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Origin-Destination matrix estimation is a keystone for traffic representationand analysis. Traditionally estimated thanks to traffic counts, surveys andsocio-economic models, recent technological advances permit to rethink theestimation problem. Road user identification technologies, such as connectedGPS, Bluetooth or Wifi detectors bring additional information, that is, for afraction of the users, the origin, the destination and to some extend theitinerary taken. In the present work, this additional information is used forthe estimation of a more comprehensive traffic representation tool: thelink-origin-destination matrix. Such three-dimensional matrices extend theconcept of traditional origin-destination matrices by also giving informationon the traffic assignment. Their estimation is solved as an inverse problemwhose objective function represents a trade-off between important propertiesthe traffic has to satisfy. This article presents the theory and how toimplement such method on real dataset. With the case study of Brisbane Citywhere over 600 hundreds Bluetooth detectors have been installed it alsoillustrates the opportunities such link-origin-destination matrices create fortraffic analysis.

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