Este trabalho é um modelo para controle de operação de frota de ônibus público urbano de baixa demanda, que objetiva o monitoramento (e intervenção, quando necessário), de forma que os ônibus monitorados cumpram a programação predeterminada pela grade-horária, evitando assim situações indesejáveis como atrasos ou congestionamentos nas estações de embarque e desembarque. A gestão do controle da movimentação dos ônibus se baseia em intervenções a serem realizadas na frota, considerando-se o sistema Bus Rapid Transit, que possui como grande diferencial as faixas segregadas para circulação dos ônibus. O objetivo é propor intervenções na frota (ações enviadas diretamente ao motorista como, por exemplo, diminuir a velocidade), e analisar os resultados, de forma a contribuir para diminuir dois grandes problemas em transporte público urbano: (1) o não cumprimento dos horários e (2) congestionamentos nas estações. Esses dois problemas geram reações em cadeia que acabam também afetando a regularidade de outros ônibus que compartilhar a mesma via e estações. Instituições públicas ou privadas de ônibus urbano podem obter benefícios utilizando um bom sistema de monitoramento e controle de frota, como por exemplo: mais segurança e previsibilidade nos horários, melhor adequação entre demanda e oferta do serviço, gerenciamento da frota através dos relatórios de pontualidade e desvio, dentre outros. Além disso, a melhora da qualidade do serviço traz como conseqüência o aumento no número de usuários do transporte, devido à oferta de um sistema mais atrativo, seguro e eficiente. A metodologia apresentada neste trabalho é constituída de um modelo para monitoramento da operação da frota e detecção de inconformidades dos ônibus, baseado na grade-horária e em um algoritmo de tomada de decisão, que objetiva a correção das inconformidades identificadas. Na tentativa de resolver (ou diminuir) os problemas que possam surgir durante a circulação dos ônibus, algoritmos (heurísticos) de decisão são utilizados em simulações de situações adversas. Com essas simulações é possível efetuar comparação das situações sem e com as intervenções propostas pelo algoritmo de decisão. Um sistema de informação geográfica é utilizado para manipulação dos dados e apresentação dos mesmos. De forma unifilar, é possível comparar as situações com e sem intervenções. Gráficos e tabelas complementam a apresentação dos resultados, onde é possível identificar e perceber a vantagem no monitoramento e intervenção na frota (a fidelidade à grade-horária melhora com as situações/intervenções simuladas). / This dissertation is a model for management of urban public bus fleet operation control, aimed at monitoring (and intervention when necessary) so that the buses monitored meet the pre-determined schedule by the time-grid, thus avoiding undesirable situations such as delays or congestion in the stations of embarkation and disembarkation. The proposed operational management model and the interventions to be carried out in the fleet are possible considering the Bus Rapid Transit system that has segregated roads for the movement of buses. The goal is to propose interventions in the fleet (actions will be sent directly to the driver, like - change the bus velocity), and analyze the results in order to help reduce two major problems in urban public transport: (1) non compliance with the schedules and (2) congestion at stations. These two problems create chain reactions that end up affecting the regularity of other vehicles who share the same track and stations. Public or private urban bus institutes can benefit by using a good system of monitoring and control of the fleet, for example: more security and predictability in schedules, better match between demand and supply of the service, fleet management through the reports of punctuality and deviation, among others. Moreover, the improvement of service quality has as consequence the increase in the number of users of transport, due to availability of a more attractive, safe and efficient bus service. The methodology presented here consists of a model for fleet tracking (operation control) and tracing of unconformities buses based on time-grid and on an algorithm of decision-making that aims to correct non-conformities identified. In an attempt to solve (or reduce) the problems that may arise during the movement of buses, algorithms (heuristic) decision is used in simulations of adverse situations. In these simulations can be performed comparing the situations \"without\" and \"with\" the interventions proposed by the decision algorithm. A geographic information system is used for data manipulation and presentation of them. Using linear representation is possible to compare the buses situations with and without interventions. Charts and tables complement the presentation of results, where it is possible to identify and realize the advantage in monitoring and intervention in the fleet (fidelity to the time-grid improvement with the simulated situations/interventions).

Este trabalho é um modelo para controle de operação de frota de ônibus público urbano de baixa demanda, que objetiva o monitoramento (e intervenção, quando necessário), de forma que os ônibus monitorados cumpram a programação predeterminada pela grade-horária, evitando assim situações indesejáveis como atrasos ou congestionamentos nas estações de embarque e desembarque. A gestão do controle da movimentação dos ônibus se baseia em intervenções a serem realizadas na frota, considerando-se o sistema Bus Rapid Transit, que possui como grande diferencial as faixas segregadas para circulação dos ônibus. O objetivo é propor intervenções na frota (ações enviadas diretamente ao motorista como, por exemplo, diminuir a velocidade), e analisar os resultados, de forma a contribuir para diminuir dois grandes problemas em transporte público urbano: (1) o não cumprimento dos horários e (2) congestionamentos nas estações. Esses dois problemas geram reações em cadeia que acabam também afetando a regularidade de outros ônibus que compartilhar a mesma via e estações. Instituições públicas ou privadas de ônibus urbano podem obter benefícios utilizando um bom sistema de monitoramento e controle de frota, como por exemplo: mais segurança e previsibilidade nos horários, melhor adequação entre demanda e oferta do serviço, gerenciamento da frota através dos relatórios de pontualidade e desvio, dentre outros. Além disso, a melhora da qualidade do serviço traz como conseqüência o aumento no número de usuários do transporte, devido à oferta de um sistema mais atrativo, seguro e eficiente. A metodologia apresentada neste trabalho é constituída de um modelo para monitoramento da operação da frota e detecção de inconformidades dos ônibus, baseado na grade-horária e em um algoritmo de tomada de decisão, que objetiva a correção das inconformidades identificadas. Na tentativa de resolver (ou diminuir) os problemas que possam surgir durante a circulação dos ônibus, algoritmos (heurísticos) de decisão são utilizados em simulações de situações adversas. Com essas simulações é possível efetuar comparação das situações sem e com as intervenções propostas pelo algoritmo de decisão. Um sistema de informação geográfica é utilizado para manipulação dos dados e apresentação dos mesmos. De forma unifilar, é possível comparar as situações com e sem intervenções. Gráficos e tabelas complementam a apresentação dos resultados, onde é possível identificar e perceber a vantagem no monitoramento e intervenção na frota (a fidelidade à grade-horária melhora com as situações/intervenções simuladas). / This dissertation is a model for management of urban public bus fleet operation control, aimed at monitoring (and intervention when necessary) so that the buses monitored meet the pre-determined schedule by the time-grid, thus avoiding undesirable situations such as delays or congestion in the stations of embarkation and disembarkation. The proposed operational management model and the interventions to be carried out in the fleet are possible considering the Bus Rapid Transit system that has segregated roads for the movement of buses. The goal is to propose interventions in the fleet (actions will be sent directly to the driver, like - change the bus velocity), and analyze the results in order to help reduce two major problems in urban public transport: (1) non compliance with the schedules and (2) congestion at stations. These two problems create chain reactions that end up affecting the regularity of other vehicles who share the same track and stations. Public or private urban bus institutes can benefit by using a good system of monitoring and control of the fleet, for example: more security and predictability in schedules, better match between demand and supply of the service, fleet management through the reports of punctuality and deviation, among others. Moreover, the improvement of service quality has as consequence the increase in the number of users of transport, due to availability of a more attractive, safe and efficient bus service. The methodology presented here consists of a model for fleet tracking (operation control) and tracing of unconformities buses based on time-grid and on an algorithm of decision-making that aims to correct non-conformities identified. In an attempt to solve (or reduce) the problems that may arise during the movement of buses, algorithms (heuristic) decision is used in simulations of adverse situations. In these simulations can be performed comparing the situations \"without\" and \"with\" the interventions proposed by the decision algorithm. A geographic information system is used for data manipulation and presentation of them. Using linear representation is possible to compare the buses situations with and without interventions. Charts and tables complement the presentation of results, where it is possible to identify and realize the advantage in monitoring and intervention in the fleet (fidelity to the time-grid improvement with the simulated situations/interventions).

Detta projekt behandlar metodutveckling för att modellera en trevägskatalysator som skulle kunna användas vidare för att uppskatta utsläppen från en Ottomotor. Modellen är byggd i AVL Cruise M för att bestämma omvandlingarna för de tre lagstiftade föroreningarna kolmonoxid, kväveoxider (NOx) och metan baserat på reaktioner som sker i trevägskatalysatorer. Projektet tar också upp olika experimentella metoder och specifika kalibreringsmetoder som används för att samla in data och bygga modellen. Projektet diskuterar två olika kalibreringsprocesser baserade på insamlade experimentella data och antalet reaktioner kalibrerade i varje steg. Dessutom diskuteras programvaran som används och ändringarna som gjorts i den fördefinierade modellen av programvaran. Resultatet efter kalibreringen visade att den andra kalibreringsprocessen gav bättre resultat, men vissa avvikelser observerades i den byggda modellen. Avvikelserna antas bero på följande tre anledningar. För det första, försummar ytreaktionsmodellen föroreningarnas diffusionshastigheter. Dessutom kan komplexiteten hos objektfunktionen som matas in i optimeraren samt de stationära data som används för kalibreringsändamål också ge avvikelser. Dessa tre argument testas i projektet och slutsatserna som dras för att kunna förbättra modellen är följande.  Objektfunktionen behöver förenklas så mycket som möjligt, diffusion av föroreningar genom washcoaten kan inkluderas i olika komplicerade steg och syrelagringsreaktioner blir viktiga när man tar hänsyn till transienta förhållanden. / This project discusses about development of a method to model the three-way catalyst which could be used further to estimate the emissions from an Otto engine. The model is built in the commercial software called AVL Cruise M to determine the conversions of three legislative pollutants namely, carbon monoxide, nitrogen oxide and methane based on reactions occurring within the three-way catalyst. The project also discusses about different experimental and calibration methods used for collecting the data and building the model. The project discusses two different calibration process based on the experimental data used and number of reactions calibrated on each step. Furthermore, the software used, and the modifications made in the predefined model of the software are also discussed. The result after the calibration showed that the second calibration process gave better results, but some deviations were observed in the model built. The deviations are assumed to be because of three arguments present. Firstly, considering the surface reaction model which neglects the diffusion rates of the species. Secondly, the complexity of the objective function fed into the optimiser. The optimiser is also a software by AVL named design explorer which helps to optimise the parameters. And thirdly, the use of steady state data only and not including transient conditions for the calibration purposes. These arguments are tested in the project, and it is concluded to improve the model, the objective function needs to be simplified as much as possible, diffusion of species through washcoat could be considered at advance stages and oxygen storage reactions become important when transient conditions are taken into consideration.

Relevant path planning using public transport is limited by reliability of the transportation network. In some cases it turns out that we can plan paths with respect to expected delays and hereby improve the reliability of the resulting path. This study focuses on prediction of the delays in public transport systems using data from vehicle tracking systems -- known as the AVL data. These data are typically collected by the transit operators. Various algorithms are compared using real data from Prague trams tracking system. The study also includes a discussion about a possible utilization of the information gained from the used methods in passenger information systems. Powered by TCPDF (www.tcpdf.org)

Academic vocabulary is an important determiner of academic success for both native and non-native speakers of English (Corson, 1997; Gardner, 2013; Hsueh-chao & Nation, 2000). In an attempt to address this need, Coxhead (2000) developed the Academic Word List (AWL)—a list of words common across a range of academic disciplines; however, Gardner & Davies (2014) identified potential limitations in the AWL and have more recently produced their own list of core academic vocabulary—the Academic Vocabulary List (AVL). This study compares the occurrences of the AWL and AVL word families in an intensive English program (IEP) corpus of 50 texts to determine which list has the best overall coverage, frequency, and range in the corpus. While the results show a strong presence of both lists in the IEP corpus, the AVL outperforms the AWL in every measure analyzed in the study. Suggestions for instruction and future research regarding these lists are provided.

<p>Our main results are that splay trees are faster for sorted insertion, where AVL trees are faster for random insertion. For searching, skip lists are faster than single class top-down splay trees, but two-class and multi-class top-down splay trees can behave better than skip lists.</p>

The role of public transportation increases as travel demand increases due to the growth in population and economics. The importance of providing a balanced public transportation has increased. In Ontario, Canada, the provincial government investing more than $17B in transit projects by the year of 2020 [28]. Consequently, planners and engineers motivated to pay more attention to mode split (mode choice) models used to estimate transit ridership. In most existing mode choice models, the likelihood of a trip maker using a transit mode (e.g. transit) is based on the generalized cost (GC) of using transit mode relative to the generalized cost of all other available modes. In conventional generalized cost formulations, transit costs are considered deterministic. It is quite evident, however, that great variability exists in the reliability of transit service and, as a result, the actual costs experienced by users. Efforts are ongoing to incorporate the costs of reliability in mode choice models by extending formulations to include penalties for arriving prior to or later than a desired arrival time. Transit operators strive to provide reliable service to retain and attract more users. Unreliable service can adversely affect the user by arriving late or early at their destination, waiting longer at their boarding station, and spending more time than expected in the transit vehicle. Unreliable service will also increase the user's anxiety associated with the uncertainty and discomfort. All these factors should be considered explicitly within the generalized cost (GC) function in order to accurately capture the GC of transit service relative to other modes and to ensure that these factors are not incorporated within the mode specific constant. In this study, a GC model is developed that explicitly represents service reliability. Service reliability is represented in the model as penalties associated with passengers' late arrival, early arrival, departure time shifting, waiting time, and anxiety. Furthermore, a methodology of utilizing field data to capture service reliability is defined. A Monte-Carlo simulation framework has been developed using the proposed GC function to quantify the impact of transit reliability on transit user cost. The proposed framework was applied on the iXpress service in the Regional of Waterloo in Ontario, Canada, utilizing Automated Vehicle Location (AVL) system data from the Regional Municipality of Waterloo to estimate service reliability. All the coefficients included in the proposed GC are assumed based on the relative importance of each penalty to scheduled in vehicle time by considering different passenger classes. In this research, the transit passengers are assumed to belong to one of three passenger classes based on their risk tolerance. From the results, it was found that increasing reliability of arrivals at a station can decrease transit users generalized costs significantly. We further posit that including uncertainty in the calculation of generalized costs may provide better estimates for mode split in travel forecasting models.

The role of public transportation increases as travel demand increases due to the growth in population and economics. The importance of providing a balanced public transportation has increased. In Ontario, Canada, the provincial government investing more than $17B in transit projects by the year of 2020 [28]. Consequently, planners and engineers motivated to pay more attention to mode split (mode choice) models used to estimate transit ridership. In most existing mode choice models, the likelihood of a trip maker using a transit mode (e.g. transit) is based on the generalized cost (GC) of using transit mode relative to the generalized cost of all other available modes. In conventional generalized cost formulations, transit costs are considered deterministic. It is quite evident, however, that great variability exists in the reliability of transit service and, as a result, the actual costs experienced by users. Efforts are ongoing to incorporate the costs of reliability in mode choice models by extending formulations to include penalties for arriving prior to or later than a desired arrival time. Transit operators strive to provide reliable service to retain and attract more users. Unreliable service can adversely affect the user by arriving late or early at their destination, waiting longer at their boarding station, and spending more time than expected in the transit vehicle. Unreliable service will also increase the user's anxiety associated with the uncertainty and discomfort. All these factors should be considered explicitly within the generalized cost (GC) function in order to accurately capture the GC of transit service relative to other modes and to ensure that these factors are not incorporated within the mode specific constant. In this study, a GC model is developed that explicitly represents service reliability. Service reliability is represented in the model as penalties associated with passengers' late arrival, early arrival, departure time shifting, waiting time, and anxiety. Furthermore, a methodology of utilizing field data to capture service reliability is defined. A Monte-Carlo simulation framework has been developed using the proposed GC function to quantify the impact of transit reliability on transit user cost. The proposed framework was applied on the iXpress service in the Regional of Waterloo in Ontario, Canada, utilizing Automated Vehicle Location (AVL) system data from the Regional Municipality of Waterloo to estimate service reliability. All the coefficients included in the proposed GC are assumed based on the relative importance of each penalty to scheduled in vehicle time by considering different passenger classes. In this research, the transit passengers are assumed to belong to one of three passenger classes based on their risk tolerance. From the results, it was found that increasing reliability of arrivals at a station can decrease transit users generalized costs significantly. We further posit that including uncertainty in the calculation of generalized costs may provide better estimates for mode split in travel forecasting models.

Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.

<p>Our main results are that splay trees are faster for sorted insertion, where AVL trees are faster for random insertion. For searching, skip lists are faster than single class top-down splay trees, but two-class and multi-class top-down splay trees can behave better than skip lists.</p>