A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting

Sheng Hong; Tianyu Yue; Yang You; Zhengnan Lv; Xu Tang; Jing Hu; Hongwei Yin

cs.AIAug 21, 2026

A Resilience Recovery Method for Complex Traffic Network Security Based on Trend Forecasting

Authors: Sheng Hong, Tianyu Yue, Yang You, Zhengnan Lv, Xu Tang, Jing Hu, Hongwei Yin

Organizations: School of Cyber Science and Technology, Beihang University, Beijing 100191, China · NSFOCUS Technologies Group Co., Ltd., Beijing 100089, China · China Electronics Technology Taiji Group Corporation Limited, Beijing 100083, China

Abstract

Due to the rapid development of information technology, a huge and complex traffic network has been established across various sectors, including aviation, aerospace, vehicles, ships, electric power, and industry. However, because of the complexity and diversity of its structure, the complex traffic network is vulnerable to being attacked and faces serious security challenges. Therefore, this paper innovatively proposes a traffic network resilience recovery method based on resilience trend forecasting. In this paper, the risk value is introduced into the analysis of the network fault propagation process, and the Susceptible, Infectious, Recovered, Dead-Risk (SIRD-R) fault propagation model is established. The resilience model of traffic network, which encompasses real-time resilience and overall resilience, is constructed through the integration of network resilience bearing capacity and resilience recovery capacity. Ten, the resilience of complex traffic networks is forecasted by using long short-term memory networks, and the resilience recovery strategy of complex traffic networks based on forecasting is proposed. Finally, the effectiveness and scalability of the proposed method are demonstrated through experimental analysis conducted on a diverse range of complex traffic networks, affirming its applicability in real-world scenarios

A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks
Jul 28, 2026Du Yin, Xiachong Lin, Yue Tan +4Intelligent Transportation Systems Continual Test-Time Adaptation
University of New South Wales · Sydney, NSW, Australia · Griffith University +7
Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Jul 30, 2026Qingzhao ZhangAdversarial Attacks Adversarial Robustness
The University of Arizoan
Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers
Mar 17, 2026Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler +4Spatiotemporal Transformers Traffic Forecasting
Department of Mechanical and Aerospace Engineering and the Center for Automotive Research, The Ohio State University · Ohio Department of Transportation · Honda Research Institute USA, Inc.

Jul 28, 2026J/K move · Enter open · S save

A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks

Du Yin, Xiachong Lin, Yue Tan, Jinliang Deng+3

University of New South Wales · Sydney, NSW, Australia · Griffith University+7

Intelligent Transportation Systems Continual Test-Time Adaptation Spatiotemporal Forecasting Traffic Forecasting

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Traffic forecasting is important for efficient traffic management and route planning in smart cities. Existing traffic forecasting studies typically assume fixed sensor graphs, overlooking the continuous evolution of real-world traffic networks, e.g., ongoing road network construction and evolving human mobility patterns. These dynamic changes can substantially degrade conventional forecasting models, motivating test-time adaptation (TTA) to efficiently adapt pretrained models during deployment. However, applying TTA to evolving traffic sensor networks remains challenging in two aspects. First, topology expansion introduces new sensors and connections, continuously reshaping the sensor graph. Second, tem- poral shifts vary in time scale and stability, requiring differentiated adaptation to long-term and short-term shifts. In this study, we address these challenges by proposing A2TTA, an Anchored-and-Agile Test-Time Adaptation framework for evolving traffic sensor networks, which transforms topology-induced forecasting errors into an expandable output calibration problem and separates tem- poral adaptation into persistent global correction and agile context-specific specialization. By jointly addressing topology evolution and multi-scale temporal shifts, A2TTA enables efficient and robust adaptation to continuously evolving traffic environments. Extensive experiments on ten real-world traffic networks demonstrate that A2TTA consistently improves forecasting performance across different backbones, datasets, and prediction horizons. Our code is available in https://github.com/lixus7/A2TTA.

Abstract

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