Virtual Learning

MPC Research: Inferencing Hourly Traffic Volume Using Data-Driven Machine Learning and Graph Theory (MPC-543)

• Feb 18, 2021
  Virtual Learning (desktop/laptop and mobile devices)

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Description

Effectively capturing traffic volumes on a network scale is beneficial to Transportation Systems Management & Operations (TSM&O). Yet it is impractical to install sensors to cover a large road network. To address this issue, spatial prediction techniques are widely performed to estimate traffic volumes at sites without sensors. In retrospect, most relevant studies resort to machine learning methods and treat each prediction location independently during the training process, ignoring the potential spatial dependency among them. We present an innovative spatial prediction method of hourly traffic volume on a network scale using a combination of machine learning techniques and graph theory to account for the spatial dependency.

Speaker(s)

Xiaoyue Cathy Liu, Ph.D., P.E. Dr. Liu is an Associate Professor of Civil and Environmental Engineering at the University of Utah. Her research interests include traffic operations, big data applications in transportation, GIS-based network modeling, and multimodal transportation.

Target Audience

Personnel involved in the traffic volume or AADT estimation for large-scale road networks; researchers involved in machine learning applications in transportation will also receive benefit from this presentation.