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The objective of this paper is to propose a method to systematize cycling planning in small-sized Brazilian cities based on open data, such as census data, collaborative mappings, and Digital Elevation Models, seeking to assist in the elaboration of municipal urban mobility plans, and that can be adapted to other countries with similar characteristics and that have equivalent information. The proposed method is summarized in four steps: geoprocessing of open spatial data, which allows the georeferencing of potential cycling demand and cycling trip generator poles, as well as updated representations of road systems; definition of the subsets of origins and destinations of the cycling routes to be identified using Dijkstra’s algorithm, in order to minimize the overall cost associated with the road segments' operational quality for cycling; impedance assignment to road segments based on their average slopes and cycling Levels of Traffic Stress; and assessment of these road segments’ proportions in the identified cycling routes (Cycling Potential Index). Examples of the proposed method’s application were carried out in two Brazilian cities, Bariri-SP and Bocaina-SP, through which it was possible to define, for both cities, continuous and interconnected cycling axles, accessible to most of the study areas’ potential cyclists. Therefore, despite the obvious importance of conducting specific research and field surveys in each Brazilian city for greater reliability of the results, the proposed method can contribute to the popularization of cycling in utilitarian trips and to the strengthening of the “bicycle culture” still in force in small-sized Brazilian cities.

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A estimativa de viagens por domicílio é fundamental para a tomada de decisões relativas ao planejamento de transportes. Porém, para obter essa estimativa são necessários dados desagregados dos domicílios, que geralmente são obtidos pela Pesquisa Domiciliar de Origem e Destino. No entanto, a maioria das cidades enfrenta problemas para a aquisição desses dados, uma vez que este tipo de pesquisa é de alto custo de preparação e execução. Desta forma, surge a necessidade de ferramentas que forneçam dados confiáveis e com baixo custo. Assim, o objetivo deste artigo é apresentar um método sequencial, para estimativa de viagens domiciliares, a partir de população sintética e Redes Neurais Artificiais (RNAs).  A população sintética foi baseada em dados agregados do censo e simulação Monte Carlo. Os resultados obtidos com as RNAs foram comparados aos resultados de um modelo linear tradicional, mostrando-se melhores e corroborando o potencial do uso de RNAs para modelagem da demanda por transportes. As viagens sintéticas por domicílio foram validadas a partir dos dados desagregados da Pesquisa Origem-Destino (2007) e testes de hipótese para comparação de valores típicos e distribuições populacionais.  Em 71% dos setores censitários, as viagens sintéticas foram consideradas similares aos dados reais, confirmando a eficiência do método proposto. Assim, a principal lacuna desta pesquisa, é a apresentação do método sequencial, capaz de tanto minimizar problemas de aquisição de dados quanto atenuar as restrições e suposições matemáticas, inerentes aos modelos tradicionais de demanda por transportes.

This paper is part of a research under enhancement since 2001, in which the main objective is to measure small dynamic displacements by analysis of L1 GPS carrier frequency with 1575.42 MHz-wavelength 19.05 cm, under an adaptive method for collecting data and filtering techniques. This method, named Phase Residual Method (PRM) is based on the frequency domain analysis of the phase residuals resulted from the L1 double difference static data processing of two satellites in almost orthogonal elevation angle. In this work it is proposed to obtain the phase residuals directly from the raw phase observable collected in a short baseline during a limited time span, in lieu of obtaining the residual data file from regular GPS processing programs. In order to improve the ability to detect millimetric displacements, two filtering techniques are introduced. The first one is the autocorrelation that reduces the phase noise with random time behavior. The other one is the running mean to separate low frequency from the high frequency phase sources. Two trials are presented to verify the proposed method and filtering techniques applied. One simulates a 2.5 millimeter vertical GPS antenna displacement and the second using the data collected during a bridge dynamic load test. The results show a good consistency to detect millimetric oscillations from L1 frequency and filtering techniques.

In recent decades, due to the increasing mobility of people and goods, the rapid growth of users of mobile devices with location-based services has increased the need for geospatial information. In this context, positioning using data collected by the Global Navigation Satellite Systems (multi-GNSS) has gained more importance in the field of geomatics. The quality of the solutions is related, among other factors, to the receiver’s type used in the work. To improve the positioning with low-cost devices and to avoid additional user expenses, this work aims to propose the implementation of an Artificial Neural Network (ANN) to estimate the GPS L2 carrier observables. For this, a network model was selected through the cross-validation (CV) technique, the observations were estimated, and the accuracy of the solutions was analyzed. The CV technique demonstrated that a Multilayer Perceptron with four intermediate layers and one with one intermediate layer are the most appropriate configurations for this problem. The dual-frequency RINEX processing (with artificial data) revealed significant improvements. For some tests, it was possible to comply with the rural property georeferencing regulations of the Brazilian National Institute of Colonization and Agrarian Reform (INCRA). The results indicate, therefore, that the methodological proposal of the present investigation is very promising for approximating the quality of positioning reachable using a dual-frequency receiver.

The present study aims to evaluate the TOPODATA Digital Elevation Model (DEM) as a source of relevant altimetric information for urban cycling planning. A case study was conducted in the city of Bariri-SP. The Cartographic Accuracy Standard of Digital Cartographic Products (PEC-PCD), assessed by comparing the TOPODATA altitudes with homologous altitudes surveyed by a precise satellite method (GNSS), suggests that the DEM may not be adequate for phases of cycling planning that require greater detailing of the elements to be designed. A moderate to strong positive spatial autocorrelation was observed between the DEM errors. Regarding its usability for estimating the average slopes of the road segments, however, the results suggest that TOPODATA average slopes do not differ statistically from those estimated with field-surveyed data and, for the two criteria adopted for acceptable gradient lengths for cycling, more than 82% of the road segments were classified similarly using both sources of information.