使用 BIRCH 算法的基于数字孪生的交通灯管理系统

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
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引用次数: 0

摘要

城市交通网络对城市的经济和环境福祉至关重要,它们面临着人类驾驶车辆(HVs)与互联和自动驾驶车辆(CAVs)的整合挑战。大多数传统交通管理系统都无法有效管理动态复杂的混合交通流,主要原因是计算量大,以及直接根据大量连续的训练数据来控制交通信号灯模型的限制。在大多数情况下,为了保证高压车辆的安全,CAV 的操作灵活性受到严重影响,或者在不考虑高压车辆效率的情况下优先考虑 CAV,从而导致性能降低,尤其是在 CAV 渗透率较低的情况下。另一方面,现有的自适应交通灯方法通常是片面的,无法适应交通系统的实时行为。有些系统采用不灵活的时间控制计划,无法对交通流量的变化做出反应,或者采用基于有限的静态交通条件的自适应控制策略。本文介绍了一种新颖的交通灯控制方法,该方法利用 BIRCH(使用层次的平衡迭代还原和聚类)聚类算法与数字双胞胎相结合,实现了更自适应、更高效的系统。BIRCH 算法能有效处理大型数据集,因为它能以增量方式将数据点动态聚类为一小部分代表。所建议的方法不仅能更好地模拟和预测交通模式,还能对信号交叉口的交通信号进行实时自适应控制。它还能根据实时和历史交通数据动态调整绿灯和红灯的持续时间,从而改善交通流量,减少拥堵,并最大限度地减少车辆空转时间。该方法在不同的交通强度(包括低、中、高)下进行评估,同时将效率、油耗和停车次数与传统和现有的自适应交通管理系统进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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