The usefulness of artificial intelligence for safety assessment of different transport modes

Tselentis, Dimitrios I.; Papadimitriou, Eleonora; Gelder, Pieter van

doi:10.1016/j.aap.2023.107034

Cited by 23 publications

(6 citation statements)

References 53 publications

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“…In [4], Tselentis et al proposed the use of artificial intelligence methods to improve the traffic safety of road, railway, sea, and air facilities. For this purpose, they analyze statistical and econometric methods, algorithmic methods, classification and grouping, artificial neural networks, and optimization.…”

Section: State Of Knowledgementioning

confidence: 99%

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Lisowski

2024

Electronics

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The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea.

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Section: State Of Knowledgementioning

confidence: 99%

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Lisowski

2024

Electronics

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“…Convolutional or recurrent neural networks can only establish local dependencies of input information, and to establish long-distance dependencies, the most direct method is to use a fully connected neural network (Tselentis et al 2023). In actual transportation tasks, the length of log texts and visual images generated by the train control system can vary, so for different input lengths, their connection weights should also be different.…”

Section: Multimodal Learning Architecturementioning

confidence: 99%

Multimodal Anomaly Detection for High-Speed Train Control System Based on Attention Mechanism

Kang,

Pang,

Cheng

et al. 2024

Preprint

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“…The integration of AI technologies such as Deep Learning (DL), a class of Machine Learning (ML) algorithms that use multiple layers to progressively extract higher-level features from the raw input, and computer vision enables autonomous vehicles to perceive their surroundings accurately and react swiftly to changing road conditions [15]. Recent studies have shown that AI-powered autonomous vehicles can significantly reduce traffic congestion by optimizing routes and minimizing delays, leading to improved travel efficiency and reduced CO 2 emissions [16].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

A Review of the Advances in Artificial Intelligence in Transportation System Development

Mirindi

2024

JCCEE

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In modern times, the rapid expansion of urban populations has intensified the urgency to optimize transportation systems, which has become an alarming issue in the face of urbanization and traffic congestion. This paper reviews the latest applications of Artificial Intelligence (AI) in the transport sector. It explores various AI methodologies, including Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Ant Colony Optimizer (ACO), Bee Colony Optimization (BCO), disruptive urban mobility, Fuzzy Logic Models (FLM), automated incident detection systems, and drones, which improve dynamic traffic management and route optimization. The study reveals that integrating these AI techniques with real-time data analytics improves traffic flow, automated incident management, and overall transportation efficiency. The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management. These advancements in technologies, such as electric Vertical Take-off and Landing (eVTOL) aircraft, Hyperloop Transportation Technologies (HTT), Mobility-as-a-Service (MaaS) and autonomous delivery robots, contribute to smarter urban mobility solutions. However, it is important to focus on refining AI models for better performance, addressing challenges such as computational complexity and privacy concerns, and continuing to innovate in AI to improve the economic efficiency and reliability of transportation systems. Furthermore, to promote sustainability development in this sector, ethical considerations such as the protection of user information and the integration of the concepts of informed consent and human autonomy with community engagement programs should also be considered.

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The usefulness of artificial intelligence for safety assessment of different transport modes

Cited by 23 publications

References 53 publications

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Computational Intelligence Supporting the Safe Control of Autonomous Multi-Objects

Multimodal Anomaly Detection for High-Speed Train Control System Based on Attention Mechanism

A Review of the Advances in Artificial Intelligence in Transportation System Development

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