Please use this identifier to cite or link to this item:

TitleA bus demand model for Low-Density Territories in Continental Portugal
Author(s)Largo, Harley
Ribeiro, Paulo
KeywordsLow-density territories
Demand model
Public transport
Bus service
Multiple linear regression
Issue dateApr-2019
PublisherInternational Association of Research and Science (IARAS)
JournalInternational Journal of Transportation Systems
CitationLargo H., Ribeiro P. J. G. A Bus Demand Model for Low-Density Territories in Continental Portugal, International Journal of Transportation Systems, Vol. 4, pp. 8 - 17, 2534-8876, 2019
Abstract(s)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.
Publisher version
AccessOpen access
Appears in Collections:C-TAC - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat 
3017-Harley_019-0002(2019).pdf421,39 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID