Modelos de geração e variabilidade no volume diário de veículos em shopping centers
DOI:
https://doi.org/10.14295/transportes.v18i1.388Abstract
Resumo: Os modelos de geração de viagens, utilizados nos Estudos de Impacto de Tráfego (EIT) de shopping centers, geralmente estimam volumes diários médios anuais de veículos atraídos. Neste artigo são confrontados os volumes estimados por modelos referenciados na literatura com volumes veiculares observados em seis empreendimentos localizados na região metropolitana de Porto Alegre. Quando comparadas com as distribuições de volumes diários de cada shopping center, as estimativas revelam erros expressivos. Modelos baseados apenas na Área Bruta Locável (ABL) mostram-se incapazes de representar as diferenças entre os empreendimentos, indicando a necessidade da incorporação de novas variáveis explicativas. Entende-se que as diferenças entre estimativas e valores observados estejam fortemente associadas ao poder aquisitivo dos clientes. O artigo propõe modelos não-convencionais de geração de viagens construídos com os dados dos shopping centers estudados. Entre os modelos estimados, o que incorpora a variável "valor do aluguel" das lojas dos empreendimentos resultou nos menores erros de estimativa.
Abstract: Trip generation models, applied in Traffic Impact Studies of shopping centers, usually estimate annual average daily volumes of attracted private vehicles. In this paper we confront volumes estimated by models referred in the literature with observed vehicular volumes of six shopping centers located within the Metropolitan Area of Porto Alegre, Brazil. When compared to the distributions of daily volumes of each shopping center, estimates reveal expressive errors. Models based only on gross leasing areas are unable to represent the differences between the shopping centers, suggesting the need to incorporate new explanatory variables. We understand that the differences between estimated and observed values are strongly associated to the income of the clients. The paper presents non-conventional trip generation models estimated with data from the shopping centers analyzed. Amongst the estimated models, the one that incorporates the variable "rental prices" of the shops resulted in the smaller estimation errors.
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