AI-Based Transportation Planning and Operation

• Autore: Sohn Keemin
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (124 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: artificial neural network; autoencoder; automated vehicle; black ice; bottleneck and gridlock identification; CNN; context-awareness; CycleGAN; deep learning; deep neural networks; driving cycle; dynamic pricing; gridlock prediction; link embedding; link emission factors; long short-term memory; micro-level vehicle emission estimation; MOVES; prevention; preventive automated driving system; reachability analysis; reinforcement learning; ridesharing; spatio-temporal data; supply improvement; taxi; traffic accidents; traffic flow centrality; traffic speed prediction; traffic volume; urban road network; vehicle counting; vehicle GPS data
• Persona (resp. second.): SohnKeemin
• Sommario/riassunto: 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.
• Titolo autorizzato: AI-Based Transportation Planning and Operation
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557486603321
• Lo trovi qui: Univ. Federico II