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Updated: Jan 10, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

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Published on: February 25, 2013

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基于高斯双相邻图的空间相关和时间依赖的交通预测在班加罗尔市

Sathish Kumar Ravichandran1,2, Chin-Shiuh Shieh3, Mong-Fong Horng3

  • 1Research Institute of IoT and Cybersecurity, Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan. dr.sathish_kumar_r@svyasa.edu.in.

Scientific reports
|November 21, 2025
PubMed
概括

准确的交通预测对于智能交通系统至关重要. 拟议的基于高斯双相邻图的空间相关和时间依赖 (GDAG-SCTT) 方法显著提高了交通预测的准确性,并减少了错误.

关键词:
在四分之一之间,四分之一之间.当地-全球不变量.最小至最大的正常化.空间相关图形卷积神经网络的神经网络.时间长短时间的时间依赖性记忆.

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