Open AccessMethodology for O‐D matrix estimation using the revealed paths of floating car data on large‐scale networks
Author(s) -
Mitra Anna,
Attanasi Alessandro,
Meschini Lorenzo,
Gentile Guido
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0684
Subject(s) - computer science , reliability (semiconductor) , matrix (chemical analysis) , set (abstract data type) , a priori and a posteriori , trip distribution , data set , scale (ratio) , data mining , scaling , mathematical optimization , algorithm , operations research , artificial intelligence , engineering , mathematics , power (physics) , philosophy , materials science , physics , geometry , epistemology , quantum mechanics , composite material , programming language
The increasing availability of historical floating car data (FCD) represents a relevant chance to improve the accuracy of model‐based traffic forecasting systems. A more precise estimation of origin–destination (O‐D) matrices is a critical issue for the successful application of traffic assignment models. The authors developed a methodology for obtaining demand matrices without any prior information, but just starting from a data set of vehicle trajectories, and without using any assignment model, as traditional correction approaches do. Several steps are considered. A data‐driven approach is applied to determine both observed departure shares from origins to destinations and static assignment matrices. Then the O‐D matrix estimation problem is formulated as a scaling problem of the observed FCD demand and carried out using as inputs: a set of traffic counts, the FCD revealed assignment matrix and the observed departure shares as an a‐priori matrix. Four different optimisation solutions are proposed. The methodology was successfully tested on the network of Turin. The results highlight the concrete opportunity to perform a data‐driven methodology that, independently from the reliability of the reference demand, minimises manual and specialised effort to build and calibrate the transportation demand models.