Weather conditions may impact traffic flow in different ways. Both the human decisions regarding the trip (route, mode, time) and the trip itself can significantly vary. Driver’s behavior may be affected by weather resulting in, among others, deterioration of the network ’s travel times and speeds. Therefore to study and analyse travel times under different weather conditions, represents an important instrument to support Intelligent Transport Systems (ITS). With the correct knowledge and information, travellers would be able to plan their trips in a cost-efficient way, while traffic managers could take advantages of these predictions to deploy control strategies (e.g. weather-responsive signal timing plans). In this project Automatic Number Plate Recognition (ANPR) data from summer 2012 to summer 2013 from three different arterial routes in Stockholm city is used in order to analyze travel times, at a link level, under different weather conditions. To determine to what extent weather variables such as rain, snowfall, temperature and visibility impact the speeds in the network, weather data is integrated with traffic data (ANPR) and analyzed through linear regression models. Results show that there is in fact a negative effect on speed but also on speed’s variability. This knowledge can be useful for trip planning and for traffic management under different weather conditions.

Intelligent Transportation Systems (ITSs) play an important role in modern traffic management, which can be divided into intelligent infrastructure systems and intelligent vehicle systems. Automatic Number Plate Recognition systems (ANPRs) are one of infrastructure systems that allow users to track, identify and monitor moving vehicles by automatically extracting their number plates. ANPR is a well proven technology that is widely used throughout the world by both public and commercial organisations. There are a wide variety of commercial uses for the technology that include automatic congestion charge systems, access control and tracing of stolen cars. The fundamental requirements of an ANPR system are image capture using an ANPR camera and processing of the captured image. The image processing part, which is a computationally intensive task, includes three stages: Number Plate Localisation (NPL), Character Segmentation (CS) and Optical Character Recognition (OCR). The common hardware choice for its implementation is often high performance workstations. However, the cost, compactness and power issues that come with these solutions motivate the search for other platforms. Recent improvements in low-power high-performance Field Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs) for image processing have motivated researchers to consider them as a low cost solution for accelerating such computationally intensive tasks. Current ANPR systems generally use a separate camera and a stand-alone computer for processing. By optimising the ANPR algorithms to take specific advantages of technical features and innovations available within new FPGAs, such as low power consumption, development time, and vast on-chip resources, it will be possible to replace the high performance roadside computers with small in-camera dedicated platforms. In spite of this, costs associated with the computational resources required for complex algorithms together with limited memory have hindered the development of embedded vision platforms. The work described in this thesis is concerned with the development of a range of image processing algorithms for NPL, CS and OCR and corresponding FPGA architectures. MATLAB implementations have been used as a proof of concept for the proposed algorithms prior to the hardware implementation. The proposed architectures are speed/area efficient architectures, which have been implemented and verified using the Mentor Graphics RC240 FPGA development board equipped with a 4M Gates Xilinx Virtex-4 LX40. The proposed NPL architecture can localise a number plate in 4.7 ms whilst achieving a 97.8% localisation rate and consuming only 33% of the available area of the Virtex-4 FPGA. The proposed CS architecture can segment the characters within a NP image in 0.2-1.4 ms with 97.7% successful segmentation rate and consumes only 11% of the Virtex-4 FPGA on-chip resources. The proposed OCR architecture can recognise a character in 0.7 ms with 97.3% successful recognition rate and consumes only 23% of the Virtex-4 FPGA available area. In addition to the three main stages, two pre-processing stages which consist of image binarisation, rotation and resizing are also proposed to link these stages together. These stages consume 9% of the available FPGA on-chip resources. The overall results achieved show that the entire ANPR system can be implemented on a single FPGA that can be placed within an ANPR camera housing to create a stand-alone unit. As the benefits of this are drastically improve energy efficiency and removing the need for the installation and cabling costs associated with bulky PCs situated in expensive, cooled, waterproof roadside cabinets.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-01-18T12:53:32Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Pedro Gonçalves.pdf: 8565820 bytes, checksum: f14c8d37ea1f5daef226a8a6f131bada (MD5) / Made available in DSpace on 2016-01-18T12:53:32Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação_Pedro Gonçalves.pdf: 8565820 bytes, checksum: f14c8d37ea1f5daef226a8a6f131bada (MD5) Previous issue date: 2015-09-01 / Punir infrações de trânsito, controlar tráfego em rodovias, controlar o acesso a áreas restritas, entre outras, são ações tomadas para melhorar o trânsito nas grandes cidades. Para realizar tais ações é necessário, portanto, identificar o veículo automotivo, utilizando, para isso, sua placa de licenciamento. Entretanto, com o aumento de automóveis nas vias urbanas, essa tarefa tornou-se muito difícil de ser realizada de uma forma eficiente por apenas agentes de trânsito, pois existe uma grande quantidade de dados a serem analisados e reportados aos órgãos competentes. Soma-se a isso, o fato de fatores emocionais, cansaços físico e mental, inerentes aos seres humanos, atrapalharem a eficácia da tarefa executada. Por isso, ferramentas que realizam o reconhecimento ótico de caracteres, Opitcal Character Recognition (OCR), vem sendo cada vez mais empregadas para realizar a identificação automática de caracteres existentes nas placas dos automóveis. Este trabalho visa descrever um sistema para identificação de veículos automotivos através de imagens estáticas, apresentando técnicas pesquisadas e estudadas em cada etapa do processo de identificação. As etapas que são apresentadas e detalhadas incluem: a identificação da placa, segmentação dos caracteres presentes na placa e o reconhecimento dos caracteres isolados. Técnicas envolvendo processamento digital de imagem como detectores de bordas, operações morfológicas, análise de componentes conectados e limiarização serão explicitadas. Redes neurais artificias são propostas para realizar o reconhecimento do caractere isolado, tais como Self-Organizing Maps (SOM) e Kernel Self-Organizing Map (KSOM), e serão pormenorizadas. Para avaliar o desempenho das técnicas empregadas nesse projeto, imagens presentes na base de dados MediaLab LPR Database foram utilizadas. Métricas como Recall, Precision e F-Score foram empregadas na avaliação de performance dos diferentes algoritmos estudados e implementados para realizar a detecção da placa, ajudando na escolha do extrator da placa do sistema final. No estágio de segmentação da placa e do reconhecimento dos caracteres isolados, a taxa de acerto foi utilizada para avaliar os algoritmos propostos. Para um grupo de 276 imagens pertencentes a uma base pública, as etapas de detecção, segmentação e reconhecimento alcançaram desempenhos semelhantes aos vigentes na literatura ANAGNOSTOPOULOS et al. (2006) e propiciaram, aproximadamente, uma taxa de acerto global do sistema OCR proposto de 85%. / Punish traffic infractions, traffic control on highways, control access to restricted areas, among others, are actions taken to improve traffic in major cities. In order to take these actions is therefore necessary to identify the motor vehicle using it licensing plate. However, with the increase of the number of cars on urban roads, this task has become very difficult to be performed effectively only by traffic agents because there is a lot of data to be analyzed and reported to the competent agencies. In addition, the fact that emotional factors, physical and mental tiredness, that inherent features to humans, hider effectiveness of task begin performed. Therefore, tools that perform optical character recognition (OCR) are begin increasingly used for automating the identification of characters on licensing plate of the vehicles. This research describes a system for identification of automotive vehicles through still images showing algorithms researched in the literature on each step of the identification process. The stages are presented and detailed include: plate identification, segmentation of the characters existing in plate and the recognition of single characters. Techniques involving digital image processing like edge detectors, morphological operations, connected component analysis and thresholding are explained. Artificial neural networks are submitted to achieve the recognition of single character, such as Self-Organizing Maps (SOM) and Kernel Self-Organizing Map (KSOM), are detailed. In order to evaluate the performance of the techniques used in this project, images from mainly the MediaLab LPR Database were used. The metrics employed to analyze the performance of algorithms implemented to detect a region of plate on image are Recall, Precision and F-Score. This metrics helped to choose the better algorithms for extraction plate on image. In the segmentation stage of the plate and the recognition of single characters, the hit rate was used to evaluate the proposed algorithms. For group of 276 images belonging a public database, the stages of detection, segmentation and recognition reached similar performance with previous approaches (ANAGNOSTOPOULOS et al., 2006), leading the proposed OCR system to 85% of hit rate.