通过实时车辆数据集成增强交通流量预测

Rishabh Jain, Sunita Dhingra, Kamaldeep Joshi, A. Rana, Nitin Goyal
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引用次数: 1

摘要

本研究探讨复杂的交通控制系统如何影响交通流量。这些尖端的解决方案使用实时交通数据来提高道路网络的智能化。这些技术通过改善交通信号定时和自动将车辆转向不那么拥挤的路线,使交通流量更顺畅、更高效。值得注意的是,这些创新显著降低了空气污染、温室气体排放和燃料消耗,同时也最大限度地减少了与交通拥堵相关的财务和时间支出。我们独特的实时车辆数据集成(RTVDI)算法被用来描绘智能交通控制系统的潜力。这些技术有可能通过使用实时数据和复杂流程来彻底改变交通管理程序。它们具有改善通勤安全、提高道路效率和改善交通流量的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhance traffic flow prediction with Real-Time Vehicle Data Integration
This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.
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CiteScore
0.40
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