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Title: Bioinspired computational intelligence and transportation systems: a long road ahead
Authors: Del Ser, Javier
Osaba, Eneko
Sanchez-Medina, Javier J. 
Fister, Iztok
UNESCO Clasification: 332703 Sistemas de transito urbano
Keywords: Bioinspired computational intelligence
Route planning
Traffic forecasting
Autonomous and cooperative driving
Driver characterization, et al
Issue Date: 2020
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.
ISSN: 1524-9050
DOI: 10.1109/TITS.2019.2897377
Source: IEEE Transactions on Intelligent Transportation Systems [ISSN 1524-9050], v. 21 (2), p. 466-495, (Febrero 2020)
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