Combining traffic counts and Bluetooth data for link-origin-destination matrix estimation in large urban networks: The Brisbane case study

TL;DR

This paper introduces a method to estimate detailed link-origin-destination matrices in large urban networks by integrating Bluetooth data with traditional traffic counts, demonstrated through a case study in Brisbane.

Contribution

It presents a novel inverse problem approach to estimate comprehensive traffic matrices using Bluetooth and traffic count data, enhancing traditional traffic analysis methods.

Findings

Successful estimation of link-origin-destination matrices in Brisbane

Demonstrated the integration of Bluetooth data improves traffic representation

Provided a practical implementation framework for large-scale urban networks

Abstract

Origin-Destination matrix estimation is a keystone for traffic representation and analysis. Traditionally estimated thanks to traffic counts, surveys and socio-economic models, recent technological advances permit to rethink the estimation problem. Road user identification technologies, such as connected GPS, Bluetooth or Wifi detectors bring additional information, that is, for a fraction of the users, the origin, the destination and to some extend the itinerary taken. In the present work, this additional information is used for the estimation of a more comprehensive traffic representation tool: the link-origin-destination matrix. Such three-dimensional matrices extend the concept of traditional origin-destination matrices by also giving information on the traffic assignment. Their estimation is solved as an inverse problem whose objective function represents a trade-off between important properties the traffic has to satisfy. This article presents the theory and how to implement such method on real dataset. With the case study of Brisbane City where over 600 hundreds Bluetooth detectors have been installed it also illustrates the opportunities such link-origin-destination matrices create for traffic analysis.