There are some knowledge gaps regarding the relationship between transportation infrastructure and economic development, especially about economic impacts that occur due to implementation of infrastructure in a given region, albeit various studies have addressed the issue. This paper aims to identify variables that affect economic development in order to contribute to the development of a theoretical model that could explain the relationship between transportation infrastructure and economic development. The theoretical model is satisfactory because it begins by analyzing the actions generated by the transportation infrastructure. Moreover, the model is based on the Location Theory considering the economic development and taking into account variables such as transportation costs, gain, product value, consumption, competition between companies and lastly monopoly. Finally, an econometric procedure, Spatial Panel Auto Regressive Vector Model (PVAR), was used to evaluate the relationship between economic development and investments in transportation infrastructure.
Several scholars have addressed the locational factors necessary for the best installation of industries or services; among them, one finds the costs with transportation of products and raw materials, labor-related costs, benefits deriving from the agglomeration of companies, as well as place-environment associations. Some agglomeration types stand out in this context, each one of them has its specific features, although they share the same goal. The agglomeration of companies is an increasingly frequent trend observed in production centers. Companies belonging to the same production chain remain close to each other in order to reduce costs with product transportation, storage and distribution processes. Consequently, they get to optimize their processes and increase their profits. The proximity between companies belonging to the same branch increases competitiveness between them. In addition, there is significant presence of skilled labor in these regions, a fact that favors logistics operations such as the transportation of inputs needed to enable companies’ production, and cost reduction. Thus, the aim of the present research is to create a methodology capable of identifying the variables necessary to develop a logistics cluster based on concepts such as productive economic agglomerations, by taking into consideration aspects addressed in a survey conducted with key cluster policy-development actors. Moreover, Interpretive Structural Modelling (ISM) was used to create an ontology to help better understanding the association among all variables necessary to structure logistics clusters.
The need for establishing urban mobility policies that favor pedestrians, which are the most vulnerable elements in the transit system, is known. In this sense, the study aimed to identify the pattern of pedestrian preferences on sidewalks, using the Walkability Index and Stated Preference technique. The methodology developed to achieve the objective is composed of the following steps: (i) definition of the study area, (ii) Decription of the study area, (iii) definition of variables, (iv) data collection, (v) calculation of the Walkability Index, (vi) data processing of the stated preference survey, (vii) analysis of results and definition of guidelines. A case study was carried out in the central sector of Goiânia (GO) in order to validate the presented methodology. Thus, the study proposes to investigate pedestrian preferences on sidewalks in order to provide subsidies to assist the planning of pedestrian-oriented mobility systems.
An important piece of information for planning public transportation is the number of passengers using the system. Several initiatives have started to explore the Wi-Fi packets generated by passengers’ smartphones as means to obtain this information. A sensing device located inside the bus can intercept and collect these packets. By applying filters, e.g., verifying if the signal strength is higher than a threshold, the sensor can infer passengers' presence/absence. However, such limits are set arbitrarily, leading to errors, for example, when close to bus stops. To address this issue, this article proposes a method (UAI-FI) based on an artificial intelligence technique (Support Vector Machine) to classify the origin of packets as inside or outside the bus. To validate UAI-FI, we applied and compared our approach to other methods in a bus line in Goiânia/Brazil. The results suggest that UAI-FI outperformed existing methods. Furthermore, it successfully classified the packet’s origin, obtaining 83.3% and 88.5% of the total number of passengers boarding and alighting the line. Despite the overall similarity, we highlight that UAI-FI’s counting curve presented a delay compared to the manual count indicating that the frequency that Wi-Fi packets are sent can cause the presence/absence of passengers to be perceived at different stops.
Esta pesquisa tem como objetivo desenvolver um índice, chamado Índice de Condição da Via (ICV), a ser usado para avaliar a qualidade de sistemas cicloviários a fim de auxiliar planejadores de sistemas de transportes na implantação e avaliação de sistemas cicloviários; além de aumentar a habilidade de gestores e profissionais na tomada de decisão, apresentando critérios para análises. O ICV foi desenvolvido baseado em constatações sobre o uso da bicicleta como modo de transporte sustentável, nos preceitos da psicologia ambiental e nos métodos de avaliação do nível de serviço aplicados ao transporte. Com isso, foram definidos oito (8) indicadores para compor o ICV, a saber: largura da via, velocidade, topografia, conflitos (estacionamentos, cruzamentos sem sinalização), fluxo de veículos, amenidades, pavimentação (qualidade) e uso do solo. Ao final, normatizou-se o ICV de forma análoga ao proposto por Eastman e Jiang (2010) em que é possível classifica os segmentos em A, B ou C. Nesta escala se o valor apurado do ICV para o segmento estiver entre 0 até 0,33 é considerado ruim (C). De 0,33 até 0,67, o segmento apresenta uma boa situação (B). Quando o índice apresentar valor superior a 0,68, tem-se uma situação muito boa (A).
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