Sohn, Keemin <br/><br/>
AI-Based Transportation Planning and Operation 

 - Basel, Switzerland  MDPI - Multidisciplinary Digital Publishing Institute  2021 
 - 1 electronic resource (124 p.) 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>books978-3-0365-0365-3 9783036503646 9783036503653 
<br/><br/><label>Standard No.: </label>10.3390/books978-3-0365-0365-3  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>History of engineering & technology
<br/><br/><label>Subjects--Index Terms: </label>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 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 black ice CNN prevention