I am Huang Yin, a Master’s candidate at Southwest Jiaotong University pursuing a degree in Safety Science and Engineering. Based at the National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, my research focuses on AI-powered spatiotemporal transportation data mining, including:

• Multimodal transportation data fusion and mining
• Mobile spatiotemporal pattern analysis
• Transport & Traffic Safety
• Data-driven transportation facility safety management
• Deep Learning/Deep Reinforcement Learning applications in transport services (prediction, classification, anomaly detection, optimization)

I am seeking research assistant positions (2025-2026) followed by Ph.D. opportunities (Spring or Fall 2026 intake). I welcome discussions about aligning my work with your research group’s vision for intelligent transportation systems.

Biography

From 2022 to present, I have been pursuing my Master’s degree at the School of Transportation and Logistics, Southwest Jiaotong University, specializing in data-driven transportation facility safety management. My thesis project, “Fault Warning of Hydraulic System of Railway Tamping Vehicle Based on Deep Learning” [Thesis to be released], originated from an industry-academia collaboration with Shenhua Railway Equipment Company. This work investigates sensor deployment strategies and multi-source sensing data applications for health management and fault diagnosis in railway maintenance machinery (including ballast cleaning machines, tamping vehicles, and stabilizers).

From 2022 to 2024, I established expertise in AI technologies including deep learning and deep reinforcement learning, achieving proficiency in Python libraries (numpy, pandas, torch, torch_geometric). I successfully replicated time-series models such as Transformer, Informer, Autoformer, DLinear, and PatchTST, and I conducted research on parking demand prediction under multimodal transportation data fusion scenarios.Our study “Leverage Multi-source Traffic Demand Data Fusion with Transformer Model for Urban Parking Prediction”, was accepted by the 28th International Conference Of Hong Kong Society For Transportation Studies (HKSTS 2024) for presentation and publication, and accepted by the Conference in Emerging Technologies in Transportation Systems (TRC-30) for presentation, Preprint.

I obtained my Bachelor’s degree in Transportation Engineering from Southwest Jiaotong University in 2022. From 2021-2022, I participated in the National Engineering Laboratory’s research project “Construction of Chengdu’s Comprehensive Transportation Data Governance System”. This initiative aimed to enhance data application scenarios through systematic data processing architecture development, supporting Chengdu’s smart transportation and sustainable urban development. Through this experience, I mastered various transportation data structures (subway, bus, taxi, ride-hailing, bike-sharing), data processing workflows, and transportation-geospatial visualization techniques.

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