Revolutionizing Travel Efficiency with Artificial Intelligence and Automation

The AI-powered route optimization system is designed to provide effective travel routes by analysing real-time traffic conditions. By utilizing a comprehensive dataset that includes source, destination, waypoints, distance, and live traffic data, the system identifies the most efficient route for any journey. It employs ant colony optimization (ACO), a nature-inspired algorithm, to find optimal routes by mimicking the foraging behaviour of ants. This ensures that the selected route is continuously updated based on current traffic conditions, minimizing delays and enhancing travel efficiency. In addition to route optimization, the system also estimates fuel consumption to promote economical and environmentally friendly travel. Users input details about their vehicle's fuel level and mileage, allowing the system to calculate the approximate distance they can cover and the fuel required for the trip. This feature helps travellers plan their journeys with a focus on both time and fuel efficiency, ultimately reducing fuel consumption and environmental impact. The system's real-time adaptability ensures smooth navigation, especially in congested areas, thereby enhancing the overall driving experience. By combining advanced route planning with smart fuel management, the system provides a comprehensive and eco-conscious solution for modern travel needs.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-03-28
DOI
https://doi.org/10.52783/jisem.v10i26s.4267
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

Revolutionizing Travel Efficiency with Artificial Intelligence and Automation

M.Bharathi Ph.D
Journal of Information Systems Engineering & Management
Traffic Prediction and Management Techniques
article

Revolutionizing Travel Efficiency with Artificial Intelligence and Automation

M.Bharathi Ph.D
article en

Abstract

The AI-powered route optimization system is designed to provide effective travel routes by analysing real-time traffic conditions. By utilizing a comprehensive dataset that includes source, destination, waypoints, distance, and live traffic data, the system identifies the most efficient route for any journey. It employs ant colony optimization (ACO), a nature-inspired algorithm, to find optimal routes by mimicking the foraging behaviour of ants. This ensures that the selected route is continuously updated based on current traffic conditions, minimizing delays and enhancing travel efficiency. In addition to route optimization, the system also estimates fuel consumption to promote economical and environmentally friendly travel. Users input details about their vehicle's fuel level and mileage, allowing the system to calculate the approximate distance they can cover and the fuel required for the trip. This feature helps travellers plan their journeys with a focus on both time and fuel efficiency, ultimately reducing fuel consumption and environmental impact. The system's real-time adaptability ensures smooth navigation, especially in congested areas, thereby enhancing the overall driving experience. By combining advanced route planning with smart fuel management, the system provides a comprehensive and eco-conscious solution for modern travel needs.

Journal of Information Systems Engineering & ManagementVol. 10(26s)
Openalex Percentile: Top 94%
Traffic Prediction and Management Techniques
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Revolutionizing Travel Efficiency with Artificial Intelligence and Automation — M.Bharathi Ph.D · Journal of Information Systems Engineering & Management (2025) | TGRS Research Map | TGRS