Cloud-Based Demand-Responsive Transportation System Using Forecasting Model

Khair, Younes; Dennai, Abdeslem; Elmir, Youssef

doi:10.1007/s13369-023-08102-2

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Intelligent Transportation Systems1

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2024

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Cited by 4 publications

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“…With these technologies, one can expect that the city's traffic models, including traffic assignment models, would be improved to become increasingly accurate. Thanks to the progress in AI techniques and data science, the data may also be sufficient alone to carry out traffic assessments and traffic projections [164][165][166][167][168].…”

Section: Intelligent Transportation Systemsmentioning

confidence: 99%

Review of Traffic Assignment and Future Challenges

Elimadi,

Abbas-Turki,

Koukam

et al. 2024

Applied Sciences

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The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress in the field. Accordingly, it shows that the evolution of intelligent transportation systems and the emergence of connected and autonomous vehicles present new challenges to classical approaches for solving the traffic assignment problem. It addresses two major perspectives: digital twins coupled with artificial intelligence to help decision-makers, and rule-based policy to offer users fair and efficient itineraries while respecting infrastructure capacity.

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