AI-Based Transportation Planning and Operation

Edited by
March 2021
124 pages
  • ISBN978-3-0365-0364-6 (Hardback)
  • ISBN978-3-0365-0365-3 (PDF)

This book is a reprint of the Special Issue AI-Based Transportation Planning and Operation that was published in

Computer Science & Mathematics
Physical Sciences
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
autoencoder; deep learning; traffic volume; vehicle counting; CycleGAN; bottleneck and gridlock identification; gridlock prediction; urban road network; long short-term memory; link embedding; traffic speed prediction; traffic flow centrality; reachability analysis; spatio-temporal data; artificial neural network; deep learning; context-awareness; dynamic pricing; reinforcement learning; ridesharing; supply improvement; taxi; preventive automated driving system; automated vehicle; traffic accidents; deep neural networks; vehicle GPS data; driving cycle; micro-level vehicle emission estimation; link emission factors; MOVES; automated vehicle; black ice; CNN; traffic accidents; prevention

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