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|Title:||Public transportation demand model for low density territories|
Dias, G. J. C.
multiple linear regression
|Journal:||WSEAS Transactions on Environment and Development|
|Citation:||Largo H., Ribeiro P. J. G., Dias G. J. C., Trujillo C. Public Transportation Demand Model for Low Density Territories, WSEAS Transactions on Environment and Development, Vol. 15, pp. 395-407, 1790-5079, 2019|
|Abstract(s):||In the course of recent years, there has been a gradual progressive deterioration in some territories, which is caused by economic problems, lack of job opportunities, deficiencies in mobility, among other problems that affect these territories. This deterioration has been causing a reduction in its population and therefore an emigration to large cities where the needs in economic, social and welfare terms can be met in a less complex way. The territories that have been presenting these affectations are denominated territories of low population density. For the specific case analyzed in this study, continental Portugal owns 59% of the municipalities as low density territories (LDT) (this is equivalent to 165 municipalities) . Knowing the importance of public transport to the population of LDT (since it may be the only option to be mobilized) and considering that degradation of the public transportation (PT) increases the problems of isolation of these populations. This study aims to determine which factors (variables) have more influence in the estimation of the demand of public bus transportation for the LDT in mainland Portugal. In addition, a model is proposed to estimate the demand for these low-density territories. The mathematical model of multiple linear regression (MLR) is used based on the most influential socioeconomic and demographic variables for LDT. The model was developed with the statistical tool SPSS (statistical package for the social sciences). The estimated model presented an adjustment of 87%, taking into account the variables of number of illiterate people, population density and purchasing power. In addition, an analysis by regions (NUTS II classification) was carried out to determine which region presents a lower error percentage in the estimations. From this analysis it can be concluded that the northern region with 88% of municipalities (equivalent to 36 LDT) presents an estimate of error lower than 50%.|
|Appears in Collections:||C-TAC - Artigos em Revistas Internacionais|