Faculty of Engineering Sciences & Technology, Hamdard University, Karachi 74600, Pakistan.
College of Computing and Information Science, Karachi Institute of Economics and Technology, Karachi 75190, Pakistan.
Sensors (Basel). 2023 Jan 9;23(2):768. doi: 10.3390/s23020768.
An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things-intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.
智能交通系统（ITS）旨在通过整合创新的传感、控制和通信技术来提高交通效率。工业物联网（IIoT）和第四次工业革命最近融合在一起，设计了工业物联网-智能交通系统（IIoT-ITS）。IIoT 传感技术在获取原始数据方面发挥着重要作用。该应用程序根据车辆数量、位置和时间等多个参数，持续执行有效管理交通流量的复杂任务。交通密度估计（TDE）是另一个重要的衍生参数，可跟踪交通量的动态状态。基于无线连接的车辆数量不断增加，为更准确、实时地预测交通密度提供了新的潜力，这是以前使用的方法所无法实现的。我们仅使用一些简单的指标来研究评估交通密度的问题，例如周围车辆的数量以及向路边单元和反之传播信标。本研究论文探讨了 TDE 技术，并提出了一种基于马尔可夫模型的 ITS 新 TDE 技术。最后，提出了一种基于 OMNET++的方法，该方法实现了对交通模型的重大修改，并结合了马尔可夫模型的数学建模。它旨在用于研究真实世界的交通轨迹、识别模型参数和开发模拟交通。
