Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead

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 re...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems 2020-02, Vol.21 (2), p.466-495
Hauptverfasser: Del Ser, Javier, Osaba, Eneko, Sanchez-Medina, Javier J., Fister, Iztok
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container_end_page 495
container_issue 2
container_start_page 466
container_title IEEE transactions on intelligent transportation systems
container_volume 21
creator Del Ser, Javier
Osaba, Eneko
Sanchez-Medina, Javier J.
Fister, Iztok
Fister, Iztok
description 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.
doi_str_mv 10.1109/TITS.2019.2897377
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subjects Adaptation models
Adaptive systems
Algorithms
Artificial intelligence
Atmospheric modeling
autonomous and cooperative driving
Bioinspired computational intelligence
Biological system modeling
Computational intelligence
Computational modeling
Computer simulation
driver characterization
Intelligence
Intelligent transportation systems
Optimization
Predictive models
route planning
smart mobility
State-of-the-art reviews
Task complexity
traffic forecasting
title Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead
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