This study explores the potential of vehicle trajectory data obtained from Wide Area Motion Imagery for modeling and analyzing traffic characteristics. The data in question is collected by PV Labs and also known as persistent wide-area video. This video, in combination with PVLab's integrated Tactical Content Management System's spatiotemporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information. This thesis explores the use of data collected by the PVLab's system for an approximate area of 4 square kilometers area in the CBD area of Hamilton, Canada for use in understanding traffic characteristics. The data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like any other computer vision algorithm, this data suffers from false detection, no detection, and other inaccuracies caused by faulty image registration. Data filtering requirements to remove noisy trajectories and reduce error is developed and presented. A methodology for extracting microscopic traffic data (gap, relative velocity, acceleration, speed) from the vehicle trajectories is presented in details. This study includes the development of a data model for storing this type of large-scale spatiotemporal data. The proposed data model is a combination of two efficient trajectory data storing techniques, the 3-D schema and the network schema and was developed to store trajectory information along with associated microscopic traffic information. The data model is designed to run fast queries on trajectory information. A 15-minute sample of tracks was validated using manual extraction from imagery frames from the video. Microscopic traffic data is extracted from this trajectory data using customized GIS analysis. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. The final processed dataset includes vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over two continuous three-hour periods. Two subsets of the data were extracted, cleaned, and processed for use in calibrating car-following sub-models used in microscopic simulations. The car-following model is one of the cornerstones of any simulation based traffic analysis. Calibrating and validating these models is essential for enhancing the ability of the model's capability of representing local traffic. Calibration efforts have previously been limited by the availability and accuracy of microscopic traffic data. Even datasets like the NGSIM data are restricted in either time or space. Trajectory data of all vehicles over a wide area during an extended period of time can provide new insight into microscopic models. Persistent wide-area imagery provides a source for this data. This study explores data smoothing required to handle measurement error and to prepare model input for calibration. Three car-following models : the GHR model, the linear Helly model, and the Intelligent Driver model are calibrated using this new data source. Two approaches were taken for calibrating model parameters. First, a least square method is used to estimate the best fit value for the model parameter that minimizes the global error between the observed and predicted values. The calibration results outline the limitation of both the WAMI data source and the models themselves. Existing model structures impose limitations on the parameter values. Models become unstable beyond these parameter values and these values may not be near global optima. Most of the car-following models were developed based upon some kinematic relation between driver reaction and expected stimuli of that response. For this reason, models in their current form are ill-suited for calibration with noisy microscopic data. On the other hand, the limitation of the WAMI data is the inability of obtaining an estimate of the measurement errors. With unknown measurement errors, any model development or calibration becomes questionable irrespective of the data smoothing or filtering technique undertaken. These findings indicate requirements for development of a new generation of car-following model that can accommodate noisy trajectory data for calibration of its parameters. / MS / The decision making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior in both aggregated and disaggregated manner. Different computational tools relying on representative models of aggregate traffic flow measures and/or driver behavior are used in the decision support system tools. Field data is used not only as an input for the computational tools but also to develop, calibrate, and validate the models representing a particular aspect of traffic and driver behavior. Different approaches have been undertaken to collect the data required for analyzing traffic and driver behavior. One of the applied approach is to collect trajectory (i.e. position, speed, acceleration) information of vehicles in the analysis zone. However, this data collection approach is often limited to relatively small stretch of a roadway for short duration due to high cost of collection and limitation of technology. As a result, the models developed and calibrated using these data often lack generalization power for different situation. This study explores the potential of a new data source to address the aforementioned limitations. The data used in this study collects the trajectory information for the whole population of vehicles in the study area by collecting wide-area (WAMI) video data. The data is collected by Canada based imaging solution company PV Labs. The collection area is relatively large to cover wide range of roadway types and traffic operation system. A framework has been developed to extract traffic flow measures from the trajectory data. The extracted traffic flow measures are then applied to calibrate the car-following model. The car-following model attempts to mimic the longitudinal movement of real-world drivers following another vehicle in front of them. The calibration results outline the limitations of the WAMI data. Although, this dataset is capable of capturing traffic measures for different driving condition, the lack of information about measurement error imposes limits on the direct application of the data for model calibration. Findings of this study can be applied for refinement of the video data capture technology and subsequent application in modelling traffic characteristics as well as development of new and calibration of existing driver behavior model.

Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2018-06-08T13:54:41Z No. of bitstreams: 1 RICARDO_VINICIUS_DOTH_DIS.pdf: 9281309 bytes, checksum: d26fbf7274d4c8eb7158a2d987437b1b (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-18T12:49:23Z (GMT) No. of bitstreams: 1 RICARDO_VINICIUS_DOTH_DIS.pdf: 9281309 bytes, checksum: d26fbf7274d4c8eb7158a2d987437b1b (MD5) / Made available in DSpace on 2018-06-18T12:58:59Z (GMT). No. of bitstreams: 1 RICARDO_VINICIUS_DOTH_DIS.pdf: 9281309 bytes, checksum: d26fbf7274d4c8eb7158a2d987437b1b (MD5) Previous issue date: 2018-03-27 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Wide Area Motion Imagery (WAMI) systems acquire large area aerial images in real time to provide accurate situational awareness information from a region (BLASCH et al., 2014). This system is applied for urban aerial monitoring. Unfavorable environmental conditions, such as shadow regions, are factors that increase system complexity by compromising the effectiveness of tracking algorithms and human visual interpretation (PORTER; FRASER; HUSH, 2010). Several techniques of shadow removal in aerial images have been developed, however due to the characteristics of the shadow and aerial image, a specific method to evaluate and compare the removal is unknown. The main objective of this study is to develop a method to evaluate shadow removal algorithms in aerial images acquired by the WAMI system. This work proposes a radiometric approach modifying the illumination in a controlled environment, simulating an aerial scene, acquiring images with and without the presence of shadows. The image with shadows is processed by the evaluated shadow removal algorithm, with the ideal output being the shadow free image. Shadow detection is evaluated using the confusion matrix concept. Shadow removal is evaluated using the structural similarity index (SSIM). As a result the reduced scale aerial scene model is presented to generate shadow and freeshadow images and the evaluation of 3 shadow removal methods using the data sets of images obtained from the scale model applying the methodology developed. / Sistemas WAMI (Wide Area Motion Imagery) adquirem imagens a?reas de grandes ?reas em tempo real para prover informa??es precisas de uma determinada regi?o (BLASCH et al., 2014). Este sistema ? aplicado para monitoramento a?reo urbano. Condi??es ambientais desfavor?veis, como ?reas sombreadas, s?o fatores que aumentam a complexidade do sistema comprometendo a efic?cia de algoritmos de rastreamento e a interpreta??o visual humana (PORTER; FRASER; HUSH, 2010). Diversas t?cnicas de remo??o de sombra em imagens a?reas foram desenvolvidas, no entanto devido ?s caracter?sticas da sombra e da imagem a?rea ? desconhecido um m?todo espec?fico para avaliar e comparar a remo??o de sombras em imagens a?reas. O objetivo principal deste estudo ? desenvolver um m?todo para avaliar algoritmos de remo??o de sombra em imagens a?reas adquiridas pelo sistema WAMI. Este trabalho prop?e uma abordagem radiom?trica modificando a ilumina??o em um ambiente controlado, simulando uma cena a?rea, adquirindo imagens com e sem sombras. A imagem com sombra ? processada pelo algoritmo de remo??o de sombra avaliado, sendo a imagem sem sombra o resultado ideal a ser alcan?ado. A detec??o de sombra ? avaliada utilizando o conceito de matriz de confus?o (error matrix). A remo??o de sombra ? avaliada utilizando o ?ndice de similaridade estrutural entre duas imagens (SSIM). Foram desenvolvidos o modelo de cena a?rea em escala reduzida para gerar imagens com e sem sombra e a avalia??o de 3 m?todos de remo??o de sombras utilizando os data sets de imagens obtidas do modelo em escala aplicando a metodologia descrita.

Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2017-11-08T12:48:45Z No. of bitstreams: 1 Guilherme_Silva_Dissertacao.pdf: 5806780 bytes, checksum: dd97b70975650b889aaa277b7e6f2b19 (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-17T12:39:26Z (GMT) No. of bitstreams: 1 Guilherme_Silva_Dissertacao.pdf: 5806780 bytes, checksum: dd97b70975650b889aaa277b7e6f2b19 (MD5) / Made available in DSpace on 2017-11-17T12:50:25Z (GMT). No. of bitstreams: 1 Guilherme_Silva_Dissertacao.pdf: 5806780 bytes, checksum: dd97b70975650b889aaa277b7e6f2b19 (MD5) Previous issue date: 2017-08-14 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES

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