A bus demand model for Low-Density Territories in Continental Portugal

• Autor(a) principal: Largo, Harley
• Data de Publicação: 2019
• Outros Autores: Ribeiro, Paulo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/1822/60994
• topic: Low-density territories; Demand model; Public transport; Bus service; Multiple linear regression; SPSS

Continental Portugal has 278 municipalities with 164 being classified as low density territories (LDT), according to criteria mainly centered in population density and per capita income. LDT?s are characterized as territories with economic and labor problems, which also have suffered a significant reduction in resident population. Thus, in the last decade, studies have been developed in Portugal for these territories to enhance the quality of life and living conditions. For several reasons, public transport represents an important mode of transport to guarantee cohesion and equity among different groups of population. Thus, it is important to characterize the demand patterns of public transport in LDT, in order to better plan and promote its use, especially for bus services. Therefore, this paper presents a model to estimate the demand of a bus transportation in low-density areas of Portugal. The mathematical model used to estimate the demand was the multiple linear regression (MLR) models, which is a function of the most relevant and influential socioeconomic and demographic variables for LDT. The MLR model were developed with the statistical tool SPSS (Statistical Package for the Social Sciences). It is important to highlight that were created three groups of Portuguese municipalities according to the population density to create adjusted demand model for bus services in LDT. The bus demand MLR model presented a low level of adjustment, probably due to the amount of data used to estimate each model. Results shown that the model that have a better adjustment to estimate the number of bus trips was achieved for the group of a population density lower than 50 inhabitants/Km2 that was supported by two variables: illiterate people and the number of unemployed. Thus, future works must estimate bus trips through another estimation approach for transport demand in low-density territories.