters which are only indirectly observed through variations in traffic volumes. Even so, there is still a dearth of statistical models in the literature which account for the day-today dynamic evolution of transportation systems. In this thesis, our objective is to assess the potential gains and limitations of day-to-day dynamic models for the estimation of the OD matrix based on link volumes. First, we review the main static and dynamic models available in the literature. We then describe our proposed day-to-day dynamic Bayesian model based on the theory of linear dynamic models. The proposed model is tested by means of computational experiments and compared with a static estimation model and with the generalized least squares (GLS) model. The results show some advantage in favor of dynamic models in informative scenarios, while in non-informative scenarios the performance of the models were equivalent. The experiments also indicate a significant dependence of the estimation errors on the assignment matrices. / In transportation planning, one of the first steps is to estimate the travel demand. A product of the estimation process is the so-called origin-destination matrix (OD matrix), whose entries correspond to the number of trips between pairs of zones in a geographic region in a reference time period. Traditionally, the OD matrix has been estimated through direct methods, such as home-based surveys, road-side interviews and license plate automatic recognition. These direct methods require large samples to achieve a target statistical error, which may be technically or economically infeasible. Alternatively, one can use a statistical model to indirectly estimate the OD matrix from observed traffic volumes on links of the transportation network. The first estimation models proposed in the literature assume that traffic volumes in a sequence of days are independent and identically distributed samples of a static probability distribution. Moreover, static estimation models do not allow for variations in mean OD flows or non-constant variability over time. In contrast, day-to-day dynamic models are in theory more capable of capturing underlying changes of system parameters which are only indirectly observed through variations in traffic volumes. Even so, there is still a dearth of statistical models in the literature which account for the day-today dynamic evolution of transportation systems. In this thesis, our objective is to assess the potential gains and limitations of day-to-day dynamic models for the estimation of the OD matrix based on link volumes. First, we review the main static and dynamic models available in the literature. We then describe our proposed day-to-day dynamic Bayesian model based on the theory of linear dynamic models. The proposed model is tested by means of computational experiments and compared with a static estimation model and with the generalized least squares (GLS) model. The results show some advantage in favor of dynamic models in informative scenarios, while in non-informative scenarios the performance of the models were equivalent. The experiments also indicate a significant dependence of the estimation errors on the assignment matrices.

<p>The principle of load balancing is to distribute the data load more evenly over the network in order to increase the network performance and efficiency. With dynamic load balancing the routing is undated at certain intervals. This thesis was developed to evaluate load balancing methods in the IP-network of TeliaSonera.Load balancing using short path routing, bottleneck load balancing and load balancing using MPLS have been evaluated. Short path routing is a flow sharing technique that allows routing on paths other than the shortest one. </p><p>Load balancing using short path routing is achieved by dynamic updates of the link weights. Bottleneck is in its nature a dynamic load balancing algorithm. Unlike load balancing using short path routing it updates the flow sharing, not the metrics. The algorithm uses information about current flow sharing and link loads to detect bottlenecks within the network. The information is used to calculate new flow sharing parameters. When using MPLS, one or more complete routing paths (LSPs) are defined at each edge LSR before sending any traffic. MPLS brings the ability to perform flow sharing by defining the paths to be used and how the outgoing data load is to be shared among these.</p><p>The model has been built from data about the network supplied by TeliaSonera. The model consists of a topology part, a traffic part, a routing part and cost part. The traffic model consists of a OD demand matrix. The OD demand matrix has been estimated from collected link loads. This was done with estimation models; the gravity model and an optimisation model.</p><p>The algorithms have been analysed at several scenarios; normal network, core node failure, core link failure and DWDM system failure. A cost function, where the cost increases as the link load increases has been used to evaluate the algorithms. The signalling requirements for implementation of the load balancing algorithm have also been investigated.</p>

The principle of load balancing is to distribute the data load more evenly over the network in order to increase the network performance and efficiency. With dynamic load balancing the routing is undated at certain intervals. This thesis was developed to evaluate load balancing methods in the IP-network of TeliaSonera.Load balancing using short path routing, bottleneck load balancing and load balancing using MPLS have been evaluated. Short path routing is a flow sharing technique that allows routing on paths other than the shortest one. Load balancing using short path routing is achieved by dynamic updates of the link weights. Bottleneck is in its nature a dynamic load balancing algorithm. Unlike load balancing using short path routing it updates the flow sharing, not the metrics. The algorithm uses information about current flow sharing and link loads to detect bottlenecks within the network. The information is used to calculate new flow sharing parameters. When using MPLS, one or more complete routing paths (LSPs) are defined at each edge LSR before sending any traffic. MPLS brings the ability to perform flow sharing by defining the paths to be used and how the outgoing data load is to be shared among these. The model has been built from data about the network supplied by TeliaSonera. The model consists of a topology part, a traffic part, a routing part and cost part. The traffic model consists of a OD demand matrix. The OD demand matrix has been estimated from collected link loads. This was done with estimation models; the gravity model and an optimisation model. The algorithms have been analysed at several scenarios; normal network, core node failure, core link failure and DWDM system failure. A cost function, where the cost increases as the link load increases has been used to evaluate the algorithms. The signalling requirements for implementation of the load balancing algorithm have also been investigated.

La presente investigación modela el sistema BRT Metropolitano mediante el uso del software TransCAD. Para la adecuada construcción de la matriz origen destino se recopilan datos de embarque y desembarque en los diferentes paraderos del sistema. Es por ello, que se calcula la muestra de encuestas a través de datos proporcionados por Protransporte, la empresa que administra el Metropolitano. En la información proporcionada se excluyeron días atípicos donde se obtuvo 1032 encuestas con una confiabilidad de 95% y un error de 4.2%. Luego, se usa el factor de expansión para obtener la situación actual con un total de 53,776 usuarios que viajan en la hora más crítica de la tarde de 6 a 7pm. Así mismo, se inserta la matriz OD para la asignación de viajes mediante el método de equilibrio de usuarios estocástico (SUE); en el cual, se obtiene una primera calibración de la situación actual. Posteriormente, se propone el escenario más óptimo a partir del cálculo de las cargas máximas con el uso de hojas de cálculo. Además, se obtienen nuevos parámetros como frecuencias, cantidades de buses, tiempo de viaje total, etc. Finalmente, se realiza la comparación de los resultados del escenario propuesto y actual donde se observan beneficios para el operador y los usuarios. Debido a que, la propuesta cuenta con un ahorro de 7,154.26 minutos de tiempo de viaje total por hora, la operación del sistema con quince buses menos y un índice de pasajeros por kilómetro recorrido (IPK) de cinco puntos adicionales al actual. / The present investigation models the BRT Metropolitano system through the use of TransCAD software. For the proper construction of the destination origin matrix, embarkation and disembarkation data must be collected in the different stops of the system. That is why the sample of surveys is calculated through data provided by Protransporte, the company that manages the Metropolitano. In the information provided, atypical days were excluded where 1,032 surveys were obtained with a 95% reliability and an error of 4.2%. Then, the expansion factor is used to obtain the current situation with a total of 53,776 users traveling in the most critical time of the afternoon from 6 to 7pm. Likewise, the OD matrix is inserted as a previous step to the travel assignment using the stochastic user equilibrium method (SUE), in which a first calibration of the current situation will be obtained. Subsequently, the most optimal scenario is proposed based on the calculation of the maximum loads with the use of spreadsheets. In addition, new parameters such as frequencies, bus numbers, total travel time, and various others are obtained. Finally, the results of the proposed scenario and the current one were compared, where benefits for the operator and the users of the system are observed. Since the proposal has a saving of 7,154.26 minutes of total travel time per hour, the operation of the system with fifteen fewer buses and a passenger index per kilometer traveled (IPK) five points more than the current one. / Tesis

No description available.

A meta do trabalho é propor e testar a hipótese que a contagem de tráfego nas interseções da rede de transportes, ao invés de contagem de tráfego nos arcos, reduz o grau de indeterminação e torna mais precisa a matriz OD estimada pelo modelo sintético. Ademais, é proposto e detalhado um método de estimação da matriz OD através de médias sucessivas (MEMS). É apresentada a descrição matemática das propostas e o detalhamento dos experimentos elaborados para testá-las. Três métodos de estimação, QUEENSOD, TransCAD e MEMS, foram utilizados na verificação da hipótese. A inserção de \"arcos virtuais\" na rede de transportes constituiu um artifício que permitiu aos programas QUEENSOD e TransCAD realizarem a estimação utilizando fluxos observados nas interseções. A utilização de contagens de fluxo nas interseções propiciou à matriz OD estimada, melhorias que acarretaram sua aproximação com a matriz OD \"real\". O experimento mostrou que a matriz OD estimada ao considerar contagens de tráfego nas interseções apresenta melhor desempenho em comparação a matriz estimada ao considerar contagens nos arcos da rede de transportes. A matriz estimada gradativamente aproximou-se da \"real\" à medida que foi aumentada a quantidade de informação de fluxo e sua distribuição na rede. Assim, a hipótese formulada para este trabalho não pôde ser refutada. / The aim of this work is to propose and test the hypothesis that traffic counts collected at network intersections, instead of traffic counts collected at links, reduce indeterminacy and make more accurate the OD matrix estimated by the synthetic model. Furthermore, a method is proposed and described in detail to estimate the OD matrix based on successive averages (MEMS). The model formulation of the proposals and a description of the experiments are presented. Three estimation methods, QUEENSOD, TransCAD, and MEMS were used in the hypothesis verification. The use of \"virtual links\" in the network consists of an artifice that enable the QUEENSOD and TransCAD to estimate the OD matrix based on traffic counts at intersection. By using flow counts conducted at intersections, improvements could be made to the estimated OD matrix bringing it closer to \"real situations\". The experiments results show that the OD matrix estimation based on traffic counts collected on network intersection has a better performance in contrast to the estimation based on traffic counts collected on network links. The estimated matrix gradually becomes closer to \"real situations\" while the quantity of information flow and its distribution to the network is increased. Therefore, the formulated hypothesis for this work cannot be refuted.

Esta pesquisa apresenta uma proposta de reconstrução de matriz OD a partir de dados de velocidade da corrente de tráfego disponíveis na web. Para tal, foram estabelecidos três objetivos específicos, os quais incluem: obtenção e avaliação dos dados de velocidade de tráfego; construção de um modelo computacional e aplicação desse em uma rede real. A avaliação dos dados foi realizada por meio da comparação dos valores de velocidade média da corrente de tráfego obtidos da web com os valores aferidos por sensores do tipo laço indutivo. Os resultados indicam que, apesar das velocidades extraídas da web apresentarem maior variância e valores mais baixos, a maior parcela dos dados obteve diferença similar ou inferior a 11%. Na construção da rotina computacional foi utilizado o Método das Médias Sucessivas (MEMS) e na sua avaliação foram realizados testes comparativos cujas referências foram os resultados alcançados pelo autor do método utilizado. A rotina desenvolvida apresentou melhor resultado com relação ao número total de viagens da matriz reconstruída, além de exigir menor número de iterações. Por outro lado, com relação às viagens atribuídas a cada par OD seu desempenho foi inferior. Para avaliar a aderência a realidade o método proposto foi aplicado a uma rede real composta por trechos específicos das rodovias Presidente Dutra, Pedro I, Ayrton Senna e Governador Carvalho Pinto. Devido ao fato da entrada principal do MEMS ser o volume de veículos, foi necessário estimar o mesmo a partir da velocidade fornecida pela API. A matriz reconstruída apresentou forte dependência da capacidade atribuída às vias, evidenciando a necessidade de definir de forma mais cuidadosa esse parâmetro. Embora, ainda seja necessário aprimorar o método proposto e a rotina computacional elaborada, bem como, aprofundar os estudos da fonte de dados utilizada, o experimento de caráter exploratório mostra a possibilidade de reconstruir uma matriz OD a partir de dados disponíveis na web. / This research presents a proposal for the reconstruction of OD matrix from traffic stream velocity data available on the web. To this end, three specific objectives have been established, which include: obtaining and evaluating traffic speed data; construction of a computational model and application of this in a real network. The data were evaluated by the comparison of the mean values of the traffic current, obtained from the web, with those measured by loop sensors. The results indicate that, although the velocities extracted from the web presented higher variance and lower values, the greater part of the data obtained a difference similar to or lower than 11%. In the construction of the computational routine was used the Method of Successive Averages (MEMS) and in its evaluation were performed comparative tests whose references were the results achieved by the author of the used method. The developed routine presented better results in relation to the total number of trips of the reconstructed OD matrix, besides requiring a smaller number of iterations. On the other hand, in relation to the trips attributed to each OD pair the performance was lower. To evaluate the adherence to reality, the proposed method was applied to a real network composed of specific sections of the Presidente Dutra, Pedro I, Ayrton Senna and Governador Carvalho Pinto highways. Due to the fact that the main input of the MEMS is the vehicles flow, was necessary to estimate it from the speed provided by the API. The reconstructed matrix presented a strong dependence on the capacity attributed to the roads, evidencing the need to define this parameter more carefully. Although it is still necessary to improve the proposed method and the elaborated computational routine, as well as to deepen the studies of the used data source, the exploratory experiment shows the possibility of reconstructing an OD matrix from data available on the web.

A meta do trabalho é propor e testar a hipótese que a contagem de tráfego nas interseções da rede de transportes, ao invés de contagem de tráfego nos arcos, reduz o grau de indeterminação e torna mais precisa a matriz OD estimada pelo modelo sintético. Ademais, é proposto e detalhado um método de estimação da matriz OD através de médias sucessivas (MEMS). É apresentada a descrição matemática das propostas e o detalhamento dos experimentos elaborados para testá-las. Três métodos de estimação, QUEENSOD, TransCAD e MEMS, foram utilizados na verificação da hipótese. A inserção de \"arcos virtuais\" na rede de transportes constituiu um artifício que permitiu aos programas QUEENSOD e TransCAD realizarem a estimação utilizando fluxos observados nas interseções. A utilização de contagens de fluxo nas interseções propiciou à matriz OD estimada, melhorias que acarretaram sua aproximação com a matriz OD \"real\". O experimento mostrou que a matriz OD estimada ao considerar contagens de tráfego nas interseções apresenta melhor desempenho em comparação a matriz estimada ao considerar contagens nos arcos da rede de transportes. A matriz estimada gradativamente aproximou-se da \"real\" à medida que foi aumentada a quantidade de informação de fluxo e sua distribuição na rede. Assim, a hipótese formulada para este trabalho não pôde ser refutada. / The aim of this work is to propose and test the hypothesis that traffic counts collected at network intersections, instead of traffic counts collected at links, reduce indeterminacy and make more accurate the OD matrix estimated by the synthetic model. Furthermore, a method is proposed and described in detail to estimate the OD matrix based on successive averages (MEMS). The model formulation of the proposals and a description of the experiments are presented. Three estimation methods, QUEENSOD, TransCAD, and MEMS were used in the hypothesis verification. The use of \"virtual links\" in the network consists of an artifice that enable the QUEENSOD and TransCAD to estimate the OD matrix based on traffic counts at intersection. By using flow counts conducted at intersections, improvements could be made to the estimated OD matrix bringing it closer to \"real situations\". The experiments results show that the OD matrix estimation based on traffic counts collected on network intersection has a better performance in contrast to the estimation based on traffic counts collected on network links. The estimated matrix gradually becomes closer to \"real situations\" while the quantity of information flow and its distribution to the network is increased. Therefore, the formulated hypothesis for this work cannot be refuted.

Esta pesquisa apresenta uma proposta de reconstrução de matriz OD a partir de dados de velocidade da corrente de tráfego disponíveis na web. Para tal, foram estabelecidos três objetivos específicos, os quais incluem: obtenção e avaliação dos dados de velocidade de tráfego; construção de um modelo computacional e aplicação desse em uma rede real. A avaliação dos dados foi realizada por meio da comparação dos valores de velocidade média da corrente de tráfego obtidos da web com os valores aferidos por sensores do tipo laço indutivo. Os resultados indicam que, apesar das velocidades extraídas da web apresentarem maior variância e valores mais baixos, a maior parcela dos dados obteve diferença similar ou inferior a 11%. Na construção da rotina computacional foi utilizado o Método das Médias Sucessivas (MEMS) e na sua avaliação foram realizados testes comparativos cujas referências foram os resultados alcançados pelo autor do método utilizado. A rotina desenvolvida apresentou melhor resultado com relação ao número total de viagens da matriz reconstruída, além de exigir menor número de iterações. Por outro lado, com relação às viagens atribuídas a cada par OD seu desempenho foi inferior. Para avaliar a aderência a realidade o método proposto foi aplicado a uma rede real composta por trechos específicos das rodovias Presidente Dutra, Pedro I, Ayrton Senna e Governador Carvalho Pinto. Devido ao fato da entrada principal do MEMS ser o volume de veículos, foi necessário estimar o mesmo a partir da velocidade fornecida pela API. A matriz reconstruída apresentou forte dependência da capacidade atribuída às vias, evidenciando a necessidade de definir de forma mais cuidadosa esse parâmetro. Embora, ainda seja necessário aprimorar o método proposto e a rotina computacional elaborada, bem como, aprofundar os estudos da fonte de dados utilizada, o experimento de caráter exploratório mostra a possibilidade de reconstruir uma matriz OD a partir de dados disponíveis na web. / This research presents a proposal for the reconstruction of OD matrix from traffic stream velocity data available on the web. To this end, three specific objectives have been established, which include: obtaining and evaluating traffic speed data; construction of a computational model and application of this in a real network. The data were evaluated by the comparison of the mean values of the traffic current, obtained from the web, with those measured by loop sensors. The results indicate that, although the velocities extracted from the web presented higher variance and lower values, the greater part of the data obtained a difference similar to or lower than 11%. In the construction of the computational routine was used the Method of Successive Averages (MEMS) and in its evaluation were performed comparative tests whose references were the results achieved by the author of the used method. The developed routine presented better results in relation to the total number of trips of the reconstructed OD matrix, besides requiring a smaller number of iterations. On the other hand, in relation to the trips attributed to each OD pair the performance was lower. To evaluate the adherence to reality, the proposed method was applied to a real network composed of specific sections of the Presidente Dutra, Pedro I, Ayrton Senna and Governador Carvalho Pinto highways. Due to the fact that the main input of the MEMS is the vehicles flow, was necessary to estimate it from the speed provided by the API. The reconstructed matrix presented a strong dependence on the capacity attributed to the roads, evidencing the need to define this parameter more carefully. Although it is still necessary to improve the proposed method and the elaborated computational routine, as well as to deepen the studies of the used data source, the exploratory experiment shows the possibility of reconstructing an OD matrix from data available on the web.