Abstract
Studies in transportation planning routinely use data in which location attributes are an important source of information. Thus, using spatial attributes in urban travel forecasting models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.
Highlights
Travel demand forecasting models usually consider explanatory variables, such as Traffic Analysis Zone (TAZ) characteristics, urban environments, transport facilities, travel features, and individual/household factors (Ortúzar and Willumsen 2011) to estimate the trip generation, trip distribution, mode choice, and route choice
Principal Components Analysis (PCA) component 1 presents high values for eigenvectors related to original variables such as the number of households with low income per TAZ, population, population of younger individuals, and number of households without one car
PCA component 2 consists of original variables that represent the high-income population
Summary
Travel demand forecasting models usually consider explanatory variables, such as Traffic Analysis Zone (TAZ) characteristics, urban environments, transport facilities, travel features, and individual/household factors (Ortúzar and Willumsen 2011) to estimate the trip generation, trip distribution, mode choice, and route choice. These are the four major model components of a travel demand forecasting process known as the sequential Four-Step Model (Ortúzar and Willumsen 2011). The focus of this paper is the trip generation step. The aggregated trip production model estimates the number of trips originating in a TAZ, whereas the trip attraction model estimates the number of trips to a particular TAZ
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