The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2023-12-15 DOI:10.4108/ew.4613

Srishti Dikshit, Areeba Atiq, Mohammad Shahid, Vinay Dwivedi, Aarushi Thusu

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引用次数: 0

Abstract

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

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利用人工智能优化车辆行驶路线，缓解城市交通拥堵状况

城市的快速城市化带来了无与伦比的车辆交通流量激增，导致严重拥堵、污染加剧和生活质量下降。本研究探讨了人工智能（AI）通过优化车辆路线和缓解交通拥堵来改变城市交通的能力。其目的是创建由人工智能驱动的解决方案，以提高交通效率、缩短出行时间并减轻环境影响。本文深入研究了现有的人工智能算法、车辆路由和交通管理技术。研究整合了实时交通数据、路网特征和个人出行模式，以制定智能路由策略。所提出的人工智能系统通过机器学习和优化算法来适应动态交通状况，精确定位最佳路线并重新分配交通流，以尽量减少拥堵热点。为了评估人工智能驱动方法的有效性，我们在具有代表性的城市地区进行了广泛的模拟和案例研究。采用的性能指标包括旅行时间减少、燃料消耗和排放减少，以量化拟议系统对交通拥堵和环境可持续性的影响。此外，研究还评估了在更大范围内实施基于人工智能的交通管理解决方案的可扩展性、可行性和经济可行性。这项研究的成果为人工智能在重塑城市交通方面的潜在优势提供了宝贵的见解。通过优化车辆路线和减少交通拥堵，拟议的人工智能驱动系统有可能提高整体交通效率、减少能源消耗，并为营造更健康的城市环境做出贡献。这些发现对寻求创新解决方案的决策者、城市规划者和交通管理部门具有重大意义，他们可以在促进可持续发展的同时，应对当代城市化带来的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.