基于路网相似度的未检测路段交通量估计迁移学习方法

IF 8.4 1区 工程技术 Q1 ENGINEERING, CIVIL
Shan Cao;Chunyue Song;Jie Zhang;Xiangrui Zhang
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引用次数: 0

摘要

在智能交通系统(ITS)中,评估交通状态,尤其是交通量是至关重要的。由于检测器的缺失或故障,一些路段无法被检测到,导致完全没有体量数据,从而削弱了ITS的交通监控能力。现有的估计方法要么不适用这种情况,要么由于缺乏可用数据而导致结果不佳,从而影响ITS的交通监控能力。为了解决这一问题,本文提出了一种新的基于路网相似性的迁移学习方法(RNS-TL)用于实时交通估计。首先,提出了小规模道路网络相似性评估模块(SSEM),该模块旨在为未检测到的路段识别出最相似的路段及其小规模道路网络,作为迁移学习的源域;然后,在SSEM的基础上,提出了一种迁移学习框架,在源域上训练的交通估计模型对目标未检测路段进行微调。最后,两个实际交通案例的结果表明,该方法的估计误差MAE和RMSE分别为7.813和6.383,10.689和8.892,优于所有比较方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Road Network Similarity-Based Transfer Learning Method for Traffic Volume Estimation in Undetected Road Segments
Estimating traffic state, particularly traffic volume, is crucial in Intelligent Transportation Systems (ITS). Due to the absence or malfunction of detectors, some road segments are undetected, leading to a complete absence of volume data and thereby weakening the traffic monitoring capability of ITS. The existing estimation methods are either inapplicable to this scenario or yield poor results due to a lack of available data, which will compromise the traffic monitoring capability of ITS. To handle it, this work proposes a novel Road Network Similarity-based Transfer Learning method (RNS-TL) for real-time traffic estimation. Firstly, the Small-scale Road Network Similarity Evaluation Module (SSEM) is initially proposed which aims to identify the most similar road segments and their small-scale road networks for the undetected segments, serving as the source domain for transfer learning. Then, based on SSEM, a transfer learning framework is proposed where a traffic estimation model trained on the source domain is fine-tuned for the target undetected road segment. Finally, the results from two real-world traffic cases show that the estimation errors, MAE and RMSE, for the proposed method are 7.813 and 6.383, and 10.689 and 8.892, respectively, outperforming all comparison methods.
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来源期刊
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems 工程技术-工程:电子与电气
CiteScore
14.80
自引率
12.90%
发文量
1872
审稿时长
7.5 months
期刊介绍: The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
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