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MPC-543 INFERENCING HOURLY TRAFFIC VOLUME USING DATA-DRIVEN MACHINE LEARNING AND GRAPH THEORY
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.

Feb 18, 2021 03:00 PM in Central Time (US and Canada)

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