A bidirectional-a-star-based ant colony optimization algorithm for big-data-driven taxi route recommendation

Xia, Dawen; Shen, Bingqi; Zheng, Yongling; Zhang, Wenyong; Bai, Dewei; Hu, Yang; Li, Huaqing

doi:10.1007/s11042-023-15498-4

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Over time, the ACO algorithm converges on an optimal set of routes that balance the traffic load across the network, reducing overall travel times and improving navigation efficiency. This approach can be particularly beneficial during peak traffic hours or in response to traffic incidents, where the ability to quickly adapt route planning can significantly alleviate congestion [35].…”

Section: Ant Colony Optimizermentioning

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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Section: Ant Colony Optimizermentioning

confidence: 99%

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

Mirindi

2024

JCCEE

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Searching Routing using A-Star (A*) Search Algorithm

Felix,

Yodianto,

Warnars

et al. 2024

2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)

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Research and Validation of Self-Driving Path Planning Algorithm Based on Optimized A^*-Artificial Potential Field Method

Shan,

Shao,

Zhang

et al. 2024

IEEE Sensors J.

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A bidirectional-a-star-based ant colony optimization algorithm for big-data-driven taxi route recommendation

Cited by 3 publications

References 35 publications

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

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

Searching Routing using A-Star (A*) Search Algorithm

Research and Validation of Self-Driving Path Planning Algorithm Based on Optimized A^*-Artificial Potential Field Method

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A bidirectional-a-star-based ant colony optimization algorithm for big-data-driven taxi route recommendation

Cited by 3 publications

References 35 publications

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

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

Searching Routing using A-Star (A*) Search Algorithm

Research and Validation of Self-Driving Path Planning Algorithm Based on Optimized A*-Artificial Potential Field Method

Contact Info

Product

Resources

About

Research and Validation of Self-Driving Path Planning Algorithm Based on Optimized A^*-Artificial Potential Field Method