Spatiotemporal Nonrecurring Traffic Spillback Pattern Prediction for Freeway Merging Bottleneck Using Conditional Generative Adversarial Nets with Simulation Accelerated Training

Zirui Raymond Huang, Yi Chang Chiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Forecasting short-term, nonrecurring traffic dynamics caused by incidents is an essential capability in the Intelligent Transportation Systems. This research proposes a prediction framework in which Conditional Deep Convolutional Generative Adversarial Nets (C-DCGAN) is trained to predict the traffic spillbacks patterns associated with freeway incidents at merging bottleneck. Speed tensors, which depict the spatiotemporal incident-induced impacts for multiple neighboring routes, is a suitable object for the GAN model to understand and predict. Further, we demonstrated how to use the mesoscopic Dynamic Traffic Assignment (DTA) model DynusT to generate a large number of training data, thus speeding up the model training. The developed model achieves both statistical and spatial similarities between predicted speed tensors and actual tensors, to 83.84%. To the best of our knowledge, this line of work is one of the first attempts in the literature to train the Machine Learning model to predict speed tensor representation of multi-location incident-induced spatiotemporal impact at merging bottleneck and speeding up the training via simulation.

Original languageEnglish (US)
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141497
DOIs
StatePublished - Sep 20 2020
Event23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece
Duration: Sep 20 2020Sep 23 2020

Publication series

Name2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Conference

Conference23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
CountryGreece
CityRhodes
Period9/20/209/23/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Modeling and Simulation
  • Education

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