Applying Multivariate Geostatistics for Transit Ridership Modeling at the Bus Stop Level

Abstract

Travel demand models have been developed and refined over the years to consider a characteristic normally found in travel data: spatial autocorrelation. Another important feature of travel demand data is its multivariate nature. However, regarding the public transportation demand, there is a lack of multivariate spatial models that consider the scarce nature of travel data, which generally are expensive to collect, and also need an appropriate level of detail. Thus, the main aim of this study was to estimate the Boarding variable along a bus line from the city of São Paulo -Brazil, by means of a multivariate geostatistical modeling at the bus stop level. As specific objectives, a comparative analysis conducted by applying Universal Kriging, Ordinary Kriging and Ordinary Least Squares Regression for the same travel demand variable was proposed. From goodness-of-fit measures, the results indicated that Geostatistics is a competitive tool comparing to classical modeling, emphasizing the multivariate interpolator Universal Kriging. Therefore, three main contributions can be highlighted: (1) the methodological advance of using a multivariate geostatistical approach, at the bus stop level, on public transportation demand modeling; (2) the benefits provided by the models regarding the land use and bus network planning; and (3) resource savings of field surveys for collecting travel data.

Should We Account for Network Distances or Anisotropy in the Spatial Estimation of Missing Traffic Data?

“…The lack of data on travel demand variables, which are usually spatially discrete, has led to an increasing number of geostatistical applications to travel demand modeling, with results that represent an important contribution to the planning and operation of transport systems (Gomes et al, 2018;Lindner and Pitombo, 2019;Marques and Pitombo, 2021a;Yang et al, 2018;Zhang and Wang, 2014). Several studies using Geostatistics for spatially estimate travel demand variables can be found in the bibliographic review by Marques and Pitombo (2020).…”

Avaliação da Infraestrutura Cicloviária e Interpolação Espacial de seus Indicadores de Qualidade: uma Abordagem Baseada em Análise Hierárquica e Geoestatística

“…Exemplos dessas aplicações podem ser encontrados em diversas áreas de estudo com variáveis espacialmente discretas: epidemiologia, aquicultura, agricultura, ciências florestais (CARVALHO et al, 2015;GOOVAERTS, 2009;KERRY et al, 2016;STELZENMÜLLER;EHRICH;ZAUKE, 2005) e, consistentemente, na engenharia de transportes. Nesse âmbito, estudos vêm sendo desenvolvidos tanto na área de segurança viária (GOMES et al, 2018;MAJUMDAR;NOLAND;OCHIENG, 2004) quanto de modelagem de variáveis de demanda por transportes (CHICA-OLMO; RODRÍGUEZ-LÓPEZ; CHILLÓN, 2018;LINDNER et al, 2016;PITOMBO, 2019;PITOMBO, 2021aPITOMBO, , 2021bPITOMBO et al, 2015;SELBY;KOCKELMAN, 2013;YANG et al, 2018;ZHANG;WANG, 2014). Ao longo desses trabalhos, os autores evidenciaram, por meio do gráfico da função semivariograma, a existência de uma consistente estrutura espacial nas variáveis sob análise, corroborada pelos resultados por eles obtidos.…”