Congestion is one of the most pressing urban issues Texans face today — it imposes steep social and economic costs on citizens and businesses and shows no sign of subsiding without substantial intervention. This thesis will argue that in the current environment of austerity and sharp political tension, it is of critical importance to implement low cost, politically amicable strategies to manage congestion. Flexible work arrangements like telework and flextime have been developed in the private sector as a cost-saving measure and as a reward for exceptional employees. When workers adopt a non-traditional schedule, the transportation network benefits as vehicles are removed or shifted from the peak period. Despite being widely available, non-traditional work arrangements are little used by employers and employees since both parties are uncertain if the new arrangement will benefit their career path and job security. Recently, public agencies and executives have launched programs and passed mandates that force or greatly incentivize employers to adopt flexible work arrangements. The case studies examined in this thesis show that modern programs can provide cheap, temporary congestion relief for urban areas while substantially benefitting the businesses and agencies who adopt them. / text

This report is being conducted in collaboration with Koenigsegg Automotive AB, a company founded in 1994 that manufactures mega cars. This study aims to design a license plate holder for their new car Jesko that comes in two models, Absolut and Track. The license plate holder is to be adapted to a specific country and their regulatory framework. The project also involves selecting materials and manufacturing processes in consultation with the industrial partner. The work has been divided into different stages, calculations, CAD modeling, analysis, and selection of materials and manufacturing processes. The CAD modeling is performed using Catia V5, while the analyses are conducted using SimSolid. The methodology used in this project is inspired by the method outlined in the book "Product Design and Development" by Karl T. Ulrich, Steven D. Eppinger, and Maria C. Yang. The method consists of six main steps: planning, research, customer needs, concept generation, concept selection, and detailed design. The result chose six different concepts being developed, analyzed, and compared. After identifying the most suitable concept, it is presented with a detailed product description of its design, materials, and manufacturing methods. This report is being conducted in collaboration with Koenigsegg Automotive AB, a company founded in 1994 that manufactures mega cars. This study aims to design a license plate holder for their new car Jesko that comes in two models, Absolut and Track. The license plate holder is to be adapted to a specific country and their regulatory framework. The project also involves selecting materials and manufacturing processes in consultation with the industrial partner. The work has been divided into different stages, calculations, CAD modeling, analysis, and selection of materials and manufacturing processes. The CAD modeling is performed using Catia V5, while the analyses are conducted using SimSolid. The methodology used in this project is inspired by the method outlined in the book "Product Design and Development" by Karl T. Ulrich, Steven D. Eppinger, and Maria C. Yang. The method consists of six main steps: planning, research, customer needs, concept generation, concept selection, and detailed design. The result chose six different concepts being developed, analyzed, and compared. After identifying the most suitable concept, it is presented with a detailed product description of its design, materials, and manufacturing methods.

The perception system is the key for autonomous vehicles to sense and understand the surrounding environment. As the cheapest and most mature sensor, monocular cameras create a rich and accurate visual representation of the world. The objective of this thesis is to investigate if camera-based deep learning models with transfer learning technique can achieve 2D object detection, License Plate Detection and Recognition (LPDR), and highway lane detection in real time. The You Only Look Once version 3 (YOLOv3) algorithm with and without transfer learning is applied on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset for cars, cyclists, and pedestrians detection. This application shows that objects could be detected in real time and the transfer learning boosts the detection performance. The Convolutional Recurrent Neural Network (CRNN) algorithm with a pre-trained model is applied on multiple License Plate (LP) datasets for real-time LP recognition. The optimized model is then used to recognize Ontario LPs and achieves high accuracy. The Efficient Residual Factorized ConvNet (ERFNet) algorithm with transfer learning and a cubic spline model are modified and implemented on the TuSimple dataset for lane segmentation and interpolation. The detection performance and speed are comparable with other state-of-the-art algorithms. / Thesis / Master of Applied Science (MASc)

Background: Every country has seen an increase in motorcycle accidents over the years due to social and economic differences as well as regional variations in transportation circumstances. One common mode of transportation for those in the middle class is a motorbike.  Every motorbike rider is legally required to wear a helmet when driving a bike. However, some people on bikes used to ignore their safety, which resulted in them violating traffic rules by driving the bike without a helmet. The policeman tried to address this issue manually, but it was ineffective and proved to be quite challenging in practical circumstances. Therefore, automating this procedure is essential if we are to effectively enforce road safety. As a result, an automated system was created employing a variety of techniques, including Convolutional Neural Networks (CNN), the Haar Cascade Classifier, the You Only Look Once (YOLO), the Single Shot multi-box Detector (SSD), etc. In this study, YOLOv3 and Haar Cascade Classifier are used to compare motorcycle helmet and license plate detection.  Objectives: This thesis aims to compare the machine learning algorithms that detect motorcycles’ license plates and helmets. Here, the Haar Cascade Classifier and YOLO algorithms are used on the US License Plates and Helmet Detection datasets to train the models. The accuracy is obtained in detecting the helmets and license plates of the motorcycles and analyzed.  Methods: The experiment method is chosen to answer the research question. An experiment is performed to find the accuracy of the models in detecting the helmets and license plates of motorcycles. The datasets utilized for this are from Kaggle, which included 764 pictures of two distinct classes, i.e., with and without a helmet, along with 447 unique license plate images. Before training the model, preprocessing techniques are performed on US License Plates and Helmet Detection datasets. Now the datasets are divided into test and train datasets where the test data set size is considered to be 20% and the train data set size is 80%. The models are trained using 80% pre-processed training datasets and using the Haar Cascade Classifier and YOLOv3 algorithms. An observation is made by giving the 20% test data to the trained models. Finally, the prediction results of these two models are recorded and the accuracy is measured by generating a confusion matrix.   Results: The efficient and best algorithm for detecting the helmets and license plates of motorcycles is identified from the experiment method. The YOLOv3 algorithm is considered more accurate in detecting motorcycles' helmets and license plates based on the results.  Conclusions: Models are trained using Haar Cascade and YOLOv3 algorithms on US License Plates and Helmet Detection training datasets. The accuracy of the models in detecting the helmets and license plates of motorcycles is checked by using the testing datasets. The model trained using the YOLOv3 algorithm has high accuracy; hence, the Neural network-based YOLOv3 technique is thought to be the best and most efficient.

No description available.

License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science

A before and after comparison of vehicle occupancy distributions for the Atlanta, GA I-85 HOV to High Occupancy Toll (HOT) lane conversion scheduled for summer 2011, will assess the changes in vehicle and passenger throughput associated with lane conversion. The field deployment plans and data collection methodologies developed for the HOT evaluation were the result of a comprehensive literature review, an examination of previous data collection methods, an evaluation of the physical characteristics of the I-85 corridor, and the testing of a variety of equipment/manpower strategies. The case study in this thesis evaluates the established vehicle occupancy methodology for consistency across multiple observers during parallel data collection efforts. The differences noted in exact matches and consistency across the use of the "uncertain" values developed for field implementation is specifically assessed. Results from this study are the first step in assessing the validity of the data collection methods used on the HOT corridor and will yield recommendations for improving the methodology for future occupancy studies. A separate assessment of the accuracy of the methodology is also being conducted by the research team and will be published under a separate cover.

This thesis evaluates the performance of a vehicle detection technology, Automated License Plate Recognition (ALPR) camera systems, with regards to its ability to produce real-time travel time information in active work zones. A literature review was conducted to investigate the ALPR technology as well as to identify other research that has been conducted using ALPR systems to collect travel time information. Next, the ALPR technology was tested in a series of field deployments in both an arterial and a freeway environment. The goal of the arterial field deployment was to evaluate the optimal ALPR camera angles that produce the highest license plate detection rates and accuracy percentages. Next, a series of freeway deployments were conducted on corridors of I-285 in Atlanta, Georgia in order to evaluate the ALPR system in active work zone environments. During the series of I-285 freeway deployments, ALPR data was collected in conjunction with data from Bluetooth and radar technologies, as well as from high definition video cameras. The data collected during the I-285 deployments was analyzed to determine the ALPR vehicle detection rates. Additionally, a script was written to match the ALPR reads across two data collection stations to determine the ALPR travel times through the corridors. The ALPR travel time data was compared with the travel time data produced by the Bluetooth and video cameras with a particular focus on identifying travel time biases associated with each given technology. Finally, based on the knowledge gained, recommendations for larger-scale ALPR work zone deployments as well as suggestions for future research are provided.

Made available in DSpace on 2016-03-15T19:37:29Z (GMT). No. of bitstreams: 1 Luiz Angelo D Amore.pdf: 3689058 bytes, checksum: 8476274d8f5220a2a7978da28a4a4f3d (MD5) Previous issue date: 2010-08-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The work presented here shows a robust method for license plate detection. The term robust in this work is directly related to the efficacy of the system as an automated locator of license plates without human intervention and considering specific characteristics of image acquisition and license plate features. The proposed method is based on the specify features of the digits found on the Brazilian license plates. Although the method was designed for the Brazilian license plate pattern it can be easily adjusted to other patterns. The results obtained using the proposed method showed a better performance than that of other academic approaches and even of commercial systems. / Os sistemas automáticos de reconhecimento de placas veiculares têm como principal função a identificação de veículos a partir de imagens digitais, com aplicações nas áreas de segurança pública e privada. Neste trabalho são apresentadas técnicas de processamento de imagens com o objetivo de desenvolver um método robusto para a segmentação de placas veiculares em condições complexas de aquisição. O termo robusto neste trabalho é relacionado diretamente à eficácia do sistema quanto à localização automática das placas veiculares sem intervenção humana, considerando características específicas das imagens e placas. O método proposto é baseado nas especificidades dos dígitos localizados nas placas brasileiras. Embora o método tenha sido projetado para o padrão de placas brasileiro, pode ser facilmente ajustado para outros padrões. Os resultados obtidos com o método proposto mostram um desempenho melhor que outras abordagens acadêmicas, ou mesmo de sistemas comerciais.

This thesis describes the design and workflow of creating an image processing application in Android system, and what are the possibilities in choosing development environment and how to implement them. Then I am writing about my solutions of creating applications, graphical user interface and an interface for Android. I am describing my approach in the design and functionality of the application, communication with the camera, storing and retrieving data. Further I explain which algorithms were implemented for image processing and image evaluation. Product of this thesis is a functioning application that allows to its user to capture images and video stream. The algorithm evaluates the entering data and shows the location of the number plate. The application also allows recognizing texts and numbers from images. There are other various practical features and options implemented within the application.