智能交通预测:利用自适应机器学习和大数据分析进行交通流量预测

Idriss Moumen, J. Abouchabaka, N. Rafalia
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引用次数: 0

摘要

现代社会,道路交通拥堵问题日益突出。随着城市化进程的加快、技术的进步以及道路上车辆数量的增加,几乎所有大城市都出现了交通环境差、道路效率低的问题。为了解决这一问题,研究人员开始利用各种数据资源,重点预测交通流量,这是智能交通系统(ITS)中的一个关键问题,有助于缓解交通拥堵。通过分析相关道路和车辆的数据,如速度、密度和流量,可以预测交通拥堵情况和模式。本文介绍了一种自适应交通系统,它利用有监督的机器学习和大数据分析来预测交通流量。该系统监控和提取相关的交通流量数据,对数据进行分析和处理,并将其存储起来,以提高模型的准确性和有效性。作者进行了一次模拟,以展示所提出的解决方案。研究成果对交通系统具有重大意义,为加强交通流量管理提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction
The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.
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