Remote sensing for identification of trip generating territories in support of urban mobility planning and monitoring

• Published in: GeoJournal Vol. 88; no. 1; pp. 107 - 119
• Main Authors: da Silva, Carlos Fabricio Assunção, de Andrade, Maurício Oliveira, Maia, Maria Leonor Alves, dos Santos, Alex Mota, Portis, Gabriela Tobias
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2023; Springer Nature B.V
• Subjects: Cities; Colonies & territories; Density; Differentiation; Digital imaging; Empirical analysis; Environmental Management; Geography; Human Geography; Image processing; Land use; Mobility; Public transportation; Remote sensing; Social Sciences; Spatial analysis; Transportation systems; Trip generation; Urban areas; Kernel density estimation; Land use/land cover; Trip generating territories; Remote sensing; Image classification
• ISSN: 1572-9893; 0343-2521; 1572-9893
• DOI: 10.1007/s10708-022-10595-7

This article aims is to apply a methodology to identify Trip Generating Territories (TGT) in order to discuss the relationship between transport and land use. To achieve this goal, indirect methods were applied through digital processing of orbital remote sensing images along with spatial analysis, using the Kernel Density Estimation (KDE) method. The image processing results have revealed an overall accuracy of 71% for differentiation and characterization of intra-urban classes use through the adopted typologies. In this way, through data on land use and occupation, it was possible to map out the density of the trip generating territories associated to the main built surfaces and that can indicate a higher potential of trip attraction in the urban area. Additionally, the relationship between land use and public transport system was observed in the city of Petrolina-Brazil, the empirical area of study, and the highest concentration of TGT was observed to be located in the central portion of the city, on its surroundings and on the margins of some arterial roads. Thus, it is possible to extract, in the preliminary analysis, information to support the city's transport and mobility planning.