加州I-210高速公路车载延迟容忍网络中的交通拥堵感知与按需分发
Traffic Congestion Awareness and On-Demand Distribution in Vehicular Delay-Tolerant Networks in California I-210 Freeway
- University of Nottingham（诺丁汉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对车载网络中泛洪广播导致资源浪费的问题，本文提出基于拥堵复发与持续时间预测的按需分发机制，利用加州I-210真实数据验证，显著降低网络开销并提升投递性能。
AI中文摘要：
在边缘计算环境下的车载网络中，车对车延迟容忍网络（V-DTN）可通过存储-携带-转发机制将拥堵警告传播给其他车辆，帮助其主动选择合适路线。然而，现有的大多数车载信息传播方法依赖泛洪或受限泛洪策略，一旦检测到拥堵就在整个网络中广播警报，这导致过多的冗余副本并消耗节点缓存空间。为解决此问题，本文提出一种拥堵事件感知的按需消息分发机制。通过考虑拥堵的复发性和持续时间，该机制抑制对短暂、自行消散的拥堵事件的广播。基于加州I-210走廊的真实PeMS数据，我们构建了I-210高速公路交通拥堵用例。实验表明，预测拥堵复发高度依赖车道的历史自由流速度数据。在不增加额外特征维度的前提下，对不同日期类型下车道自由流速度进行建模和比较的准确性优于机器学习模型的选择，且此类模式难以在模拟器中复现。同时，拥堵持续时间预测可有效作为V-DTN中动态阈值按需分发的基础，这表明基于机器学习的拥堵感知结合动态阈值按需分发可显著降低网络资源消耗和网络层拥塞，提高投递可靠性，并降低投递延迟。
英文摘要：
In vehicular networks under edge computing environments, vehicle-to-vehicle delay-tolerant networking (V-DTN) can disseminate congestion warnings to other vehicles via a store-carry-forward mechanism, helping them proactively choose suitable routes. However, most existing in-vehicle information dissemination methods rely on flooding or limited flooding strategies, broadcasting alerts across the entire network whenever congestion is detected. This leads to excessive redundant copies and consumes node cache space. To address this issue, this paper proposes a congestion-event- aware on-demand message dissemination mechanism. By considering the recurrence and duration of congestion, the mechanism suppresses broadcasts of short-lived, self-dissipating congestion events. Based on real-world PeMS data from California's I-210 corridor, we construct an I-210 Freeway Traffic Congestion Use Case. Experiments show that predicting congestion recurrence heavily relies on historical free-flow speed data of lanes. Without incorporating additional feature dimensions, the accuracy of modeling and comparing lane free-flow speeds across different day types outweighs the choice of machine learning models, and such patterns are difficult to reproduce in simulators. Meanwhile, congestion duration prediction serves effectively as the basis for dynamic-threshold on-demand dissemination in V-DTN, which demonstrate that machine learning-based congestion awareness combined with dynamic-threshold on-demand dissemination significantly reduces network resource consumption and network-layer congestion, enhances delivery reliability, and lowers delivery latency.
