Enhancing Origin-Destination Matrix Estimation Using Measurements from Unmanned Aerial Vehicles

EasyChair Preprint 15010

Abstract

Efficient estimation of the origin-destination (OD) matrix, defined as the travel demand between network origin and destination nodes, is pivotal for effective traffic monitoring, planning, and management. Over the years, the OD matrix estimation problem has received considerable attention, leading to the development and testing of various approaches utilising traffic counts from fixed-location sensors. In this work, we introduce a novel methodology for static OD matrix estimation leveraging traffic flow dynamics and link count observations gathered from a swarm of Unmanned Aerial Vehicles (UAVs) deployed over the network under investigation. We employ a path-based cell transmission model and formulate the problem within an optimisation framework, providing a solution approach for scenarios where data is obtained from (i) fixed-location sensors and (ii) UAVs, for both free-flow and congested scenarios. Notably, the key distinction between the two data types lies in the mobility of UAVs, enabling observations across different links at varying time intervals, while fixed-location sensors measure specific links at stationary positions over time. By comparing estimation results using both data types, we show that OD matrix estimation utilising UAV-based measurements yields significantly superior performance, even with fewer measurements per time-step from the UAV swarm.

Keyphrases: Optimisation, Traffic flow dynamics, cell transmission model, origin-destination demand, path demand

@booklet{EasyChair:15010,
  author    = {Yiolanda Englezou and Stelios Timotheou and Christos Panayiotou},
  title     = {Enhancing Origin-Destination Matrix Estimation Using Measurements from Unmanned Aerial Vehicles},
  howpublished = {EasyChair Preprint 15010},
  year      = {EasyChair, 2024}}