Citation

Kranzinger S, Steinmaßl M. Transp. Res. Rec. 2022; 2676(4): 210-227.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211057881

PMID

unavailable

Abstract

Aggregation of sparse probe vehicle data (PVD) is a crucial issue in travel time reliability (TTR) analysis. This study, therefore, examines the effect of temporal and spatial aggregation of sparse PVD on the results of a linear regression analysis where two different measures of TTR are analyzed as the dependent variable. Our results show that by aggregating the data to longer time intervals and coarser spatial units the linear model can explain a higher proportion of the variance in TTR. Furthermore, we find that the effects of road design characteristics in particular depend on the variable used to represent TTR. We conclude that the temporal and spatial aggregation of sparse PVD affects the results of linear regression explaining TTR.

Language: en

Keywords

analysis; and information systems; data analysis; data and data science; geographic information science; GPS data; information systems and technology; probe vehicle data; statistical methods; traffic information; travel time; urban transportation data