Open AccessEstimation of link‐cost function for cyclists based on stochastic optimisation and GPS traces
Author(s) -
Schweizer Joerg,
Rupi Federico,
Poliziani Cristian
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.2019.0683
Subject(s) - global positioning system , calibration , function (biology) , routing (electronic design automation) , computer science , matching (statistics) , transport engineering , mathematical optimization , statistics , engineering , mathematics , computer network , telecommunications , evolutionary biology , biology
The objective of this work is the calibration of a generalised cost function for the bicycle network links to be used in conjunction with assignment methods for uncongested networks, as cyclists are generally much less delayed by traffic congestions with respect to auto‐traffic. The calibration's goal is to find a coefficient vector for a linear link‐cost function that maximises the overlap between routes obtained with a minimum cost routing of cyclists' demand and the relative cyclists' chosen routes, identified by a map‐matching procedure of recorded global positioning system (GPS) traces. The calibration focuses on minimising an objective function through different established evolution‐based optimisation algorithms, thus avoiding the generation of route choice sets. Link cost functions are calibrated for a modified Openstreet network of Bologna, Italy, using GPS data from the European cycling challenge and Bella Mossa campaign. Results show an improvement of up to 30% of overlapping routes with respect to pure distance‐based routing. It is also demonstrated that the calibrated link‐costs are transferable to a different scenario.