A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

Li, Guanyao; Zhong, Shuhan; Deng, Xi; Xiang, Letian; Chan, S.-H. Gary; Li, Ruiyuan; Liu, Yang; Zhang, Ming; Hung, Chih‐Chieh; Peng, Wen-Chih

doi:10.1109/tkde.2022.3233086

Cited by 17 publications

(1 citation statement)

References 37 publications

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“…With the advent of deep learning (DL) and its proven prowess in handling complex datasets, there is a burgeoning interest in its application to this domain [6][7][8]. If successful, it promises a more nuanced, accurate, and actionable understanding of when and where accidents might occur.…”

Section: Introduction and Related Workmentioning

confidence: 99%

LLM Multimodal Traffic Accident Forecasting

de Zarzà,

de Curtò,

Roig

et al. 2023

Sensors

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With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)—a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)—in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making.

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Section: Introduction and Related Workmentioning

confidence: 99%